KVM testing on Ubuntu 22.04 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 2310039-NE-2310031NE80
GCE c3d-standard-60
KVM testing on Ubuntu 22.04 via the Phoronix Test Suite.
,,"c3d-standard-60 AMD Genoa","t2d-standard-60 AMD Milan"
Processor,,AMD EPYC 9B14 (30 Cores / 60 Threads),AMD EPYC 7B13 (60 Cores)
Motherboard,,Google Compute Engine c3d-standard-60,Google Compute Engine t2d-standard-60
Chipset,,Intel 440FX 82441FX PMC,Intel 440FX 82441FX PMC
Memory,,240GB,240GB
Disk,,215GB nvme_card-pd,215GB PersistentDisk
Network,,Google Compute Engine Virtual,Red Hat Virtio device
OS,,Ubuntu 22.04,Ubuntu 22.04
Kernel,,6.2.0-1014-gcp (x86_64),6.2.0-1014-gcp (x86_64)
Vulkan,,1.3.238,1.3.238
Compiler,,GCC 11.4.0,GCC 11.4.0
File-System,,ext4,ext4
System Layer,,KVM,KVM
,,"c3d-standard-60 AMD Genoa","t2d-standard-60 AMD Milan"
"BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,510819,629363
"nekRS - Input: Kershaw (flops/rank)",HIB,4289858333,3681935833
"nekRS - Input: TurboPipe Periodic (flops/rank)",HIB,4723940000,2730620000
"OpenRadioss - Model: Bumper Beam (sec)",LIB,92.87,75.68
"OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,337.70,327.88
"OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,38.82,30.12
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,148.575,196.948
"OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,147.31,123.61
"OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,89.65,72.06
"Laghos - Test: Sedov Blast Wave, ube_922_hex.mesh (Major Kernels Rate)",HIB,259.55,364.64
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,88.6301,109.676
"libxsmm - M N K: 64 (GFLOPS/s)",HIB,489.7,554.2
"Laghos - Test: Triple Point Problem (Major Kernels Rate)",HIB,209.00,222.30
"HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,57.3116,60.0343
"libxsmm - M N K: 32 (GFLOPS/s)",HIB,255.4,289.2
"OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,,
"TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,50.99,18.29
"TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,69.68,20.90
"GROMACS - Implementation: MPI CPU - Input: water_GMX50_bare (Ns/Day)",HIB,4.391,5.289
"LAMMPS Molecular Dynamics Simulator - Model: 20k Atoms (ns/day)",HIB,19.776,26.734
"HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,93.6005,106.029
"TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,62.74,20.36
"LAMMPS Molecular Dynamics Simulator - Model: Rhodopsin Protein (ns/day)",HIB,17.423,27.828
"NAS Parallel Benchmarks - Test / Class: CG.C (Mop/s)",HIB,19597.86,16649.37
"Remhos - Test: Sample Remap Example (sec)",LIB,33.362,16.326
"NAS Parallel Benchmarks - Test / Class: BT.C (Mop/s)",HIB,96257.48,122720.61
"NAS Parallel Benchmarks - Test / Class: EP.D (Mop/s)",HIB,3783.60,4935.68
"NAS Parallel Benchmarks - Test / Class: FT.C (Mop/s)",HIB,39647.47,54846.18
"NAS Parallel Benchmarks - Test / Class: IS.D (Mop/s)",HIB,2422.40,1752.62
"NAS Parallel Benchmarks - Test / Class: LU.C (Mop/s)",HIB,73563.13,94247.77
"NAS Parallel Benchmarks - Test / Class: MG.C (Mop/s)",HIB,42701.83,47291.96
"NAS Parallel Benchmarks - Test / Class: SP.C (Mop/s)",HIB,39919.71,43228.11
"Rodinia - Test: OpenMP LavaMD (sec)",LIB,64.862,50.974
"Rodinia - Test: OpenMP HotSpot3D (sec)",LIB,84.166,88.535
"Rodinia - Test: OpenMP Leukocyte (sec)",LIB,45.498,42.010
"Rodinia - Test: OpenMP CFD Solver (sec)",LIB,10.025,7.368
"Rodinia - Test: OpenMP Streamcluster (sec)",LIB,6.448,6.423
"OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,18.39,10.73
"OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,648.81,1393.56
"OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,142.75,73.74
"OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,83.99,208.47
"OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,142.90,78.96
"OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,83.91,193.45
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,1389.69,368.39
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,8.62,40.67
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,35.19,26.28
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,340.29,568.77
"OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,4166.39,1285.47
"OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,2.87,11.65
"OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,576.94,225.48
"OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,20.77,66.46
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,2043.05,1512.57
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,5.86,9.90
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,1875.28,1014.70
