Tests for a future article. AMD EPYC 9124 16-Core testing with a Supermicro H13SSW (1.1 BIOS) and astdrmfb on AlmaLinux 9.2 via the Phoronix Test Suite.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2310228-NE-EXTRATEST37
extra tests2
Tests for a future article. AMD EPYC 9124 16-Core testing with a Supermicro H13SSW (1.1 BIOS) and astdrmfb on AlmaLinux 9.2 via the Phoronix Test Suite.
a:
Processor: 2 x AMD EPYC 9254 24-Core @ 2.90GHz (48 Cores / 96 Threads), Motherboard: Supermicro H13DSH (1.5 BIOS), Memory: 24 x 32 GB DDR5-4800MT/s Samsung M321R4GA3BB6-CQKET, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
b:
Processor: 2 x AMD EPYC 9254 24-Core @ 2.90GHz (48 Cores / 96 Threads), Motherboard: Supermicro H13DSH (1.5 BIOS), Memory: 24 x 32 GB DDR5-4800MT/s Samsung M321R4GA3BB6-CQKET, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
c:
Processor: 2 x AMD EPYC 9254 24-Core @ 2.90GHz (48 Cores / 96 Threads), Motherboard: Supermicro H13DSH (1.5 BIOS), Memory: 24 x 32 GB DDR5-4800MT/s Samsung M321R4GA3BB6-CQKET, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
d:
Processor: AMD EPYC 9124 16-Core @ 3.00GHz (16 Cores / 32 Threads), Motherboard: Supermicro H13SSW (1.1 BIOS), Memory: 12 x 64 GB DDR5-4800MT/s HMCG94MEBRA123N, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
e:
Processor: AMD EPYC 9124 16-Core @ 3.00GHz (16 Cores / 32 Threads), Motherboard: Supermicro H13SSW (1.1 BIOS), Memory: 12 x 64 GB DDR5-4800MT/s HMCG94MEBRA123N, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
f:
Processor: AMD EPYC 9124 16-Core @ 3.00GHz (16 Cores / 32 Threads), Motherboard: Supermicro H13SSW (1.1 BIOS), Memory: 12 x 64 GB DDR5-4800MT/s HMCG94MEBRA123N, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
g:
Processor: AMD EPYC 9124 16-Core @ 3.00GHz (16 Cores / 32 Threads), Motherboard: Supermicro H13SSW (1.1 BIOS), Memory: 12 x 64 GB DDR5-4800MT/s HMCG94MEBRA123N, Disk: 2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07, Graphics: astdrmfb
OS: AlmaLinux 9.2, Kernel: 5.14.0-284.25.1.el9_2.x86_64 (x86_64), Compiler: GCC 11.3.1 20221121, File-System: ext4, Screen Resolution: 1024x768
nekRS 23.0
Input: Kershaw
flops/rank > Higher Is Better
a . 11106900000 |=============================================================
b . 11240300000 |==============================================================
c . 10826700000 |============================================================
d . 10318900000 |=========================================================
e . 10264000000 |=========================================================
f . 9976450000 |=======================================================
g . 10500600000 |==========================================================
nekRS 23.0
Input: TurboPipe Periodic
flops/rank > Higher Is Better
a . 6767710000 |======================================================
b . 6757360000 |=====================================================
c . 6754170000 |=====================================================
d . 7934570000 |===============================================================
e . 7931010000 |===============================================================
f . 7955790000 |===============================================================
g . 7964910000 |===============================================================
OpenVINO 2023.1
Model: Face Detection FP16 - Device: CPU
FPS > Higher Is Better
a . 30.41 |====================================================================
b . 30.44 |====================================================================
c . 30.43 |====================================================================
d . 10.47 |=======================
e . 10.47 |=======================
f . 10.48 |=======================
g . 10.48 |=======================
OpenVINO 2023.1
Model: Person Detection FP16 - Device: CPU
FPS > Higher Is Better
a . 282.55 |===================================================================
b . 284.22 |===================================================================
c . 282.67 |===================================================================
d . 107.02 |=========================
e . 107.27 |=========================
f . 107.39 |=========================
g . 107.04 |=========================
OpenVINO 2023.1
Model: Person Detection FP32 - Device: CPU
FPS > Higher Is Better
a . 283.97 |===================================================================
b . 284.99 |===================================================================
c . 284.31 |===================================================================
d . 106.90 |=========================
e . 107.24 |=========================
f . 106.76 |=========================
g . 107.24 |=========================
OpenVINO 2023.1
Model: Vehicle Detection FP16 - Device: CPU
FPS > Higher Is Better
a . 2033.17 |==================================================================
b . 2028.01 |==================================================================
c . 2029.79 |==================================================================
d . 797.64 |==========================
e . 793.75 |==========================
f . 791.74 |==========================
g . 793.90 |==========================
OpenVINO 2023.1
Model: Face Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 56.01 |====================================================================
b . 56.06 |====================================================================
c . 56.02 |====================================================================
d . 20.03 |========================
e . 20.00 |========================
f . 20.01 |========================
g . 20.05 |========================
OpenVINO 2023.1
Model: Face Detection Retail FP16 - Device: CPU
FPS > Higher Is Better
a . 5882.91 |==================================================================
b . 5836.27 |=================================================================
c . 5840.53 |==================================================================
d . 2564.78 |=============================
e . 2562.54 |=============================
f . 2539.97 |============================
g . 2557.66 |=============================
OpenVINO 2023.1
Model: Road Segmentation ADAS FP16 - Device: CPU
FPS > Higher Is Better
a . 748.44 |==================================================================
b . 750.49 |==================================================================
c . 757.38 |===================================================================
d . 344.67 |==============================
e . 342.81 |==============================
f . 343.49 |==============================
g . 341.36 |==============================
OpenVINO 2023.1
Model: Vehicle Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 2873.24 |==================================================================
b . 2880.58 |==================================================================
c . 2881.14 |==================================================================
d . 1175.67 |===========================
e . 1174.60 |===========================
f . 1180.85 |===========================
g . 1175.58 |===========================
OpenVINO 2023.1
Model: Weld Porosity Detection FP16 - Device: CPU
FPS > Higher Is Better
a . 2945.26 |=================================================================
b . 2986.46 |==================================================================
c . 2987.33 |==================================================================
d . 1039.61 |=======================
e . 1039.82 |=======================
f . 1038.47 |=======================
g . 1039.37 |=======================
OpenVINO 2023.1
Model: Face Detection Retail FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 9837.58 |==================================================================
b . 9849.07 |==================================================================
c . 9845.27 |==================================================================
d . 3540.88 |========================
e . 3544.18 |========================
f . 3548.78 |========================
g . 3533.64 |========================
OpenVINO 2023.1
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 842.91 |==================================================================
b . 854.51 |===================================================================
c . 849.30 |===================================================================
d . 370.57 |=============================
e . 373.64 |=============================
f . 369.26 |=============================
g . 372.26 |=============================
OpenVINO 2023.1
Model: Machine Translation EN To DE FP16 - Device: CPU
FPS > Higher Is Better
a . 317.22 |===================================================================
b . 317.28 |===================================================================
c . 317.33 |===================================================================
d . 124.12 |==========================
e . 123.61 |==========================
f . 124.30 |==========================
g . 123.41 |==========================
OpenVINO 2023.1
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 5776.94 |==================================================================
b . 5780.44 |==================================================================
c . 5802.65 |==================================================================
d . 2013.77 |=======================
e . 2007.53 |=======================
f . 2004.76 |=======================
g . 2006.09 |=======================
OpenVINO 2023.1
Model: Person Vehicle Bike Detection FP16 - Device: CPU
FPS > Higher Is Better
a . 2454.09 |==================================================================
b . 2450.26 |==================================================================
c . 2455.51 |==================================================================
d . 1036.99 |============================
e . 1028.64 |============================
f . 1041.87 |============================
g . 1031.60 |============================
OpenVINO 2023.1
Model: Handwritten English Recognition FP16 - Device: CPU
FPS > Higher Is Better
a . 1560.03 |==================================================================
b . 1546.02 |=================================================================
c . 1551.63 |==================================================================
d . 532.59 |=======================
e . 530.99 |======================
f . 533.74 |=======================
g . 538.01 |=======================
OpenVINO 2023.1
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
FPS > Higher Is Better
a . 86884.64 |=================================================================
b . 87359.23 |=================================================================
c . 86789.80 |=================================================================
d . 32002.62 |========================
e . 32032.06 |========================
f . 31951.64 |========================
g . 32008.03 |========================
OpenVINO 2023.1
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 1244.69 |==================================================================
b . 1239.67 |==================================================================
c . 1237.29 |==================================================================
d . 395.66 |=====================
e . 432.32 |=======================
f . 431.94 |=======================
g . 432.20 |=======================
OpenVINO 2023.1
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
FPS > Higher Is Better
a . 120606.38 |===============================================================
b . 120728.22 |===============================================================
c . 123484.28 |================================================================
d . 44958.07 |=======================
e . 44933.27 |=======================
f . 44968.43 |=======================
g . 45097.99 |=======================
Embree 4.1
Binary: Pathtracer - Model: Crown
Frames Per Second > Higher Is Better
a . 54.90 |===================================================================
b . 55.39 |====================================================================
c . 55.40 |====================================================================
d . 21.48 |==========================
e . 21.44 |==========================
f . 21.59 |===========================
g . 21.58 |==========================
Embree 4.1
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
a . 56.09 |===================================================================
b . 56.46 |====================================================================
c . 56.81 |====================================================================
d . 22.59 |===========================
e . 22.57 |===========================
f . 22.66 |===========================
g . 22.77 |===========================
Embree 4.1
Binary: Pathtracer - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 60.14 |====================================================================
b . 59.91 |====================================================================
