Tests for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 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 2308065-NE-XEONAUGGY78
xeon auggy
Tests for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 via the Phoronix Test Suite.
a:
Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.2.0-rc5-phx-dodt (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
b:
Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.2.0-rc5-phx-dodt (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
a . 93.01 |====================================================================
b . 92.82 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
a . 101.80 |=================================================================
b . 104.35 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
a . 156.22 |===================================================================
b . 148.18 |================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
a . 45.85 |===================================================================
b . 46.46 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
a . 107.45 |===================================================================
b . 107.95 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
a . 222.22 |=================================================================
b . 230.54 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
a . 94.45 |====================================================================
b . 91.90 |==================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
a . 102.28 |===================================================================
b . 98.83 |=================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
a . 199.10 |===================================================================
b . 199.33 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
a . 159.34 |===================================================================
b . 158.71 |===================================================================
libxsmm 2-1.17-3645
M N K: 64
GFLOPS/s > Higher Is Better
a . 1219.9 |===================================================================
b . 1216.0 |===================================================================
libxsmm 2-1.17-3645
M N K: 32
GFLOPS/s > Higher Is Better
a . 633.2 |===================================================================
b . 639.3 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
a . 185.45 |===================================================================
b . 182.03 |==================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
a . 236.67 |===================================================================
b . 236.12 |===================================================================
Stress-NG 0.15.10
Test: Pipe
Bogo Ops/s > Higher Is Better
a . 40500166.81 |===================================================
b . 49325396.97 |==============================================================
Stress-NG 0.15.10
Test: Zlib
Bogo Ops/s > Higher Is Better
a . 6879.86 |==================================================================
b . 6880.22 |==================================================================
Stress-NG 0.15.10
Test: Cloning
Bogo Ops/s > Higher Is Better
a . 16195.03 |=================================================================
b . 13172.81 |=====================================================
Stress-NG 0.15.10
Test: Pthread
Bogo Ops/s > Higher Is Better
a . 92131.54 |=================================================================
b . 90361.70 |================================================================
Stress-NG 0.15.10
Test: AVL Tree
Bogo Ops/s > Higher Is Better
a . 610.69 |===================================================================
b . 610.83 |===================================================================
Stress-NG 0.15.10
Test: Floating Point
Bogo Ops/s > Higher Is Better
a . 21134.81 |=================================================================
b . 21133.02 |=================================================================
Stress-NG 0.15.10
Test: Matrix 3D Math
Bogo Ops/s > Higher Is Better
a . 12743.81 |=================================================================
b . 12742.70 |=================================================================
Stress-NG 0.15.10
Test: Vector Shuffle
Bogo Ops/s > Higher Is Better
a . 48054.48 |=================================================================
b . 48076.78 |=================================================================
Stress-NG 0.15.10
Test: Fused Multiply-Add
Bogo Ops/s > Higher Is Better
a . 181083180.47 |=============================================================
b . 181314757.42 |=============================================================
Remhos 1.0
Test: Sample Remap Example
Seconds < Lower Is Better
a . 12.20 |===================================================================
b . 12.37 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
a . 101.94 |===================================================================
b . 102.43 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
a . 117.01 |===================================================================
b . 116.84 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
a . 46.66 |====================================================================
b . 46.51 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
a . 69.48 |====================================================================
b . 67.95 |===================================================================
Stress-NG 0.15.10
Test: Wide Vector Math
Bogo Ops/s > Higher Is Better
a . 2195391.41 |===============================================================
b . 2196242.21 |===============================================================
Stress-NG 0.15.10
Test: Vector Floating Point
Bogo Ops/s > Higher Is Better
a . 132479.08 |================================================================
b . 131100.25 |===============================================================
Opus Codec Encoding 1.4
WAV To Opus Encode
Seconds < Lower Is Better
a . 36.74 |====================================================================
b . 36.73 |====================================================================
QuantLib 1.30
MFLOPS > Higher Is Better
a . 2622.9 |===================================================================
b . 2607.6 |===================================================================
NCNN 20230517
Target: CPU - Model: mobilenet
ms < Lower Is Better
a . 16.06 |====================================================================
b . 15.90 |===================================================================
NCNN 20230517
Target: CPU-v2-v2 - Model: mobilenet-v2
ms < Lower Is Better
a . 7.91 |=====================================================================
b . 7.94 |=====================================================================
NCNN 20230517
Target: CPU-v3-v3 - Model: mobilenet-v3
ms < Lower Is Better
a . 8.71 |=====================================================================
b . 8.77 |=====================================================================
NCNN 20230517
Target: CPU - Model: shufflenet-v2
