GCE c3d-standard-60

amazon 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 2310055-NE-2310039NE76
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Timed Code Compilation 3 Tests
C/C++ Compiler Tests 7 Tests
CPU Massive 12 Tests
Creator Workloads 4 Tests
Database Test Suite 3 Tests
Fortran Tests 4 Tests
HPC - High Performance Computing 10 Tests
Java Tests 2 Tests
Common Kernel Benchmarks 2 Tests
Machine Learning 2 Tests
Molecular Dynamics 3 Tests
MPI Benchmarks 4 Tests
Multi-Core 15 Tests
NVIDIA GPU Compute 3 Tests
OpenMPI Tests 11 Tests
Programmer / Developer System Benchmarks 4 Tests
Python Tests 4 Tests
Scientific Computing 4 Tests
Server 5 Tests
Server CPU Tests 7 Tests
Common Workstation Benchmarks 3 Tests

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  Test
  Duration
c3d-standard-60 AMD Genoa
October 03 2023
  10 Hours, 30 Minutes
t2d-standard-60 AMD Milan
October 03 2023
  13 Hours, 23 Minutes
c6g.16xlarge
October 05 2023
  9 Hours, 27 Minutes
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  11 Hours, 7 Minutes

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GCE c3d-standard-60 amazon testing on Ubuntu 22.04 via the Phoronix Test Suite. ,,"c3d-standard-60 AMD Genoa","t2d-standard-60 AMD Milan","c6g.16xlarge" Processor,,AMD EPYC 9B14 (30 Cores / 60 Threads),AMD EPYC 7B13 (60 Cores),ARMv8 Neoverse-N1 (64 Cores) Motherboard,,Google Compute Engine c3d-standard-60,Google Compute Engine t2d-standard-60,Amazon EC2 c6g.16xlarge (1.0 BIOS) Chipset,,Intel 440FX 82441FX PMC,Intel 440FX 82441FX PMC,Amazon Device 0200 Memory,,240GB,240GB,128GB Disk,,215GB nvme_card-pd,215GB PersistentDisk,215GB Amazon Elastic Block Store Network,,Google Compute Engine Virtual,Red Hat Virtio device,Amazon Elastic OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,6.2.0-1014-gcp (x86_64),6.2.0-1014-gcp (x86_64),5.19.0-1025-aws (aarch64) Vulkan,,1.3.238,1.3.238,1.3.238 Compiler,,GCC 11.4.0,GCC 11.4.0,GCC 11.4.0 File-System,,ext4,ext4,ext4 System Layer,,KVM,KVM,amazon ,,"c3d-standard-60 AMD Genoa","t2d-standard-60 AMD Milan","c6g.16xlarge" "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,271795,278973,239735 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,226211,247255,234046 "Algebraic Multi-Grid Benchmark - (Figure Of Merit)",HIB,962889833,920427767,1032893667 "Apache Cassandra - Test: Writes (Op/s)",HIB,228640,187169,217355 "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, "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, "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,510819,629363, "Coremark - CoreMark Size 666 - Iterations Per Second (Iterations/Sec)",HIB,1445843.521552,1730658.449440,1259870.716902 "GROMACS - Implementation: MPI CPU - Input: water_GMX50_bare (Ns/Day)",HIB,4.391,5.289,2.766 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,88.6301,109.676,129.172 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,148.575,196.948,202.445 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,57.3116,60.0343,32.3575 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,93.6005,106.029,79.0156 "Laghos - Test: Triple Point Problem (Major Kernels Rate)",HIB,209.00,222.30,179.52 "Laghos - Test: Sedov Blast Wave, ube_922_hex.mesh (Major Kernels Rate)",HIB,259.55,364.64,321.29 "LAMMPS Molecular Dynamics Simulator - Model: 20k Atoms (ns/day)",HIB,19.776,26.734,25.059 "LAMMPS Molecular Dynamics Simulator - Model: Rhodopsin Protein (ns/day)",HIB,17.423,27.828,26.041 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,78.068,78.350,270.068 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,41.538,41.989,167.946 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,3.250,3.205,4.467 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,6.889,7.639,8.879 "libxsmm - M N K: 32 (GFLOPS/s)",HIB,255.4,289.2,312.7 "libxsmm - M N K: 64 (GFLOPS/s)",HIB,489.7,554.2,589.5 "NAS Parallel Benchmarks - Test / Class: BT.C (Mop/s)",HIB,96257.48,122720.61,24229.14 "NAS Parallel Benchmarks - Test / Class: CG.C (Mop/s)",HIB,19597.86,16649.37,13343.35 "NAS Parallel Benchmarks - Test / Class: EP.D (Mop/s)",HIB,3783.60,4935.68,2213.76 "NAS Parallel Benchmarks - Test / Class: FT.C (Mop/s)",HIB,39647.47,54846.18,21386.37 "NAS Parallel Benchmarks - Test / Class: IS.D (Mop/s)",HIB,2422.40,1752.62,915.80 "NAS Parallel Benchmarks - Test / Class: LU.C (Mop/s)",HIB,73563.13,94247.77,18807.75 "NAS Parallel Benchmarks - Test / Class: MG.C (Mop/s)",HIB,42701.83,47291.96,25661.04 "NAS Parallel Benchmarks - Test / Class: SP.C (Mop/s)",HIB,39919.71,43228.11,9716.99 "nekRS - Input: Kershaw (flops/rank)",HIB,4289858333,3681935833,1758860000 "nekRS - Input: TurboPipe Periodic (flops/rank)",HIB,4723940000,2730620000,2221710000 "nginx - Connections: 500 (Reqs/sec)",HIB,187350.44,162957.75,162553.85 "nginx - Connections: 1000 (Reqs/sec)",HIB,180537.84,155609.04,158700.36 "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, "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, "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,,, "OpenSSL - Algorithm: SHA256 (byte/s)",HIB,46211821313,50884997103,42288513973 "OpenSSL - Algorithm: SHA512 (byte/s)",HIB,14702270573,22244804183,14384917863 "OpenSSL - Algorithm: RSA4096 (sign/s)",HIB,20079.5,12973.0,2640.0 "OpenSSL - Algorithm: RSA4096 (verify/s)",HIB,493077.6,860844.6,215683.2 "OpenSSL - Algorithm: ChaCha20 (byte/s)",HIB,173980949893,180249145770,67324778360 "OpenSSL - Algorithm: AES-128-GCM (byte/s)",HIB,343095284440,234604082610,158788510970 "OpenSSL - Algorithm: AES-256-GCM (byte/s)",HIB,293328048497,216025967640,129198197600 "OpenSSL - Algorithm: ChaCha20-Poly1305 (byte/s)",HIB,123909304773,119647720337,46715126487 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,18.39,10.73,0.1 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,648.81,1393.56,9996.56 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,142.75,73.74,1.06 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,83.99,208.47,947.59 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,142.90,78.96,1.06 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,83.91,193.45,947.86 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,1389.69,368.39,6.53 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,8.62,40.67,153.12 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,35.19,26.28,0.04 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,340.29,568.77,22391.86 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,4166.39,1285.47,20.79 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,2.87,11.65,48.08 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,576.94,225.48,2.61 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,20.77,66.46,382.47 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,2043.05,1512.57,0.14 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,5.86,9.90,6990.10 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,1875.28,1014.70,8.39 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,15.98,14.76,119.17 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,6605.12,4239.52,0.46 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,4.53,3.52,2186.81 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,645.74,565.34,0.15 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,18.56,26.50,6773.31 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,185.29,96.58,1.36 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,64.70,155.14,735.58 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,3650.57,2646.58,5.50 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,8.20,11.32,181.94 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,1764.21,633.76,7.36 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,6.79,23.64,135.87 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,964.46,370.03,2.53 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,31.08,81.00,394.94 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,43607.04,29668.12,178.82 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,0.52,0.99,5.58 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,761.14,390.72,2.36 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,39.38,76.72,423.95 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,54971.26,44049.15,136.16 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.4,0.61,7.33 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only (TPS)",HIB,,2003784,1043267 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only (TPS)",HIB,,2008186,975031 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write (TPS)",HIB,,5682,4784 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write (TPS)",HIB,,5793,4776 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,,0.399,0.767 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,,0.498,1.026 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,,140.916,168.191 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,,172.717,210.608 "Remhos - Test: Sample Remap Example (sec)",LIB,33.362,16.326,20.816 "Rodinia - Test: OpenMP LavaMD (sec)",LIB,64.862,50.974,62.301 "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,10.025,7.368,5.983 "Rodinia - Test: OpenMP Streamcluster (sec)",LIB,6.448,6.423,14.212 "Stockfish - Total Time (Nodes/s)",HIB,105894457,112958788,81807706 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,50.99,18.29, "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,62.74,20.36, "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,69.68,20.90, "Timed Gem5 Compilation - Time To Compile (sec)",LIB,176.767,170.930,224.414 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,,33.399,102.216 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,,333.351,409.097 "Timed Node.js Compilation - Time To Compile (sec)",LIB,198.394,191.706,286.201 "Xcompact3d Incompact3d - Input: input.i3d 129 Cells Per Direction (sec)",LIB,5.87157885,5.63057327,5.61811686 "Xcompact3d Incompact3d - Input: input.i3d 193 Cells Per Direction (sec)",LIB,28.0196877,24.5721181,25.8748328