c . 59.79 |====================================================================
d . 24.69 |============================
e . 24.73 |============================
f . 24.70 |============================
g . 24.82 |============================
Embree 4.1
Binary: Pathtracer - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 53.57 |====================================================================
b . 53.81 |====================================================================
c . 53.69 |====================================================================
d . 22.26 |============================
e . 22.16 |============================
f . 22.15 |============================
g . 22.19 |============================
Embree 4.1
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 67.34 |====================================================================
b . 67.20 |====================================================================
c . 67.50 |====================================================================
d . 28.36 |=============================
e . 28.31 |=============================
f . 28.32 |=============================
g . 28.48 |=============================
Embree 4.1
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 56.49 |===================================================================
b . 56.69 |====================================================================
c . 56.93 |====================================================================
d . 23.87 |=============================
e . 23.94 |=============================
f . 23.94 |=============================
g . 23.88 |=============================
SVT-AV1 1.7
Encoder Mode: Preset 4 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a . 5.203 |====================================================================
b . 5.149 |===================================================================
c . 5.049 |==================================================================
d . 4.107 |======================================================
e . 4.114 |======================================================
f . 4.138 |======================================================
g . 4.143 |======================================================
SVT-AV1 1.7
Encoder Mode: Preset 8 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a . 90.81 |====================================================================
b . 91.32 |====================================================================
c . 90.42 |===================================================================
d . 66.99 |==================================================
e . 67.72 |==================================================
f . 67.39 |==================================================
g . 67.81 |==================================================
SVT-AV1 1.7
Encoder Mode: Preset 12 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a . 163.46 |==================================================================
b . 166.38 |===================================================================
c . 163.06 |==================================================================
d . 163.19 |==================================================================
e . 162.61 |=================================================================
f . 161.85 |=================================================================
g . 160.32 |=================================================================
SVT-AV1 1.7
Encoder Mode: Preset 13 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a . 163.01 |==================================================================
b . 166.69 |===================================================================
c . 161.50 |=================================================================
d . 161.85 |=================================================================
e . 162.05 |=================================================================
f . 160.80 |=================================================================
g . 161.32 |=================================================================
SVT-AV1 1.7
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a . 12.48 |===================================================================
b . 12.59 |====================================================================
c . 12.62 |====================================================================
d . 10.91 |===========================================================
e . 10.98 |===========================================================
f . 10.74 |==========================================================
g . 11.02 |===========================================================
SVT-AV1 1.7
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a . 141.22 |==================================================================
b . 138.34 |=================================================================
c . 143.55 |===================================================================
d . 118.95 |========================================================
e . 119.31 |========================================================
f . 118.49 |=======================================================
g . 118.48 |=======================================================
SVT-AV1 1.7
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a . 422.99 |======================================================
b . 427.69 |======================================================
c . 431.90 |=======================================================
d . 526.22 |===================================================================
e . 525.17 |===================================================================
f . 521.52 |==================================================================
g . 528.53 |===================================================================
SVT-AV1 1.7
Encoder Mode: Preset 13 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a . 510.36 |=========================================================
b . 542.61 |============================================================
c . 516.91 |=========================================================
d . 604.99 |===================================================================
e . 597.01 |==================================================================
f . 585.37 |=================================================================
g . 586.75 |=================================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon
Frames Per Second > Higher Is Better
d . 24.85 |====================================================================
e . 24.83 |====================================================================
f . 24.89 |====================================================================
g . 24.96 |====================================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
d . 22.35 |====================================================================
e . 22.29 |====================================================================
f . 22.27 |====================================================================
g . 22.26 |====================================================================
Embree 4.3
Binary: Pathtracer - Model: Crown
Frames Per Second > Higher Is Better
d . 21.89 |====================================================================
e . 21.99 |====================================================================
f . 21.77 |===================================================================
g . 21.83 |====================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
d . 27.74 |====================================================================
e . 27.83 |====================================================================
f . 27.83 |====================================================================
g . 27.91 |====================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
d . 23.35 |===================================================================
e . 23.53 |===================================================================
f . 23.50 |===================================================================
g . 23.71 |====================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
d . 22.39 |====================================================================
e . 22.34 |====================================================================
f . 22.44 |====================================================================
g . 22.42 |====================================================================
Intel Open Image Denoise 2.0
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 1.83 |=====================================================================
b . 1.83 |=====================================================================
c . 1.83 |=====================================================================
d . 0.72 |===========================
e . 0.72 |===========================
f . 0.72 |===========================
g . 0.72 |===========================
Intel Open Image Denoise 2.0
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 1.84 |=====================================================================
b . 1.84 |=====================================================================
c . 1.82 |====================================================================
d . 0.72 |===========================
e . 0.72 |===========================
f . 0.72 |===========================
g . 0.72 |===========================
Intel Open Image Denoise 2.0
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 0.86 |====================================================================
b . 0.86 |====================================================================
c . 0.87 |=====================================================================
d . 0.34 |===========================
e . 0.34 |===========================
f . 0.34 |===========================
g . 0.34 |===========================
Intel Open Image Denoise 2.1
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
d . 0.72 |=====================================================================
e . 0.72 |=====================================================================
f . 0.72 |=====================================================================
g . 0.72 |=====================================================================
Intel Open Image Denoise 2.1
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
d . 0.72 |=====================================================================
e . 0.72 |=====================================================================
f . 0.72 |=====================================================================
g . 0.72 |=====================================================================
Intel Open Image Denoise 2.1
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
d . 0.34 |=====================================================================
e . 0.34 |=====================================================================
f . 0.34 |=====================================================================
g . 0.34 |=====================================================================
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU Scalar
Items / Sec > Higher Is Better
d . 191 |======================================================================
e . 190 |======================================================================
f . 191 |======================================================================
g . 191 |======================================================================
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU ISPC
Items / Sec > Higher Is Better
d . 487 |======================================================================
e . 487 |======================================================================
f . 488 |======================================================================
g . 489 |======================================================================
OSPRay 2.12
Benchmark: particle_volume/ao/real_time
Items Per Second > Higher Is Better
a . 15.98600 |=================================================================
b . 15.97850 |=================================================================
c . 15.98720 |=================================================================
d . 5.57469 |=======================
e . 5.54107 |=======================
f . 5.57320 |=======================
g . 5.57553 |=======================
OSPRay 2.12
Benchmark: particle_volume/scivis/real_time
Items Per Second > Higher Is Better
a . 15.95280 |=================================================================
b . 15.98880 |=================================================================
c . 15.97780 |=================================================================
d . 5.57001 |=======================
e . 5.56353 |=======================
f . 5.55581 |=======================
g . 5.56539 |=======================
OSPRay 2.12
Benchmark: particle_volume/pathtracer/real_time
Items Per Second > Higher Is Better
a . 215.10 |===================================================================