ms < Lower Is Better
a . 9.82 |=====================================================================
b . 9.75 |=====================================================================
NCNN 20230517
Target: CPU - Model: mnasnet
ms < Lower Is Better
a . 7.61 |=====================================================================
b . 7.43 |===================================================================
NCNN 20230517
Target: CPU - Model: efficientnet-b0
ms < Lower Is Better
a . 11.48 |===================================================================
b . 11.64 |====================================================================
NCNN 20230517
Target: CPU - Model: blazeface
ms < Lower Is Better
a . 4.49 |===================================================================
b . 4.61 |=====================================================================
NCNN 20230517
Target: CPU - Model: googlenet
ms < Lower Is Better
a . 17.06 |====================================================================
b . 16.72 |===================================================================
NCNN 20230517
Target: CPU - Model: vgg16
ms < Lower Is Better
a . 26.27 |====================================================================
b . 26.19 |====================================================================
NCNN 20230517
Target: CPU - Model: resnet18
ms < Lower Is Better
a . 10.31 |====================================================================
b . 9.63 |================================================================
NCNN 20230517
Target: CPU - Model: alexnet
ms < Lower Is Better
a . 5.65 |=====================================================================
b . 5.44 |==================================================================
NCNN 20230517
Target: CPU - Model: resnet50
ms < Lower Is Better
a . 17.57 |==================================================================
b . 18.15 |====================================================================
NCNN 20230517
Target: CPU - Model: yolov4-tiny
ms < Lower Is Better
a . 24.77 |====================================================================
b . 24.48 |===================================================================
NCNN 20230517
Target: CPU - Model: squeezenet_ssd
ms < Lower Is Better
a . 15.78 |===================================================================
b . 16.13 |====================================================================
NCNN 20230517
Target: CPU - Model: regnety_400m
ms < Lower Is Better
a . 38.20 |==================================================================
b . 39.37 |====================================================================
NCNN 20230517
Target: CPU - Model: vision_transformer
ms < Lower Is Better
a . 46.50 |====================================================================
b . 45.56 |===================================================================
NCNN 20230517
Target: CPU - Model: FastestDet
ms < Lower Is Better
a . 9.62 |=================================================================
b . 10.01 |====================================================================
GPAW 23.6
Input: Carbon Nanotube
Seconds < Lower Is Better
a . 45.82 |====================================================================
b . 45.64 |====================================================================
Timed GCC Compilation 13.2
Time To Compile
Seconds < Lower Is Better
a . 957.95 |===================================================================
b . 956.13 |===================================================================
dav1d 1.2.1
Video Input: Chimera 1080p
FPS > Higher Is Better
a . 516.17 |===================================================================
b . 516.50 |===================================================================
dav1d 1.2.1
Video Input: Summer Nature 4K
FPS > Higher Is Better
a . 282.53 |===================================================================
b . 282.65 |===================================================================
dav1d 1.2.1
Video Input: Summer Nature 1080p
FPS > Higher Is Better
a . 699.97 |===================================================================
b . 699.09 |===================================================================
dav1d 1.2.1
Video Input: Chimera 1080p 10-bit
FPS > Higher Is Better
a . 476.82 |===================================================================
b . 476.77 |===================================================================
Blender 3.6
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
a . 23.69 |====================================================================
b . 23.62 |====================================================================
Blender 3.6
Blend File: Classroom - Compute: CPU-Only
Seconds < Lower Is Better
a . 62.35 |====================================================================
b . 62.51 |====================================================================
Blender 3.6
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
a . 30.59 |====================================================================
b . 30.77 |====================================================================
Blender 3.6
Blend File: Barbershop - Compute: CPU-Only
Seconds < Lower Is Better
a . 239.55 |===================================================================
b . 239.03 |===================================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 5.672 |===================================================================
b . 5.717 |====================================================================
VVenC 1.9
Video Input: Bosphorus 1080p - Video Preset: Fast
Frames Per Second > Higher Is Better
a . 15.71 |====================================================================
b . 15.72 |====================================================================
Z3 Theorem Prover 4.12.1
SMT File: 2.smt2
Seconds < Lower Is Better
a . 88.00 |====================================================================
b . 87.18 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: double - X Y Z: 512
GFLOP/s > Higher Is Better
a . 94.26 |====================================================================
b . 92.72 |===================================================================
Z3 Theorem Prover 4.12.1
SMT File: 1.smt2
Seconds < Lower Is Better
a . 25.71 |====================================================================
b . 25.25 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: Stock - Precision: float - X Y Z: 512
GFLOP/s > Higher Is Better
a . 176.63 |===================================================================
b . 174.11 |==================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: float - X Y Z: 512
GFLOP/s > Higher Is Better
a . 93.33 |====================================================================
b . 92.26 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 512
GFLOP/s > Higher Is Better
a . 90.57 |====================================================================
b . 90.24 |====================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 512
GFLOP/s > Higher Is Better
a . 49.44 |====================================================================
