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 2310065-NE-2310055NE35
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Timed Code Compilation 3 Tests
C/C++ Compiler Tests 7 Tests
CPU Massive 12 Tests
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HPC - High Performance Computing 10 Tests
Java Tests 2 Tests
Common Kernel Benchmarks 2 Tests
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MPI Benchmarks 4 Tests
Multi-Core 15 Tests
NVIDIA GPU Compute 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
m7a.16xlarge
October 06 2023
  9 Hours, 1 Minute
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  10 Hours, 35 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","m7a.16xlarge" Processor,,AMD EPYC 9B14 (30 Cores / 60 Threads),AMD EPYC 7B13 (60 Cores),ARMv8 Neoverse-N1 (64 Cores),AMD EPYC 9R14 (64 Cores) Motherboard,,Google Compute Engine c3d-standard-60,Google Compute Engine t2d-standard-60,Amazon EC2 c6g.16xlarge (1.0 BIOS),Amazon EC2 m7a.16xlarge (1.0 BIOS) Chipset,,Intel 440FX 82441FX PMC,Intel 440FX 82441FX PMC,Amazon Device 0200,Intel 440FX 82441FX PMC Memory,,240GB,240GB,128GB,256GB Disk,,215GB nvme_card-pd,215GB PersistentDisk,215GB Amazon Elastic Block Store,215GB Amazon Elastic Block Store Network,,Google Compute Engine Virtual,Red Hat Virtio device,Amazon Elastic,Amazon Elastic OS,,Ubuntu 22.04,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),5.19.0-1025-aws (x86_64) Vulkan,,1.3.238,1.3.238,1.3.238,1.3.238 Compiler,,GCC 11.4.0,GCC 11.4.0,GCC 11.4.0,GCC 11.4.0 File-System,,ext4,ext4,ext4,ext4 System Layer,,KVM,KVM,amazon,amazon ,,"c3d-standard-60 AMD Genoa","t2d-standard-60 AMD Milan","c6g.16xlarge","m7a.16xlarge" "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency (ms)",LIB,,140.916,168.191,150.601 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,682.12,709.46,,598.49 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,35359884,35068557,,44699315 "LAMMPS Molecular Dynamics Simulator - Model: 20k Atoms (ns/day)",HIB,19.776,26.734,25.059,31.471 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Write (TPS)",HIB,,5682,4784,5312 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency (ms)",LIB,,172.717,210.608,188.676 "OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,337.70,327.88,,190.79 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,,351.58,,276.23 "nekRS - Input: Kershaw (flops/rank)",HIB,4289858333,3681935833,1758860000,7667846667 "Rodinia - Test: OpenMP HotSpot3D (sec)",LIB,84.166,88.535,,74.778 "nekRS - Input: TurboPipe Periodic (flops/rank)",HIB,4723940000,2730620000,2221710000,4774796667 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,,333.351,409.097,267.965 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,447.68,433.96,,355.13 "Apache IoTDB - Device Count: 800 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,34332237,34123810,,42643903 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Write (TPS)",HIB,,5793,4776,5300 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (point/sec)",HIB,34762565,34925899,,44502333 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 800 - Client Number: 400 (Latency)",LIB,623.89,633.58,,521.98 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,147.31,123.61,,115.96 "Apache Cassandra - Test: Writes (Op/s)",HIB,228640,187169,217355,278585 "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,510819,629363,,788704 "Timed Node.js Compilation - Time To Compile (sec)",LIB,198.394,191.706,286.201,154.441 "OpenRadioss - Model: Bumper Beam (sec)",LIB,92.87,75.68,,66.27 "Stockfish - Total Time (Nodes/s)",HIB,105894457,112958788,81807706,135419169 "Timed Gem5 Compilation - Time To Compile (sec)",LIB,176.767,170.930,224.414,153.800 "OpenSSL - Algorithm: AES-256-GCM (byte/s)",HIB,293328048497,216025967640,129198197600,522113080527 "OpenSSL - Algorithm: ChaCha20 (byte/s)",HIB,173980949893,180249145770,67324778360,308308045083 "OpenSSL - Algorithm: AES-128-GCM (byte/s)",HIB,343095284440,234604082610,158788510970,592545362740 "OpenSSL - Algorithm: ChaCha20-Poly1305 (byte/s)",HIB,123909304773,119647720337,46715126487,216773475533 "OpenSSL - Algorithm: SHA512 (byte/s)",HIB,14702270573,22244804183,14384917863,26481506820 "OpenSSL - Algorithm: SHA256 (byte/s)",HIB,46211821313,50884997103,42288513973,62253861197 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (Latency)",LIB,418.59,415.08,,340.11 "Apache IoTDB - Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 400 (point/sec)",HIB,33268158,33466804,,40899210 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,89.65,72.06,,59.18 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,38.82,30.12,,26.14 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency (ms)",LIB,,0.399,0.767,0.274 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency (ms)",LIB,,0.498,1.026,0.347 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,5.86,9.90,6990.10,4.35 