b . 214.07 |===================================================================
c . 214.14 |===================================================================
d . 151.91 |===============================================
e . 151.51 |===============================================
f . 151.78 |===============================================
g . 151.68 |===============================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/ao/real_time
Items Per Second > Higher Is Better
a . 14.23690 |=================================================================
b . 14.17830 |=================================================================
c . 14.13990 |=================================================================
d . 5.60747 |==========================
e . 5.62040 |==========================
f . 5.61454 |==========================
g . 5.62278 |==========================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/scivis/real_time
Items Per Second > Higher Is Better
a . 13.87390 |=================================================================
b . 13.76660 |================================================================
c . 13.83170 |=================================================================
d . 5.45329 |==========================
e . 5.46153 |==========================
f . 5.45227 |==========================
g . 5.47725 |==========================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time
Items Per Second > Higher Is Better
a . 16.34680 |================================================================
b . 16.43650 |=================================================================
c . 16.53500 |=================================================================
d . 6.58745 |==========================
e . 6.58270 |==========================
f . 6.59563 |==========================
g . 6.60085 |==========================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 39.50 |====================================================================
b . 39.47 |====================================================================
c . 39.45 |====================================================================
d . 13.07 |=======================
e . 12.94 |======================
f . 13.09 |=======================
g . 13.07 |=======================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 1417.07 |==================================================================
b . 1403.07 |=================================================================
c . 1418.90 |==================================================================
d . 508.09 |========================
e . 511.41 |========================
f . 508.21 |========================
g . 509.14 |========================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 672.46 |===================================================================
b . 672.37 |===================================================================
c . 671.26 |===================================================================
d . 257.27 |==========================
e . 257.89 |==========================
f . 257.50 |==========================
g . 257.28 |==========================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 201.39 |===================================================================
b . 201.25 |===================================================================
c . 201.54 |===================================================================
d . 71.14 |========================
e . 71.27 |========================
f . 71.04 |========================
g . 70.93 |========================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 485.67 |===================================================================
b . 488.13 |===================================================================
c . 489.11 |===================================================================
d . 162.85 |======================
e . 163.14 |======================
f . 162.93 |======================
g . 162.99 |======================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 5137.01 |==================================================================
b . 5138.83 |==================================================================
c . 5153.66 |==================================================================
d . 1599.21 |====================
e . 1599.15 |====================
f . 1600.53 |====================
g . 1602.52 |=====================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 215.64 |===================================================================
b . 215.93 |===================================================================
c . 215.65 |===================================================================
d . 71.92 |======================
e . 71.91 |======================
f . 71.94 |======================
g . 71.90 |======================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 49.33 |====================================================================
b . 49.17 |====================================================================
c . 47.15 |=================================================================
d . 16.16 |======================
e . 16.14 |======================
f . 16.13 |======================
g . 16.07 |======================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 489.12 |===================================================================
b . 489.45 |===================================================================
c . 487.05 |===================================================================
d . 163.56 |======================
e . 162.93 |======================
f . 162.90 |======================
g . 163.23 |======================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 218.15 |===================================================================
b . 219.53 |===================================================================
c . 218.52 |===================================================================
d . 72.46 |======================
e . 72.66 |======================
f . 72.57 |======================
g . 72.69 |======================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 322.25 |===================================================================
b . 321.18 |===================================================================
c . 321.51 |===================================================================
d . 108.91 |=======================
e . 109.09 |=======================
f . 109.09 |=======================
g . 109.22 |=======================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 68.60 |====================================================================
b . 68.66 |====================================================================
c . 68.63 |====================================================================
d . 24.48 |========================
e . 24.47 |========================
f . 24.52 |========================
g . 24.46 |========================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 718.92 |===================================================================
b . 717.97 |===================================================================
c . 716.14 |===================================================================
d . 240.55 |======================
e . 240.23 |======================
f . 240.16 |======================
g . 239.52 |======================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 158.92 |=================================================================
b . 159.06 |=================================================================
c . 164.61 |===================================================================
d . 55.61 |=======================
e . 55.46 |=======================
f . 55.54 |=======================
g . 55.43 |=======================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
a . 39.44 |====================================================================
b . 39.45 |====================================================================
c . 39.42 |====================================================================
d . 13.13 |=======================
e . 13.12 |=======================
f . 13.09 |=======================
g . 13.06 |=======================
Apache Cassandra 4.1.3
Test: Writes
Op/s > Higher Is Better
a . 248095 |=============================================================
b . 256661 |================================================================
c . 270480 |===================================================================
d . 197866 |=================================================
e . 195798 |=================================================
f . 196287 |=================================================
g . 197092 |=================================================
Apache Hadoop 3.3.6
Operation: Open - Threads: 50 - Files: 100000
Ops per sec > Higher Is Better
a . 460829 |=====================================================
b . 469484 |======================================================
c . 401606 |===============================================
d . 578035 |===================================================================
e . 552486 |================================================================
f . 578035 |===================================================================
g . 546448 |===============================================================
Apache Hadoop 3.3.6
Operation: Open - Threads: 100 - Files: 100000
Ops per sec > Higher Is Better
a . 420168 |=====================================================
b . 404858 |===================================================
c . 403226 |===================================================
d . 529101 |===================================================================
e . 294985 |=====================================
f . 523560 |==================================================================
g . 460829 |==========================================================
Apache Hadoop 3.3.6
Operation: Open - Threads: 50 - Files: 1000000
Ops per sec > Higher Is Better
a . 1126126 |=============================================================
b . 1020408 |=======================================================
c . 683995 |=====================================
d . 278319 |===============
e . 251004 |==============
f . 1221001 |==================================================================
g . 654022 |===================================
Apache Hadoop 3.3.6
Operation: Create - Threads: 50 - Files: 100000
Ops per sec > Higher Is Better
a . 43649 |=================================================
b . 41288 |==============================================
c . 43937 |=================================================
d . 58617 |==================================================================
e . 58617 |==================================================================
f . 58343 |=================================================================
g . 60680 |====================================================================
Apache Hadoop 3.3.6
Operation: Delete - Threads: 50 - Files: 100000
Ops per sec > Higher Is Better
a . 91075 |===========================================================
b . 73801 |================================================
c . 90580 |==========================================================
d . 101010 |=================================================================
e . 100604 |=================================================================
f . 96993 |===============================================================
g . 103950 |===================================================================
Apache Hadoop 3.3.6
Operation: Open - Threads: 100 - Files: 1000000
Ops per sec > Higher Is Better
a . 215332 |===========
b . 173822 |=========
c . 185874 |=========
d . 1248439 |===============================================================
e . 1204819 |=============================================================
f . 1303781 |==================================================================
g . 1107420 |========================================================
Apache Hadoop 3.3.6
Operation: Rename - Threads: 50 - Files: 100000
Ops per sec > Higher Is Better
a . 70522 |==========================================================
b . 73046 |============================================================
c . 77101 |================================================================
d . 82372 |====================================================================
e . 82237 |====================================================================
f . 81633 |===================================================================
g . 82237 |====================================================================
Apache Hadoop 3.3.6
Operation: Create - Threads: 100 - Files: 100000
Ops per sec > Higher Is Better
a . 40733 |===============================================
b . 37425 |===========================================
c . 35075 |========================================
