okt

AMD Ryzen 9 3900XT 12-Core testing with a MSI MEG X570 GODLIKE (MS-7C34) v1.0 (1.B3 BIOS) and AMD Radeon RX 56/64 8GB on Ubuntu 22.04 via the Phoronix Test Suite.

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AV1 3 Tests
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October 30 2023
  10 Hours, 59 Minutes
b
October 30 2023
  11 Hours, 3 Minutes
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okt AMD Ryzen 9 3900XT 12-Core testing with a MSI MEG X570 GODLIKE (MS-7C34) v1.0 (1.B3 BIOS) and AMD Radeon RX 56/64 8GB on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b" Processor,,AMD Ryzen 9 3900XT 12-Core @ 3.80GHz (12 Cores / 24 Threads),AMD Ryzen 9 3900XT 12-Core @ 3.80GHz (12 Cores / 24 Threads) Motherboard,,MSI MEG X570 GODLIKE (MS-7C34) v1.0 (1.B3 BIOS),MSI MEG X570 GODLIKE (MS-7C34) v1.0 (1.B3 BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse Memory,,16GB,16GB Disk,,500GB Seagate FireCuda 520 SSD ZP500GM30002,500GB Seagate FireCuda 520 SSD ZP500GM30002 Graphics,,AMD Radeon RX 56/64 8GB (1630/945MHz),AMD Radeon RX 56/64 8GB (1630/945MHz) Audio,,AMD Vega 10 HDMI Audio,AMD Vega 10 HDMI Audio Monitor,,ASUS MG28U,ASUS MG28U Network,,Realtek Device 2600 + Realtek Killer E3000 2.5GbE + Intel Wi-Fi 6 AX200,Realtek Device 2600 + Realtek Killer E3000 2.5GbE + Intel Wi-Fi 6 AX200 OS,,Ubuntu 22.04,Ubuntu 22.04 Kernel,,6.2.0-35-generic (x86_64),6.2.0-35-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server + Wayland,X Server + Wayland OpenGL,,4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.49),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.49) Vulkan,,1.3.204,1.3.204 Compiler,,GCC 11.4.0,GCC 11.4.0 File-System,,ext4,ext4 Screen Resolution,,3840x2160,3840x2160 ,,"a","b" "SQLite - Threads / Copies: 1 (sec)",LIB,15.155,15.272 "SQLite - Threads / Copies: 2 (sec)",LIB,26.109,43.165 "SQLite - Threads / Copies: 4 (sec)",LIB,31.238,66.299 "SQLite - Threads / Copies: 8 (sec)",LIB,46.349,153.746 "3DMark Wild Life Extreme - Resolution: 1920 x 1080 (FPS)",HIB,251.67,253.07 "QuantLib - Configuration: Multi-Threaded (MFLOPS)",HIB,42432.3,42491.7 "QuantLib - Configuration: Single-Threaded (MFLOPS)",HIB,3064.1,3055.5 "Crypto++ - Test: Unkeyed Algorithms (MiB/s)",HIB,415.494466,439.590685 "High Performance Conjugate Gradient - X Y Z: 104 104 104 - RT: 60 (GFLOP/s)",HIB,5.10918,5.09819 "CloverLeaf - Input: clover_bm (sec)",LIB,134.11,135.45 "CloverLeaf - Input: clover_bm64_short (sec)",LIB,391.22,391.60 "CP2K Molecular Dynamics - Input: H20-64 (sec)",LIB,102.991,102.745 "CP2K Molecular Dynamics - Input: H2O-DFT-LS (sec)",LIB,, "CP2K Molecular Dynamics - Input: Fayalite-FIST (sec)",LIB,171.292,171.272 "libxsmm - M N K: 128 (GFLOPS/s)",HIB,231,230.3 "libxsmm - M N K: 32 (GFLOPS/s)",HIB,55,54.9 "libxsmm - M N K: 64 (GFLOPS/s)",HIB,112.8,112.7 "Palabos - Grid Size: 100 (Mega Site Updates/sec)",HIB,40.839,40.6509 "QMCPACK - Input: H4_ae (Execution Time - sec)",LIB,24.37,23.6 "QMCPACK - Input: Li2_STO_ae (Execution Time - sec)",LIB,229.86,231.53 "QMCPACK - Input: LiH_ae_MSD (Execution Time - sec)",LIB,111.68,108.69 "QMCPACK - Input: simple-H2O (Execution Time - sec)",LIB,26.114,26.329 "QMCPACK - Input: O_ae_pyscf_UHF (Execution Time - sec)",LIB,185.19,186.52 "QMCPACK - Input: FeCO6_b3lyp_gms (Execution Time - sec)",LIB,169.28,169.43 "OpenRadioss - Model: Bumper Beam (sec)",LIB,131.78,131.94 "OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,1612.17,1610.5 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,98.05,97.7 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,272.77,272.42 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,134.16,134.76 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,600.03,599.25 "Z3 Theorem Prover - SMT File: 1.smt2 (sec)",LIB,29.481,29.326 "Z3 Theorem Prover - SMT File: 2.smt2 (sec)",LIB,75.478,76.66 "nekRS - Input: Kershaw (flops/rank)",HIB,1771410000,1777340000 "nekRS - Input: TurboPipe Periodic (flops/rank)",HIB,2989030000,2989160000 "srsRAN Project - Test: Downlink Processor Benchmark (Mbps)",HIB,824.7,800.9 "srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Total (Mbps)",HIB,2003.6,1999.6 "srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Thread (Mbps)",HIB,245.5,245.1 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 (sec)",LIB,12.292,12.265 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 (sec)",LIB,382.078,382.269 "dav1d - Video Input: Chimera 1080p (FPS)",HIB,402.46,400.81 "dav1d - Video Input: Summer Nature 4K (FPS)",HIB,196.13,196.12 "dav1d - Video Input: Summer Nature 1080p (FPS)",HIB,793.04,793.9 "dav1d - Video Input: Chimera 1080p 10-bit (FPS)",HIB,457.63,458.1 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,15.3439,15.1862 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,14.034,14.1009 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,16.2704,16.3297 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,14.6908,14.7704 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,15.5772,15.649 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,13.4423,13.4374 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,3.15,3.109 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,43.194,43.193 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,92.08,93.275 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,92.635,91.617 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,8.875,8.897 