b . 48.72 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 512
GFLOP/s > Higher Is Better
a . 170.91 |===================================================================
b . 171.13 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 512
GFLOP/s > Higher Is Better
a . 94.83 |====================================================================
b . 94.34 |====================================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 10.28 |===================================================================
b . 10.43 |====================================================================
VVenC 1.9
Video Input: Bosphorus 1080p - Video Preset: Faster
Frames Per Second > Higher Is Better
a . 29.08 |====================================================================
b . 29.18 |====================================================================
Embree 4.1
Binary: Pathtracer - Model: Crown
Frames Per Second > Higher Is Better
a . 72.04 |====================================================================
b . 70.79 |===================================================================
Embree 4.1
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
a . 87.93 |====================================================================
b . 87.93 |====================================================================
libxsmm 2-1.17-3645
M N K: 256
GFLOPS/s > Higher Is Better
a . 599.8 |====================================================================
b . 592.5 |===================================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: Stock - Precision: double - X Y Z: 512
GFLOP/s > Higher Is Better
a . 47.28 |====================================================================
b . 47.39 |====================================================================
Embree 4.1
Binary: Pathtracer - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 85.24 |====================================================================
b . 85.13 |====================================================================
Embree 4.1
Binary: Pathtracer - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 76.96 |====================================================================
b . 77.26 |====================================================================
Embree 4.1
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 104.41 |===================================================================
b . 104.55 |===================================================================
Embree 4.1
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 89.84 |====================================================================
b . 90.01 |====================================================================
Intel Open Image Denoise 2.0
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 3.04 |=====================================================================
b . 3.01 |====================================================================
Intel Open Image Denoise 2.0
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 3.05 |=====================================================================
b . 3.03 |=====================================================================
Intel Open Image Denoise 2.0
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 1.47 |=====================================================================
b . 1.46 |=====================================================================
OSPRay 2.12
Benchmark: particle_volume/ao/real_time
Items Per Second > Higher Is Better
a . 24.64 |====================================================================
b . 24.62 |====================================================================
OSPRay 2.12
Benchmark: particle_volume/scivis/real_time
Items Per Second > Higher Is Better
a . 24.36 |===================================================================
b . 24.78 |====================================================================
OSPRay 2.12
Benchmark: particle_volume/pathtracer/real_time
Items Per Second > Higher Is Better
a . 151.14 |===================================================================
b . 151.27 |===================================================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/ao/real_time
Items Per Second > Higher Is Better
a . 21.21 |====================================================================
b . 20.95 |===================================================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/scivis/real_time
Items Per Second > Higher Is Better
a . 20.81 |====================================================================
b . 20.48 |===================================================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time
Items Per Second > Higher Is Better
a . 22.70 |====================================================================
b . 22.58 |====================================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 992540000 |================================================================
b . 993445000 |================================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 1197700000 |===============================================================
b . 1185500000 |==============================================================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 1805000000 |==============================================================
b . 1825450000 |===============================================================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 2069200000 |===============================================================
b . 2076650000 |===============================================================
Liquid-DSP 1.6
Threads: 128 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 2961100000 |===============================================================
b . 2945150000 |===============================================================
Liquid-DSP 1.6
Threads: 128 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 2519200000 |===============================================================
b . 2426350000 |=============================================================
Liquid-DSP 1.6
Threads: 160 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 3390700000 |===============================================================
b . 3381950000 |===============================================================
Liquid-DSP 1.6
Threads: 160 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 2602300000 |==============================================================
b . 2636450000 |===============================================================
Liquid-DSP 1.6
Threads: 32 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 400730000 |================================================================
b . 396265000 |===============================================================
Liquid-DSP 1.6
Threads: 64 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 725840000 |================================================================
b . 730310000 |================================================================
Liquid-DSP 1.6