d . 57971 |==================================================================
e . 58824 |===================================================================
f . 59382 |====================================================================
g . 58928 |===================================================================
Apache Hadoop 3.3.6
Operation: Create - Threads: 50 - Files: 1000000
Ops per sec > Higher Is Better
a . 53665 |==================================================
b . 52119 |=================================================
c . 52260 |=================================================
d . 72134 |===================================================================
e . 70897 |==================================================================
f . 69920 |=================================================================
g . 72706 |====================================================================
Apache Hadoop 3.3.6
Operation: Delete - Threads: 100 - Files: 100000
Ops per sec > Higher Is Better
a . 87566 |========================================================
b . 90827 |==========================================================
c . 73475 |===============================================
d . 105708 |===================================================================
e . 98039 |==============================================================
f . 99404 |===============================================================
g . 102564 |=================================================================
Apache Hadoop 3.3.6
Operation: Delete - Threads: 50 - Files: 1000000
Ops per sec > Higher Is Better
a . 98932 |==========================================================
b . 97314 |==========================================================
c . 90147 |=====================================================
d . 111012 |==================================================================
e . 113327 |===================================================================
f . 111198 |==================================================================
g . 110828 |==================================================================
Apache Hadoop 3.3.6
Operation: Rename - Threads: 100 - Files: 100000
Ops per sec > Higher Is Better
a . 75529 |=============================================================
b . 69348 |========================================================
c . 67159 |======================================================
d . 82102 |===================================================================
e . 83822 |====================================================================
f . 79491 |================================================================
g . 80386 |=================================================================
Apache Hadoop 3.3.6
Operation: Rename - Threads: 50 - Files: 1000000
Ops per sec > Higher Is Better
a . 73239 |===========================================================
b . 71679 |=========================================================
c . 74638 |============================================================
d . 83921 |===================================================================
e . 84041 |===================================================================
f . 82501 |==================================================================
g . 84810 |====================================================================
Apache Hadoop 3.3.6
Operation: Create - Threads: 100 - Files: 1000000
Ops per sec > Higher Is Better
a . 46145 |============================================
b . 44437 |==========================================
c . 44001 |==========================================
d . 71296 |====================================================================
e . 70057 |===================================================================
f . 70537 |===================================================================
g . 70922 |====================================================================
Apache Hadoop 3.3.6
Operation: Delete - Threads: 100 - Files: 1000000
Ops per sec > Higher Is Better
a . 90114 |=====================================================
b . 86715 |===================================================
c . 97031 |=========================================================
d . 112613 |==================================================================
e . 113225 |===================================================================
f . 110803 |=================================================================
g . 113895 |===================================================================
Apache Hadoop 3.3.6
Operation: Rename - Threads: 100 - Files: 1000000
Ops per sec > Higher Is Better
a . 73078 |==========================================================
b . 72129 |=========================================================
c . 66827 |=====================================================
d . 81208 |================================================================
e . 84360 |===================================================================
f . 85815 |====================================================================
g . 85763 |====================================================================
Apache Hadoop 3.3.6
Operation: File Status - Threads: 50 - Files: 100000
Ops per sec > Higher Is Better
a . 529101 |=========================================
b . 862069 |===================================================================
c . 657895 |===================================================
d . 632911 |=================================================
e . 389105 |==============================
f . 709220 |=======================================================
g . 561798 |============================================
Apache Hadoop 3.3.6
Operation: File Status - Threads: 100 - Files: 100000
Ops per sec > Higher Is Better
a . 515464 |===============================================
b . 458716 |==========================================
c . 729927 |===================================================================
d . 591716 |======================================================
e . 613497 |========================================================
f . 478469 |============================================
g . 487805 |=============================================
Apache Hadoop 3.3.6
Operation: File Status - Threads: 50 - Files: 1000000
Ops per sec > Higher Is Better
a . 2173913 |==================================================================
b . 1941748 |===========================================================
c . 284252 |=========
d . 1818182 |=======================================================
e . 320924 |==========
f . 1795332 |=======================================================
g . 2036660 |==============================================================
Apache Hadoop 3.3.6
Operation: File Status - Threads: 100 - Files: 1000000
Ops per sec > Higher Is Better
a . 1886792 |=============================================================
b . 161970 |=====
c . 1893939 |=============================================================
d . 600601 |===================
e . 235627 |========
f . 1964637 |===============================================================
g . 2049180 |==================================================================
TiDB Community Server 7.3
Test: oltp_read_write - Threads: 1
Queries Per Second > Higher Is Better
a . 2540 |======================================================
b . 2510 |======================================================
c . 2485 |=====================================================
e . 3209 |=====================================================================
f . 3218 |=====================================================================
g . 3195 |=====================================================================
TiDB Community Server 7.3
Test: oltp_read_write - Threads: 16
Queries Per Second > Higher Is Better
a . 38331 |====================================================================
b . 36950 |==================================================================
c . 37368 |==================================================================
d . 36480 |=================================================================
e . 36784 |=================================================================
f . 36125 |================================================================
g . 36088 |================================================================
TiDB Community Server 7.3
Test: oltp_read_write - Threads: 32
Queries Per Second > Higher Is Better
a . 58974 |=================================================================
b . 61520 |====================================================================
c . 59630 |==================================================================
d . 46977 |====================================================
e . 46737 |====================================================
f . 47141 |====================================================
g . 46993 |====================================================
TiDB Community Server 7.3
Test: oltp_read_write - Threads: 64
Queries Per Second > Higher Is Better
a . 79090 |===================================================================
b . 80183 |====================================================================
c . 78469 |===================================================================
d . 55334 |===============================================
e . 53893 |==============================================
f . 54956 |===============================================
g . 55301 |===============================================
TiDB Community Server 7.3
Test: oltp_point_select - Threads: 1
Queries Per Second > Higher Is Better
a . 4331 |==================================================
b . 4405 |===================================================
c . 4471 |====================================================
d . 5898 |====================================================================
e . 5976 |=====================================================================
f . 5954 |=====================================================================
TiDB Community Server 7.3
Test: oltp_read_write - Threads: 128
Queries Per Second > Higher Is Better
a . 85757 |=================================================================
b . 89099 |====================================================================
d . 59727 |==============================================
e . 60145 |==============================================
f . 60310 |==============================================
g . 59944 |==============================================
TiDB Community Server 7.3
Test: oltp_update_index - Threads: 1
Queries Per Second > Higher Is Better
a . 1212 |========================================================
c . 1189 |=======================================================
d . 1479 |====================================================================
e . 1490 |=====================================================================
f . 1483 |=====================================================================
g . 1481 |=====================================================================
TiDB Community Server 7.3
Test: oltp_point_select - Threads: 16
Queries Per Second > Higher Is Better
b . 67515 |=================================================================
c . 65406 |===============================================================
e . 70250 |====================================================================
f . 70105 |====================================================================
g . 69923 |====================================================================
TiDB Community Server 7.3
Test: oltp_point_select - Threads: 32
Queries Per Second > Higher Is Better
a . 104627 |==================================================================
b . 106180 |===================================================================
d . 98149 |==============================================================
e . 96907 |=============================================================
f . 97368 |=============================================================
g . 96840 |=============================================================
TiDB Community Server 7.3
Test: oltp_point_select - Threads: 64
Queries Per Second > Higher Is Better
a . 127567 |=================================================================
b . 130802 |===================================================================
d . 115675 |===========================================================
e . 118657 |=============================================================
f . 119092 |=============================================================
g . 118549 |=============================================================
TiDB Community Server 7.3
Test: oltp_update_index - Threads: 16
Queries Per Second > Higher Is Better
a . 12558 |===================================================================
c . 12681 |====================================================================
d . 12622 |====================================================================