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,15.98,14.76
"OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,6605.12,4239.52
"OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,4.53,3.52
"OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,645.74,565.34
"OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,18.56,26.50
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,185.29,96.58
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,64.70,155.14
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,3650.57,2646.58
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,8.20,11.32
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,1764.21,633.76
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,6.79,23.64
"OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,964.46,370.03
"OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,31.08,81.00
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,43607.04,29668.12
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,0.52,0.99
"OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,761.14,390.72
"OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,39.38,76.72
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,54971.26,44049.15
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.4,0.61
"Algebraic Multi-Grid Benchmark - (Figure Of Merit)",HIB,962889833,920427767
"Xcompact3d Incompact3d - Input: input.i3d 129 Cells Per Direction (sec)",LIB,5.87157885,5.63057327
"Xcompact3d Incompact3d - Input: input.i3d 193 Cells Per Direction (sec)",LIB,28.0196877,24.5721181
"Coremark - CoreMark Size 666 - Iterations Per Second (Iterations/Sec)",HIB,1445843.521552,1730658.449440
"Stockfish - Total Time (Nodes/s)",HIB,105894457,112958788
"7-Zip Compression - Test: Compression Rating (MIPS)",HIB,271795,278973
"7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,226211,247255
"Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,,33.399
"Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,,333.351
"Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,,34.27
"Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,,89.35
"Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,,45.22
"Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,,351.58
"Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,,112.64
"libavif avifenc - Encoder Speed: 0 (sec)",LIB,78.068,78.350
"libavif avifenc - Encoder Speed: 2 (sec)",LIB,41.538,41.989
"libavif avifenc - Encoder Speed: 6 (sec)",LIB,3.250,3.205
"libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,6.889,7.639
"Timed Gem5 Compilation - Time To Compile (sec)",LIB,176.767,170.930
"Timed Node.js Compilation - Time To Compile (sec)",LIB,198.394,191.706
"nginx - Connections: 500 (Reqs/sec)",HIB,187350.44,162957.75
"nginx - Connections: 1000 (Reqs/sec)",HIB,180537.84,155609.04
"OpenSSL - Algorithm: SHA256 (byte/s)",HIB,46211821313,50884997103
"OpenSSL - Algorithm: SHA512 (byte/s)",HIB,14702270573,22244804183
"OpenSSL - Algorithm: RSA4096 (sign/s)",HIB,20079.5,12973.0
"OpenSSL - Algorithm: RSA4096 (verify/s)",HIB,493077.6,860844.6
"OpenSSL - Algorithm: ChaCha20 (byte/s)",HIB,173980949893,180249145770
"OpenSSL - Algorithm: AES-128-GCM (byte/s)",HIB,343095284440,234604082610
"OpenSSL - Algorithm: AES-256-GCM (byte/s)",HIB,293328048497,216025967640
"OpenSSL - Algorithm: ChaCha20-Poly1305 (byte/s)",HIB,123909304773,119647720337
"Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,33268158,33466804
"Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,418.59,415.08
"Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,34762565,34925899
"Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,623.89,633.58
"Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,34332237,34123810
"Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,447.68,433.96
"Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,35359884,35068557
"Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,682.12,709.46
"Apache Cassandra - Test: Writes (Op/s)",HIB,228640,187169
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only (TPS)",HIB,,2003784
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only (TPS)",HIB,,2008186
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write (TPS)",HIB,,5682
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write (TPS)",HIB,,5793
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,,0.399
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,,0.498
"PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,,140.916
"PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,,172.717