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,80.613,79.873 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,352.036,350.351 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,397.449,394.016 "VVenC - Video Input: Bosphorus 4K - Video Preset: Fast (FPS)",HIB,4.422,4.415 "VVenC - Video Input: Bosphorus 4K - Video Preset: Faster (FPS)",HIB,9.018,9.051 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Fast (FPS)",HIB,14.143,14.085 "VVenC - Video Input: Bosphorus 1080p - Video Preset: Faster (FPS)",HIB,28.39,28.412 "Intel Open Image Denoise - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.46,0.45 "Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,0.44,0.44 "Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,0.23,0.22 "OpenVKL - Benchmark: vklBenchmarkCPU ISPC (Items / Sec)",HIB,230,230 "OpenVKL - Benchmark: vklBenchmarkCPU Scalar (Items / Sec)",HIB,125,126 "OSPRay - Benchmark: particle_volume/ao/real_time (Items/sec)",HIB,3.83843,3.8418 "OSPRay - Benchmark: particle_volume/scivis/real_time (Items/sec)",HIB,3.79088,3.79143 "OSPRay - Benchmark: particle_volume/pathtracer/real_time (Items/sec)",HIB,111.963,110.473 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/ao/real_time (Items/sec)",HIB,1.94041,1.95769 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/scivis/real_time (Items/sec)",HIB,1.82533,1.83527 "OSPRay - Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time (Items/sec)",HIB,3.07954,3.07569 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,128.999,128.798 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,63.918,63.175 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,6.329,6.238 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,10.593,10.513 "libavif avifenc - Encoder Speed: 10, Lossless (sec)",LIB,6.291,6.229 "Timed GCC Compilation - Time To Compile (sec)",LIB,1095.023,1094.513 "Timed Gem5 Compilation - Time To Compile (sec)",LIB,460.886,457.199 "Timed Godot Game Engine Compilation - Time To Compile (sec)",LIB,281.547,281.706 "Timed LLVM Compilation - Build System: Ninja (sec)",LIB,573.501,575.033 "Timed LLVM Compilation - Build System: Unix Makefiles (sec)",LIB,591.045,600.278 "Timed Node.js Compilation - Time To Compile (sec)",LIB,,468.621 "Build2 - Time To Compile (sec)",LIB,123.554,119.192 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,4.76396,4.7208 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,10.6365,11.1973 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.87701,1.79957 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.893574,0.931971 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,22.3492,22.3837 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,7.4047,7.51062 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,5.39869,5.37146 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,23.944,24.1847 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.47724,2.46296 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.44831,3.47437 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,3936.51,3965.21 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,2418.33,2414.21 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3923.99,3959.79 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,, "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2410.82,2405.08 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3937.24,3967.68 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2409.49,2412.87 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,10796,10809 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,11044,11022 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,12790,12792 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,178923,179435 "OSPRay Studio - Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,354104,352945 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,182398,182424 "OSPRay Studio - Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,359981,359095 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,210828,213159 "OSPRay Studio - Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,415946,415202 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,2712,2718 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,2773,2762 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,3222,3213 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,49808,49490 "OSPRay Studio - Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,92700,93216 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,50479,50427 "OSPRay Studio - Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,94954,94957 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,57406,57662 "OSPRay Studio - Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU (ms)",LIB,108963,108921 "Opus Codec Encoding - WAV To Opus Encode (sec)",LIB,29.929,29.64 "Cpuminer-Opt - Algorithm: Magi (kH/s)",HIB,370.56,371.36 "Cpuminer-Opt - Algorithm: scrypt (kH/s)",HIB,128.03,128.06 "Cpuminer-Opt - Algorithm: Deepcoin (kH/s)",HIB,4267.42,4307.38 "Cpuminer-Opt - Algorithm: Ringcoin (kH/s)",HIB,1882.92,1936.29 "Cpuminer-Opt - Algorithm: Blake-2 S (kH/s)",HIB,73570,73540 "Cpuminer-Opt - Algorithm: Garlicoin (kH/s)",HIB,1281.81,1333.62 "Cpuminer-Opt - Algorithm: Skeincoin (kH/s)",HIB,18940,18510 "Cpuminer-Opt - Algorithm: Myriad-Groestl (kH/s)",HIB,6257.66,6205.35 "Cpuminer-Opt - Algorithm: LBC, LBRY Credits (kH/s)",HIB,8168.31,8157.18 "Cpuminer-Opt - Algorithm: Quad SHA-256, Pyrite (kH/s)",HIB,28470,28390 "Cpuminer-Opt - Algorithm: Triple SHA-256, Onecoin (kH/s)",HIB,40300,40150 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,47740000,45239000 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,53633000,52191000 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,88956000,90487000 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,102790000,104190000 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,174840000,176360000 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,200240000,201440000 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,344230000,341300000 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,391000000,395730000 "Liquid-DSP - Threads: 1 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,10723000,10761000 "Liquid-DSP - Threads: 16 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,640290000,633870000 "Liquid-DSP - Threads: 16 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,630380000,631120000 "Liquid-DSP - Threads: 2 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,21283000,21268000 "Liquid-DSP - Threads: 24 - Buffer Length: 256 - Filter Length: 32 (samples/s)",HIB,890410000,890730000 "Liquid-DSP - Threads: 24 - Buffer Length: 256 - Filter Length: 57 (samples/s)",HIB,732320000,721140000 "Liquid-DSP - Threads: 4 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,39977000,39956000 "Liquid-DSP - Threads: 8 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,78180000,78962000 "Liquid-DSP - Threads: 16 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,145080000,143910000 "Liquid-DSP - Threads: 24 - Buffer Length: 256 - Filter Length: 512 (samples/s)",HIB,198510000,198780000 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100 (point/sec)",HIB,870895,876234 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 100 (Latency)",LIB,21.46,21.31 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400 (point/sec)",HIB,883206,907114 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 200 - Client Number: 400 (Latency)",LIB,82.72,79.59 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100 (point/sec)",HIB,1598972,1634118 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 100 (Latency)",LIB,29.57,29.06 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,1680328,1678639 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,108.87,108.49 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100 (point/sec)",HIB,1353913,1345876 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 100 (Latency)",LIB,57.1,57.24 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,1946289,1907446 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 1 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,151.1,154.86 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100 (point/sec)",HIB,27513199,27931670 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 100 (Latency)",LIB,68.27,67.75 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 200 - Client Number: 400 ()",,, "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100 (point/sec)",HIB,22648486,25003579 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100 (Latency)",LIB,214.04,193.72 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,23078340,22919674 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,783.24,800.04 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100 (point/sec)",HIB,23311141,21936964 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 100 (Latency)",LIB,335.42,356.7 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,19627767,18927006 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,1540.09,1597.52 "Memcached - Set To Get Ratio: 1:10 (Ops/sec)",HIB,1646210.97,1656534.13 "Memcached - Set To Get Ratio: 1:100 (Ops/sec)",HIB,1590140.06,1598152.08 "DuckDB - Benchmark: IMDB (sec)",LIB,, "DuckDB - Benchmark: TPC-H Parquet (sec)",LIB,, "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only (TPS)",HIB,492881,513618 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,2.029,1.947 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write (TPS)",HIB,9823,9833 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,101.804,101.701 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,10.21,10.29 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,10.05,10.06 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,9.9426,9.9514 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,603.4131,601.8748 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.1515,8.1693 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,122.6699,122.4024 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,237.5691,237.6562 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,25.2289,25.2209 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,130.7431,131.2231 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,7.644,7.6158 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,94.1953,93.8829 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,63.6788,63.8902 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (items/sec)",HIB,51.7574,52.2652 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream (ms/batch)",LIB,19.314,19.126 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,31.7265,31.5674 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,189.0831,190.0338 