e . 12567 |===================================================================
f . 12692 |====================================================================
g . 12627 |====================================================================
TiDB Community Server 7.3
Test: oltp_update_index - Threads: 32
Queries Per Second > Higher Is Better
a . 18361 |====================================================================
b . 17817 |==================================================================
c . 17565 |=================================================================
d . 17612 |=================================================================
e . 17117 |===============================================================
g . 17135 |===============================================================
TiDB Community Server 7.3
Test: oltp_update_index - Threads: 64
Queries Per Second > Higher Is Better
b . 24371 |====================================================================
c . 23324 |=================================================================
d . 21108 |===========================================================
e . 21271 |===========================================================
f . 21067 |===========================================================
TiDB Community Server 7.3
Test: oltp_point_select - Threads: 128
Queries Per Second > Higher Is Better
a . 159242 |===================================================================
b . 159728 |===================================================================
c . 149962 |===============================================================
d . 129492 |======================================================
e . 129904 |======================================================
f . 130389 |=======================================================
TiDB Community Server 7.3
Test: oltp_update_index - Threads: 128
Queries Per Second > Higher Is Better
a . 27087 |===================================================================
b . 27464 |====================================================================
c . 26546 |==================================================================
e . 24611 |=============================================================
f . 24830 |=============================================================
g . 24574 |=============================================================
TiDB Community Server 7.3
Test: oltp_update_non_index - Threads: 1
Queries Per Second > Higher Is Better
a . 1328 |======================================================
b . 1312 |=====================================================
c . 1381 |========================================================
d . 1693 |====================================================================
e . 1708 |=====================================================================
f . 1697 |=====================================================================
g . 1705 |=====================================================================
TiDB Community Server 7.3
Test: oltp_update_non_index - Threads: 16
Queries Per Second > Higher Is Better
a . 18095 |==================================================================
b . 18068 |==================================================================
d . 18563 |===================================================================
e . 18557 |===================================================================
g . 18735 |====================================================================
TiDB Community Server 7.3
Test: oltp_update_non_index - Threads: 32
Queries Per Second > Higher Is Better
a . 28735 |====================================================================
b . 28914 |====================================================================
d . 26273 |==============================================================
e . 26285 |==============================================================
f . 26695 |===============================================================
TiDB Community Server 7.3
Test: oltp_update_non_index - Threads: 64
Queries Per Second > Higher Is Better
a . 41281 |====================================================================
b . 39759 |=================================================================
c . 39106 |================================================================
d . 34224 |========================================================
e . 33881 |========================================================
f . 34470 |=========================================================
g . 34107 |========================================================
TiDB Community Server 7.3
Test: oltp_update_non_index - Threads: 128
Queries Per Second > Higher Is Better
a . 51105 |==================================================================
c . 52865 |====================================================================
e . 42138 |======================================================
f . 41424 |=====================================================
g . 41695 |======================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 39499000 |=================================================================
b . 39486000 |=================================================================
c . 39453000 |=================================================================
d . 35228000 |==========================================================
e . 35315000 |==========================================================
f . 35271000 |==========================================================
g . 35236000 |==========================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 59401000 |=================================================================
b . 59296000 |=================================================================
c . 57519000 |===============================================================
d . 52665000 |==========================================================
e . 52827000 |==========================================================
f . 52879000 |==========================================================
g . 52854000 |==========================================================
Liquid-DSP 1.6
Threads: 2 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 77181000 |=================================================================
b . 77019000 |=================================================================
c . 76924000 |=================================================================
d . 67054000 |========================================================
e . 68846000 |==========================================================
f . 68861000 |==========================================================
g . 68678000 |==========================================================
Liquid-DSP 1.6
Threads: 2 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 117490000 |===============================================================
b . 114010000 |==============================================================
c . 118550000 |================================================================
d . 105650000 |=========================================================
e . 105480000 |=========================================================
f . 105740000 |=========================================================
g . 104800000 |=========================================================
Liquid-DSP 1.6
Threads: 4 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 153850000 |================================================================
b . 153690000 |================================================================
c . 153670000 |================================================================
d . 138600000 |==========================================================
e . 138620000 |==========================================================
f . 138580000 |==========================================================
g . 138460000 |==========================================================
Liquid-DSP 1.6
Threads: 4 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 196220000 |================================================================
b . 196590000 |================================================================
c . 194510000 |===============================================================
d . 188930000 |==============================================================
e . 191230000 |==============================================================
f . 189880000 |==============================================================
g . 190750000 |==============================================================
Liquid-DSP 1.6
Threads: 8 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 307540000 |================================================================
b . 305110000 |===============================================================
c . 306760000 |================================================================
d . 278030000 |==========================================================
e . 277780000 |==========================================================
f . 276390000 |==========================================================
g . 277410000 |==========================================================
Liquid-DSP 1.6
Threads: 8 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 369430000 |================================================================
b . 366930000 |================================================================
c . 366990000 |================================================================
d . 363310000 |===============================================================
e . 357990000 |==============================================================
f . 350450000 |=============================================================
g . 357810000 |==============================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 13909000 |================================================================
b . 14021000 |================================================================
c . 14225000 |=================================================================
d . 12683000 |==========================================================
e . 12366000 |=========================================================
f . 12681000 |==========================================================
g . 12256000 |========================================================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 594230000 |===============================================================
b . 602470000 |================================================================
c . 603650000 |================================================================
d . 545360000 |==========================================================
e . 545140000 |==========================================================
f . 545020000 |==========================================================
g . 543050000 |==========================================================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 699740000 |================================================================
b . 692760000 |===============================================================
c . 674930000 |==============================================================
d . 689150000 |===============================================================
e . 692920000 |===============================================================
f . 693340000 |===============================================================
g . 682070000 |==============================================================
Liquid-DSP 1.6
Threads: 2 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 27901000 |================================================================
b . 27736000 |================================================================
c . 28227000 |=================================================================
d . 24627000 |=========================================================
e . 25207000 |==========================================================
f . 25199000 |==========================================================
g . 22727000 |====================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 1183500000 |===============================================================
b . 1190300000 |===============================================================
c . 1184800000 |===============================================================
d . 1047100000 |=======================================================
e . 1046600000 |=======================================================
f . 1041900000 |=======================================================
g . 1047100000 |=======================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 1192100000 |============================================================
b . 1214200000 |=============================================================
c . 1254800000 |===============================================================
d . 1035000000 |====================================================
e . 1032000000 |====================================================