Threads: 128 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 949400000 |================================================================
b . 945190000 |================================================================
Liquid-DSP 1.6
Threads: 160 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 1013200000 |===============================================================
b . 1011200000 |===============================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 32338000 |=================================================================
b . 32267000 |=================================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 53918500 |=================================================================
b . 53926500 |=================================================================
Liquid-DSP 1.6
Threads: 1 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 13323000 |=================================================================
b . 13291000 |=================================================================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 32
samples/s > Higher Is Better
a . 493660000 |===============================================================
b . 498410000 |================================================================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 57
samples/s > Higher Is Better
a . 615105000 |===============================================================
b . 623535000 |================================================================
Liquid-DSP 1.6
Threads: 16 - Buffer Length: 256 - Filter Length: 512
samples/s > Higher Is Better
a . 201615000 |================================================================
b . 198790000 |===============================================================
srsRAN Project 23.5
Test: Downlink Processor Benchmark
Mbps > Higher Is Better
a . 556.5 |====================================================================
b . 556.8 |====================================================================
srsRAN Project 23.5
Test: PUSCH Processor Benchmark, Throughput Total
Mbps > Higher Is Better
a . 9800.5 |===================================================================
b . 9756.7 |===================================================================
srsRAN Project 23.5
Test: PUSCH Processor Benchmark, Throughput Thread
Mbps > Higher Is Better
a . 164.8 |====================================================================
b . 164.7 |====================================================================
Apache CouchDB 3.3.2
Bulk Size: 100 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 94.83 |====================================================================
Apache CouchDB 3.3.2
Bulk Size: 300 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 152.46 |===================================================================
Apache CouchDB 3.3.2
Bulk Size: 500 - Inserts: 1000 - Rounds: 30
Seconds < Lower Is Better
a . 1090.42 |==================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 638644.35 |================================================================
b . 628202.55 |===============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency < Lower Is Better
a . 17.54 |==================================================================
b . 18.04 |====================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 995259.68 |================================================================
b . 992909.69 |================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency < Lower Is Better
a . 35.99 |====================================================================
b . 36.16 |====================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 904320.60 |===============================================================
b . 920435.77 |================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency < Lower Is Better
a . 14.84 |====================================================================
b . 14.42 |==================================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 1134736.54 |===============================================================
b . 1137612.61 |===============================================================
Apache IoTDB 1.1.2
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency < Lower Is Better
a . 36.74 |====================================================================
b . 36.82 |====================================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
point/sec > Higher Is Better
a . 1199743.22 |===============================================================
b . 1141859.25 |============================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Average Latency < Lower Is Better
a . 13.25 |================================================================
b . 14.12 |====================================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
point/sec > Higher Is Better
a . 1343156.56 |==============================================================
b . 1372429.58 |===============================================================
Apache IoTDB 1.1.2
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Average Latency < Lower Is Better
a . 33.38 |====================================================================
b . 32.75 |===================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
point/sec > Higher Is Better
a . 34266143.85 |=============================================================
b . 34807016.85 |==============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Average Latency < Lower Is Better
a . 42.79 |====================================================================
b . 42.19 |===================================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
point/sec > Higher Is Better
a . 39562245.22 |=========================================================
b . 43021501.40 |==============================================================
Apache IoTDB 1.1.2
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Average Latency < Lower Is Better
a . 109.40 |===================================================================
b . 96.18 |===========================================================
Dragonflydb 1.6.2
Clients Per Thread: 10 - Set To Get Ratio: 1:5
libxsmm 2-1.17-3645
M N K: 128
GFLOPS/s > Higher Is Better
a . 1055.3 |====================================
b . 1946.7 |===================================================================
Dragonflydb 1.6.2
Clients Per Thread: 20 - Set To Get Ratio: 1:5
Dragonflydb 1.6.2
Clients Per Thread: 50 - Set To Get Ratio: 1:5
Dragonflydb 1.6.2
Clients Per Thread: 60 - Set To Get Ratio: 1:5
Dragonflydb 1.6.2
Clients Per Thread: 10 - Set To Get Ratio: 1:10
Dragonflydb 1.6.2
Clients Per Thread: 20 - Set To Get Ratio: 1:10
Dragonflydb 1.6.2
Clients Per Thread: 50 - Set To Get Ratio: 1:10
Dragonflydb 1.6.2
Clients Per Thread: 60 - Set To Get Ratio: 1:10
Dragonflydb 1.6.2
Clients Per Thread: 10 - Set To Get Ratio: 1:100
Dragonflydb 1.6.2
Clients Per Thread: 20 - Set To Get Ratio: 1:100
Dragonflydb 1.6.2
Clients Per Thread: 50 - Set To Get Ratio: 1:100
Dragonflydb 1.6.2
Clients Per Thread: 60 - Set To Get Ratio: 1:100