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (items/sec)",HIB,19.8169,19.6988 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,50.4539,50.7568 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,125.0097,125.1795 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,47.9742,47.9091 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,85.9707,85.8786 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.6246,11.637 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,737.2404,737.749 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,8.1177,8.1122 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,466.2224,467.1283 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,2.1424,2.1383 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,55.5772,55.8768 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,107.8695,107.3495 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,46.3632,46.4968 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,21.5583,21.497 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,12.676,12.6878 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,473.3056,472.8641 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,9.3034,9.3103 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,107.4805,107.4009 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,124.4685,124.7254 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,48.1839,48.065 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,85.5166,85.4024 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.6861,11.7019 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,56.4458,56.3444 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,106.2711,106.3662 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,46.7111,46.6825 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,21.4011,21.415 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,86.3167,86.3419 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,69.4918,69.4695 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,63.8366,64.0296 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,15.6588,15.6117 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,12.2373,12.3051 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,490.268,487.5642 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,11.2075,11.2046 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,89.2132,89.2356 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,126.6146,126.9044 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,47.3217,47.2413 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,57.339,57.5387 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,17.4329,17.372 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,43.7603,44.0421 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,137.0885,136.2151 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (items/sec)",HIB,32.1494,32.3176 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,31.0984,30.9366 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,9.9597,9.9696 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,602.3735,600.8695 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,8.1486,8.1412 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,122.7143,122.8253 "Stress-NG - Test: Hash (Bogo Ops/s)",HIB,2849944.32,2861814.33 "Stress-NG - Test: MMAP (Bogo Ops/s)",HIB,169.42,168.18 "Stress-NG - Test: NUMA (Bogo Ops/s)",HIB,134.28,134.69 "Stress-NG - Test: Pipe (Bogo Ops/s)",HIB,5085408.46,5137270.21 "Stress-NG - Test: Poll (Bogo Ops/s)",HIB,1274815.51,1269594.39 "Stress-NG - Test: Zlib (Bogo Ops/s)",HIB,1529.17,1536.76 "Stress-NG - Test: Futex (Bogo Ops/s)",HIB,2719437.33,2724951.63 "Stress-NG - Test: MEMFD (Bogo Ops/s)",HIB,167.93,173.1 "Stress-NG - Test: Mutex (Bogo Ops/s)",HIB,3757618.65,3763560.06 "Stress-NG - Test: Atomic (Bogo Ops/s)",HIB,546.82,550.8 "Stress-NG - Test: Crypto (Bogo Ops/s)",HIB,30063.86,30023.08 "Stress-NG - Test: Malloc (Bogo Ops/s)",HIB,6492227.26,6507116.41 "Stress-NG - Test: Cloning (Bogo Ops/s)",HIB,876.55,876.19 "Stress-NG - Test: Forking (Bogo Ops/s)",HIB,25858.27,26129.74 "Stress-NG - Test: Pthread (Bogo Ops/s)",HIB,112428.11,112860.19 "Stress-NG - Test: AVL Tree (Bogo Ops/s)",HIB,116.18,116.68 "Stress-NG - Test: IO_uring (Bogo Ops/s)",HIB,151644.87,147373.62 "Stress-NG - Test: SENDFILE (Bogo Ops/s)",HIB,127691.03,127965.64 "Stress-NG - Test: CPU Cache (Bogo Ops/s)",HIB,1265046.6,1276371.48 "Stress-NG - Test: CPU Stress (Bogo Ops/s)",HIB,31748.06,30131.49 "Stress-NG - Test: Semaphores (Bogo Ops/s)",HIB,14365053.84,14280745.55 "Stress-NG - Test: Matrix Math (Bogo Ops/s)",HIB,76846.98,77971.11 "Stress-NG - Test: Vector Math (Bogo Ops/s)",HIB,87181.78,87270.26 "Stress-NG - Test: AVX-512 VNNI (Bogo Ops/s)",HIB,527354.87,528272.84 "Stress-NG - Test: Function Call (Bogo Ops/s)",HIB,9373.74,9463.1 "Stress-NG - Test: x86_64 RdRand (Bogo Ops/s)",HIB,6422.15,6421.87 "Stress-NG - Test: Floating Point (Bogo Ops/s)",HIB,4282.52,4272.51 "Stress-NG - Test: Matrix 3D Math (Bogo Ops/s)",HIB,887.15,871.12 "Stress-NG - Test: Memory Copying (Bogo Ops/s)",HIB,3721.25,3725.74 "Stress-NG - Test: Vector Shuffle (Bogo Ops/s)",HIB,8782.71,8803.33 "Stress-NG - Test: Mixed Scheduler (Bogo Ops/s)",HIB,8305.74,8320.26 "Stress-NG - Test: Socket Activity (Bogo Ops/s)",HIB,7209.11,7240.74 "Stress-NG - Test: Wide Vector Math (Bogo Ops/s)",HIB,587699.43,584000.31 "Stress-NG - Test: Context Switching (Bogo Ops/s)",HIB,2670705.05,2690991.53 "Stress-NG - Test: Fused Multiply-Add (Bogo Ops/s)",HIB,13209967.5,13224332.05 "Stress-NG - Test: Vector Floating Point (Bogo Ops/s)",HIB,35283.23,35339.57 "Stress-NG - Test: Glibc C String Functions (Bogo Ops/s)",HIB,13058517.39,12751183.72 "Stress-NG - Test: Glibc Qsort Data Sorting (Bogo Ops/s)",HIB,370.88,370.05 "Stress-NG - Test: System V Message Passing (Bogo Ops/s)",HIB,8498512.2,8490199.38 "GPAW - Input: Carbon Nanotube (sec)",LIB,310.499,310.453 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,13.21,13.2 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,4.24,4.23 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,3.64,3.62 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,4.62,4.6 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,3.81,3.81 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,6.21,6.18 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,1.76,1.72 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,14.18,14.03 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,52.11,52.55 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,9.75,9.82 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,9.14,9.23 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,18.14,17.98 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,24.32,24.27 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,11.93,11.91 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,10.3,10.14 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,70.77,70.42 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,5.11,5.09 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,106.56,107.52 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,289.11,290.28 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,134.28,133.77 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,1113.55,1113.73 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,345.49,343.58 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,3.14,3.14 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,1907.31,1894.17 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,24.67,24.42 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,242.85,245.33 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,24.7,24.3 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,242.84,246.67 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,150.52,149.62 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,39.83,40.08 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,4.3,4.3 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,1389.42,1388.86 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,674.92,678.1 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,8.87,8.83 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,39.08,39.27 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,153.43,152.68 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,354.56,354.06 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,16.91,16.93 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,302.35,303.21 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,19.82,19.76 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,1065.97,1067.32 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,5.62,5.61 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,158.63,158.31 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,37.79,37.87 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,34.38,34.87 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,174.33,171.89 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,424.82,425.1 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,28.23,28.21 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,328.8,325.14 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,18.23,18.44 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,129.11,130.15 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,92.88,92.11 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,9800.16,9776.02 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,1.22,1.22 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,134.26,134.3 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,89.28,89.31 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,14175.7,14139.29 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.84,0.84 "Apache Cassandra - Test: Writes (Op/s)",HIB,110053,111134 "Apache Hadoop - Operation: Create - Threads: 20 - Files: 100000 (Ops per sec)",HIB,18804,19128 "nginx - Connections: 100 (Reqs/sec)",HIB,79156.02,78192.19 "nginx - Connections: 200 (Reqs/sec)",HIB,76304.8,76041.37 "nginx - Connections: 500 (Reqs/sec)",HIB,72766.53,72403.86 "nginx - Connections: 1000 (Reqs/sec)",HIB,63534.92,62870.64 "Apache HTTP Server - Concurrent Requests: 100 (Reqs/sec)",HIB,, "Apache HTTP Server - Concurrent Requests: 200 (Reqs/sec)",HIB,, "Apache HTTP Server - Concurrent Requests: 500 (Reqs/sec)",HIB,, "Apache HTTP Server - Concurrent Requests: 1000 (Reqs/sec)",HIB,, "Whisper.cpp - Model: ggml-base.en - Input: 2016 State of the Union (sec)",LIB,160.65923,159.8743 "Whisper.cpp - Model: ggml-small.en - Input: 2016 State of the Union (sec)",LIB,475.27406,471.32178 "Whisper.cpp - Model: ggml-medium.en - Input: 2016 State of the Union (sec)",LIB,1447.53988,1472.8675 "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,189733,190422