f . 1024600000 |===================================================
g . 1033400000 |====================================================
Liquid-DSP 1.6
Threads: 4 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 52911000 |==============================================================
b . 55588000 |=================================================================
c . 55165000 |=================================================================
d . 50258000 |===========================================================
e . 50380000 |===========================================================
f . 49977000 |==========================================================
g . 49556000 |==========================================================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 2207700000 |===============================================================
b . 2212100000 |===============================================================
c . 2206800000 |===============================================================
d . 1059500000 |==============================
e . 1057500000 |==============================
f . 1057100000 |==============================
g . 1056200000 |==============================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 1994400000 |===============================================================
b . 2001900000 |===============================================================
c . 2010300000 |===============================================================
d . 1093300000 |==================================
e . 1095400000 |==================================
f . 1094600000 |==================================
g . 1099300000 |==================================
Liquid-DSP 1.6
Threads: 8 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 109870000 |================================================================
b . 108080000 |===============================================================
c . 109140000 |================================================================
d . 99594000 |==========================================================
e . 97005000 |=========================================================
f . 99441000 |==========================================================
g . 100170000 |==========================================================
Liquid-DSP 1.6
Threads: 96 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 3005800000 |===============================================================
b . 2995400000 |===============================================================
c . 2999800000 |===============================================================
d . 1065200000 |======================
e . 1065100000 |======================
f . 1065300000 |======================
g . 1065700000 |======================
Liquid-DSP 1.6
Threads: 96 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 2559800000 |===============================================================
b . 2571100000 |===============================================================
c . 2564900000 |===============================================================
d . 1120800000 |===========================
e . 1117800000 |===========================
f . 1120500000 |===========================
g . 1118200000 |===========================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 216080000 |================================================================
b . 216150000 |================================================================
c . 214910000 |================================================================
d . 193850000 |=========================================================
e . 196040000 |==========================================================
f . 194500000 |==========================================================
g . 194670000 |==========================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 425810000 |===============================================================
b . 429620000 |================================================================
c . 424400000 |===============================================================
d . 273760000 |=========================================
e . 273480000 |=========================================
f . 273390000 |=========================================
g . 274070000 |=========================================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 622560000 |================================================================
b . 610950000 |===============================================================
c . 622630000 |================================================================
d . 282920000 |=============================
e . 281830000 |=============================
f . 283030000 |=============================
g . 281730000 |=============================
Liquid-DSP 1.6
Threads: 96 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 711640000 |===============================================================
b . 718140000 |================================================================
c . 715030000 |================================================================
d . 286250000 |==========================
e . 285880000 |=========================
f . 285920000 |=========================
g . 286530000 |==========================
Kripke 1.2.6
Throughput FoM > Higher Is Better
d . 240994500 |================================================================
e . 236243900 |===============================================================
f . 236591000 |===============================================================
g . 237175700 |===============================================================
BRL-CAD 7.36
VGR Performance Metric
VGR Performance Metric > Higher Is Better
a . 772162 |===================================================================
b . 768517 |===================================================================
c . 762529 |==================================================================
d . 298064 |==========================
e . 296125 |==========================
f . 295603 |==========================
g . 295522 |==========================
OpenVINO 2023.1
Model: Face Detection FP16 - Device: CPU
ms < Lower Is Better
a . 393.60 |===================================
b . 393.23 |===================================
c . 393.37 |===================================
d . 761.59 |===================================================================
e . 761.16 |===================================================================
f . 760.57 |===================================================================
g . 759.92 |===================================================================
OpenVINO 2023.1
Model: Person Detection FP16 - Device: CPU
ms < Lower Is Better
a . 42.44 |=======================================
b . 42.20 |======================================
c . 42.43 |=======================================
d . 74.71 |====================================================================
e . 74.50 |====================================================================
f . 74.43 |====================================================================
g . 74.71 |====================================================================
OpenVINO 2023.1
Model: Person Detection FP32 - Device: CPU
ms < Lower Is Better
a . 42.24 |======================================
b . 42.09 |======================================
c . 42.19 |======================================
d . 74.81 |====================================================================
e . 74.54 |====================================================================
f . 74.87 |====================================================================
g . 74.58 |====================================================================
OpenVINO 2023.1
Model: Vehicle Detection FP16 - Device: CPU
ms < Lower Is Better
a . 5.89 |========================================
b . 5.91 |========================================
c . 5.90 |========================================
d . 10.01 |===================================================================
e . 10.06 |====================================================================
f . 10.09 |====================================================================
g . 10.06 |====================================================================
OpenVINO 2023.1
Model: Face Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 213.94 |====================================
b . 213.62 |====================================
c . 213.79 |====================================
d . 398.52 |===================================================================
e . 398.91 |===================================================================
f . 399.24 |===================================================================
g . 398.13 |===================================================================
OpenVINO 2023.1
Model: Face Detection Retail FP16 - Device: CPU
ms < Lower Is Better
a . 2.03 |=============================================
b . 2.05 |=============================================
c . 2.05 |=============================================
d . 3.11 |====================================================================
e . 3.11 |====================================================================
f . 3.14 |=====================================================================
g . 3.12 |=====================================================================
OpenVINO 2023.1
Model: Road Segmentation ADAS FP16 - Device: CPU
ms < Lower Is Better
a . 16.02 |===============================================
b . 15.98 |==============================================
c . 15.83 |==============================================
d . 23.20 |===================================================================
e . 23.32 |====================================================================
f . 23.28 |====================================================================
g . 23.42 |====================================================================
OpenVINO 2023.1
Model: Vehicle Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 4.17 |==========================================
b . 4.16 |==========================================
c . 4.16 |==========================================
d . 6.79 |=====================================================================
e . 6.80 |=====================================================================
f . 6.76 |=====================================================================
g . 6.79 |=====================================================================
OpenVINO 2023.1
Model: Weld Porosity Detection FP16 - Device: CPU
ms < Lower Is Better
a . 16.26 |====================================================================
b . 16.02 |===================================================================
c . 16.02 |===================================================================
d . 15.36 |================================================================
e . 15.36 |================================================================
f . 15.38 |================================================================
g . 15.37 |================================================================
OpenVINO 2023.1
Model: Face Detection Retail FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 4.86 |=====================================================================
b . 4.85 |=====================================================================
c . 4.86 |=====================================================================
d . 4.51 |================================================================
e . 4.51 |================================================================
f . 4.50 |================================================================
g . 4.52 |================================================================
OpenVINO 2023.1
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 14.23 |=============================================
b . 14.03 |============================================
c . 14.12 |============================================
d . 21.57 |====================================================================
e . 21.40 |===================================================================
f . 21.65 |====================================================================
g . 21.47 |===================================================================
OpenVINO 2023.1
Model: Machine Translation EN To DE FP16 - Device: CPU
ms < Lower Is Better
a . 37.80 |========================================
b . 37.79 |========================================
c . 37.79 |========================================
d . 64.41 |====================================================================
e . 64.68 |====================================================================
f . 64.31 |====================================================================
g . 64.77 |====================================================================
OpenVINO 2023.1
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 8.28 |=====================================================================
b . 8.27 |=====================================================================
c . 8.24 |=====================================================================
d . 7.93 |==================================================================
e . 7.96 |==================================================================
f . 7.97 |==================================================================
g . 7.96 |==================================================================
OpenVINO 2023.1
Model: Person Vehicle Bike Detection FP16 - Device: CPU
ms < Lower Is Better
a . 4.88 |===========================================
b . 4.89 |===========================================
c . 4.88 |===========================================
d . 7.70 |====================================================================
e . 7.77 |=====================================================================
f . 7.67 |====================================================================
g . 7.74 |=====================================================================
OpenVINO 2023.1
Model: Handwritten English Recognition FP16 - Device: CPU
ms < Lower Is Better
a . 30.72 |===================================================================
b . 31.00 |====================================================================
c . 30.89 |====================================================================
d . 30.02 |==================================================================
e . 30.10 |==================================================================
f . 29.95 |==================================================================
g . 29.72 |=================================================================
OpenVINO 2023.1
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
ms < Lower Is Better
a . 0.54 |=====================================================================
b . 0.54 |=====================================================================
c . 0.54 |=====================================================================
d . 0.49 |===============================================================
e . 0.49 |===============================================================
f . 0.49 |===============================================================
g . 0.49 |===============================================================
OpenVINO 2023.1
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 38.50 |=================================================================
b . 38.66 |=================================================================
c . 38.75 |=================================================================
d . 40.40 |====================================================================
e . 36.98 |==============================================================
f . 37.01 |==============================================================
g . 36.98 |==============================================================
OpenVINO 2023.1
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
ms < Lower Is Better
a . 0.34 |===================================================================
b . 0.34 |===================================================================
c . 0.34 |===================================================================
d . 0.35 |=====================================================================
e . 0.35 |=====================================================================
f . 0.35 |=====================================================================
g . 0.35 |=====================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 2.13332 |==================================================================
e . 2.12570 |==================================================================
f . 2.13062 |==================================================================
g . 2.11813 |==================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 1.55824 |=================================================================
e . 1.54911 |=================================================================
f . 1.57282 |==================================================================
g . 1.55118 |=================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 1.33789 |==================================================================
e . 1.33861 |==================================================================
f . 1.34183 |==================================================================
g . 1.33564 |==================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 3.81576 |==================================================================
e . 3.84421 |==================================================================
f . 3.81823 |==================================================================
g . 3.82381 |==================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 0.628236 |================================================================
e . 0.633975 |=================================================================
f . 0.630325 |=================================================================
g . 0.629108 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 3.05991 |==================================================================
e . 3.06370 |==================================================================
f . 3.05674 |==================================================================
g . 3.05458 |==================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 3.37782 |==================================================================
e . 3.38436 |==================================================================
f . 3.37956 |==================================================================
g . 3.38156 |==================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 0.847805 |=================================================================
e . 0.844434 |=================================================================
f . 0.850691 |=================================================================
g . 0.843492 |================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 1.91374 |==================================================================
e . 1.91781 |==================================================================
f . 1.91422 |==================================================================
g . 1.91274 |==================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 2.49408 |================================================================
e . 2.56522 |==================================================================
f . 2.49714 |================================================================
g . 2.51441 |=================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 0.652259 |================================================================
e . 0.657610 |=================================================================
f . 0.653182 |=================================================================
g . 0.647700 |================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 1.03749 |============================================================
e . 1.14432 |==================================================================
f . 1.00136 |==========================================================
g . 1.12723 |=================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 1.25758 |=================================================================
e . 1.28043 |==================================================================
f . 1.20653 |==============================================================
g . 1.27918 |==================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 0.603950 |================================================================
e . 0.575794 |=============================================================
f . 0.600834 |================================================================
g . 0.612320 |=================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 1.02875 |================================================================
e . 1.05425 |==================================================================
f . 1.06144 |==================================================================
g . 1.04567 |=================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 1641.92 |==================================================================
e . 1641.00 |==================================================================
f . 1636.76 |==================================================================
g . 1637.37 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 1642.51 |==================================================================
e . 1639.36 |==================================================================
f . 1636.44 |==================================================================
g . 1631.99 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 1643.99 |==================================================================
e . 1643.97 |==================================================================
f . 1642.35 |==================================================================
g . 1641.40 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
d . 838.52 |==================================================================
e . 849.71 |===================================================================
f . 851.49 |===================================================================
g . 848.03 |===================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
d . 849.16 |===================================================================
e . 851.66 |===================================================================
f . 849.34 |===================================================================
g . 837.60 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
d . 847.38 |===================================================================
e . 841.08 |==================================================================
f . 845.31 |===================================================================
g . 847.42 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 605.04 |===================================================================
b . 605.73 |===================================================================
c . 605.92 |===================================================================
d . 606.10 |===================================================================
e . 607.91 |===================================================================
f . 607.82 |===================================================================
g . 607.16 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 16.91 |===================================================================
b . 17.07 |====================================================================
c . 16.89 |===================================================================
d . 15.72 |===============================================================
e . 15.62 |==============================================================
f . 15.72 |===============================================================
g . 15.69 |===============================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 35.63 |====================================================================
b . 35.64 |====================================================================
c . 35.68 |====================================================================
d . 31.05 |===========================================================
e . 30.99 |===========================================================
f . 31.03 |===========================================================
g . 31.06 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 118.75 |===================================================================
b . 118.95 |===================================================================
c . 118.78 |===================================================================
d . 112.25 |===============================================================
e . 112.06 |===============================================================
f . 112.41 |===============================================================
g . 112.48 |===============================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 49.37 |====================================================================
b . 49.11 |====================================================================
c . 49.02 |====================================================================
d . 49.09 |====================================================================
e . 49.01 |====================================================================
f . 49.07 |====================================================================
g . 49.03 |====================================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 4.6508 |==============================================================
b . 4.6476 |==============================================================
c . 4.6348 |==============================================================
d . 4.9960 |===================================================================
e . 4.9859 |===================================================================
f . 4.9877 |===================================================================
g . 4.9787 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 111.01 |===================================================================
b . 110.92 |===================================================================
c . 111.03 |===================================================================
d . 111.11 |===================================================================
e . 111.09 |===================================================================
f . 110.89 |===================================================================
g . 110.98 |===================================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 485.72 |================================================================
b . 487.36 |================================================================
c . 507.48 |===================================================================
d . 493.60 |=================================================================
e . 494.26 |=================================================================
f . 494.22 |=================================================================
g . 495.60 |=================================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 49.01 |====================================================================
b . 48.97 |====================================================================
c . 49.21 |====================================================================
d . 48.87 |====================================================================
e . 49.06 |====================================================================
f . 49.07 |====================================================================
g . 48.98 |====================================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 109.80 |===================================================================
b . 109.23 |==================================================================
c . 109.58 |===================================================================
d . 110.11 |===================================================================
e . 109.97 |===================================================================
f . 110.00 |===================================================================
g . 109.90 |===================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 74.32 |====================================================================
b . 74.56 |====================================================================
c . 74.50 |====================================================================
d . 73.31 |===================================================================
e . 73.26 |===================================================================
f . 73.22 |===================================================================
g . 73.19 |===================================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 347.66 |===================================================================
b . 347.22 |===================================================================
c . 347.37 |===================================================================
d . 325.88 |===============================================================
e . 325.74 |===============================================================
f . 324.96 |===============================================================
g . 325.51 |===============================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 33.34 |====================================================================
b . 33.38 |====================================================================
c . 33.46 |====================================================================
d . 33.22 |====================================================================
e . 33.26 |====================================================================
f . 33.28 |====================================================================
g . 33.37 |====================================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 150.59 |===================================================================
b . 150.61 |===================================================================
c . 145.26 |=================================================================
d . 143.76 |================================================================
e . 144.10 |================================================================
f . 143.69 |================================================================
g . 144.11 |================================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
a . 605.76 |===================================================================
b . 606.67 |===================================================================
c . 605.88 |===================================================================
d . 606.58 |===================================================================
e . 606.76 |===================================================================
f . 606.79 |===================================================================
g . 608.72 |===================================================================
OpenRadioss 2023.09.15
Model: Bumper Beam
Seconds < Lower Is Better
OpenRadioss 2023.09.15
Model: Chrysler Neon 1M
Seconds < Lower Is Better
OpenRadioss 2023.09.15
Model: Cell Phone Drop Test
Seconds < Lower Is Better
OpenRadioss 2023.09.15
Model: Bird Strike on Windshield
Seconds < Lower Is Better
OpenRadioss 2023.09.15
Model: Rubber O-Ring Seal Installation
Seconds < Lower Is Better
OpenRadioss 2023.09.15
Model: INIVOL and Fluid Structure Interaction Drop Container
Seconds < Lower Is Better
Remhos 1.0
Test: Sample Remap Example
Seconds < Lower Is Better
a . 16.35 |====================================
b . 16.79 |=====================================
c . 16.24 |====================================
d . 30.76 |====================================================================
e . 30.85 |====================================================================
f . 30.73 |====================================================================
g . 30.75 |====================================================================
SPECFEM3D 4.0
Model: Mount St. Helens
Seconds < Lower Is Better
a . 11.02 |===========================
b . 11.32 |============================
c . 11.33 |============================
d . 26.74 |==================================================================
e . 26.80 |==================================================================
f . 26.87 |==================================================================
g . 27.70 |====================================================================
SPECFEM3D 4.0
Model: Layered Halfspace
Seconds < Lower Is Better
a . 26.89 |==========================
b . 28.65 |===========================
c . 27.49 |==========================
d . 71.61 |====================================================================
e . 70.19 |===================================================================
f . 70.54 |===================================================================
g . 69.96 |==================================================================
SPECFEM3D 4.0
Model: Tomographic Model
Seconds < Lower Is Better
a . 12.31 |==============================
b . 12.10 |==============================
c . 12.04 |==============================
d . 27.33 |===================================================================
e . 27.46 |===================================================================
f . 26.97 |==================================================================
g . 27.75 |====================================================================
SPECFEM3D 4.0
Model: Homogeneous Halfspace
Seconds < Lower Is Better
a . 15.11 |=============================
b . 14.46 |============================
c . 14.81 |============================
d . 35.57 |====================================================================
e . 35.03 |===================================================================
f . 35.54 |====================================================================
g . 35.38 |====================================================================
SPECFEM3D 4.0
Model: Water-layered Halfspace
Seconds < Lower Is Better
a . 26.99 |=============================
b . 29.46 |================================
c . 27.06 |=============================
d . 62.44 |====================================================================
e . 62.33 |===================================================================
f . 61.28 |==================================================================
g . 62.81 |====================================================================
Timed Linux Kernel Compilation 6.1
Build: defconfig
Seconds < Lower Is Better
a . 27.35 |==================================
b . 27.24 |==================================
c . 27.41 |==================================
d . 55.17 |====================================================================
e . 55.09 |====================================================================
f . 55.15 |====================================================================
g . 55.17 |====================================================================
Timed Linux Kernel Compilation 6.1
Build: allmodconfig
Seconds < Lower Is Better
Blender 3.6
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 26.20 |=========================
b . 26.24 |=========================
c . 26.12 |=========================
d . 72.00 |====================================================================
e . 71.44 |===================================================================
f . 71.96 |====================================================================
g . 72.01 |====================================================================
Blender 3.6
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 66.42 |========================
b . 66.64 |========================
c . 66.72 |========================
d . 182.99 |===================================================================
e . 182.56 |===================================================================
f . 181.70 |==================================================================
g . 183.29 |===================================================================
Blender 3.6
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 33.22 |=========================
b . 33.17 |=========================
c . 33.03 |=========================
d . 90.03 |====================================================================
e . 90.31 |====================================================================
f . 90.26 |====================================================================
g . 90.63 |====================================================================
Blender 3.6
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 254.88 |=========================
b . 255.30 |=========================
c . 254.72 |=========================
d . 670.87 |===================================================================
e . 670.64 |===================================================================
f . 667.87 |===================================================================
g . 669.09 |===================================================================
Blender 3.6
Blend File: Pabellon Barcelona - Compute: CPU-Only
Seconds < Lower Is Better
a . 80.54 |========================
b . 80.76 |========================
c . 80.41 |========================
d . 224.15 |===================================================================
e . 224.10 |===================================================================
f . 223.95 |===================================================================
g . 224.12 |===================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240
Seconds < Lower Is Better
d . 1.657 |====================================================================
e . 1.654 |====================================================================
f . 1.657 |====================================================================
g . 1.648 |====================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200
Seconds < Lower Is Better
d . 38.11 |====================================================================
e . 38.07 |====================================================================
f . 38.02 |====================================================================
g . 37.95 |====================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400
Seconds < Lower Is Better
d . 98.98 |====================================================================
e . 99.42 |====================================================================
f . 97.99 |===================================================================
g . 97.53 |===================================================================