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,2043.05,1512.57,0.14,3666.89 "TensorFlow - Device: CPU - Batch Size: 64 - Model: ResNet-50 (images/sec)",HIB,69.68,20.90,,100.15 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,83.99,208.47,947.59,56.21 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,142.75,73.74,1.06,284.42 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,83.91,193.45,947.86,56.41 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,142.90,78.96,1.06,283.40 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,78.068,78.350,270.068,65.447 "PostgreSQL - Scaling Factor: 100 - Clients: 800 - Mode: Read Only (TPS)",HIB,,2003784,1043267,2923009 "PostgreSQL - Scaling Factor: 100 - Clients: 1000 - Mode: Read Only (TPS)",HIB,,2008186,975031,2880940 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,,112.64,,91.88 "Laghos - Test: Sedov Blast Wave, ube_922_hex.mesh (Major Kernels Rate)",HIB,259.55,364.64,321.29,409.73 "nginx - Connections: 1000 (Reqs/sec)",HIB,180537.84,155609.04,158700.36,224859.09 "nginx - Connections: 500 (Reqs/sec)",HIB,187350.44,162957.75,162553.85,233014.72 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,,89.35,,71.51 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,340.29,568.77,22391.86,261.11 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,35.19,26.28,0.04,61.19 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,41.538,41.989,167.946,35.805 "TensorFlow - Device: CPU - Batch Size: 32 - Model: ResNet-50 (images/sec)",HIB,62.74,20.36,,87.19 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,648.81,1393.56,9996.56,503.38 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,18.39,10.73,0.1,31.72 "NAS Parallel Benchmarks - Test / Class: IS.D (Mop/s)",HIB,2422.40,1752.62,915.80,4085.20 "NAS Parallel Benchmarks - Test / Class: LU.C (Mop/s)",HIB,73563.13,94247.77,18807.75,210544.87 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,18.56,26.50,6773.31,13.07 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,645.74,565.34,0.15,1222.99 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,4.53,3.52,2186.81,3.07 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,6605.12,4239.52,0.46,10382.22 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,64.70,155.14,735.58,50.72 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,185.29,96.58,1.36,315.14 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,6.79,23.64,135.87,5.07 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,1764.21,633.76,7.36,3146.04 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,20.77,66.46,382.47,15.22 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,576.94,225.48,2.61,1049.66 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,39.38,76.72,423.95,27.60 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,761.14,390.72,2.36,1158.43 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,31.08,81.00,394.94,22.53 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,964.46,370.03,2.53,1419.11 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,8.62,40.67,153.12,6.60 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,1389.69,368.39,6.53,2417.34 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,8.20,11.32,181.94,5.16 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,3650.57,2646.58,5.50,6177.94 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.4,0.61,7.33,0.27 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,54971.26,44049.15,136.16,92100.80 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,15.98,14.76,119.17,10.19 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,1875.28,1014.70,8.39,3132.48 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,2.87,11.65,48.08,2.21 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,4166.39,1285.47,20.79,7222.10 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,0.52,0.99,5.58,0.38 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,43607.04,29668.12,178.82,81996.81 "OpenSSL - Algorithm: RSA4096 (verify/s)",HIB,493077.6,860844.6,215683.2,996017.5 "OpenSSL - Algorithm: RSA4096 (sign/s)",HIB,20079.5,12973.0,2640.0,31583.8 "NAS Parallel Benchmarks - Test / Class: SP.C (Mop/s)",HIB,39919.71,43228.11,9716.99,102392.40 "Rodinia - Test: OpenMP LavaMD (sec)",LIB,64.862,50.974,62.301,43.286 "Laghos - Test: Triple Point Problem (Major Kernels Rate)",HIB,209.00,222.30,179.52,218.86 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,,33.399,102.216,27.709 "GROMACS - Implementation: MPI CPU - Input: water_GMX50_bare (Ns/Day)",HIB,4.391,5.289,2.766,7.655 "NAS Parallel Benchmarks - Test / Class: BT.C (Mop/s)",HIB,96257.48,122720.61,24229.14,193219.12 "Xcompact3d Incompact3d - Input: input.i3d 193 Cells Per Direction (sec)",LIB,28.0196877,24.5721181,25.8748328,11.5913086 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,50.99,18.29,,69.55 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,,45.22,,37.12 "NAS Parallel Benchmarks - Test / Class: EP.D (Mop/s)",HIB,3783.60,4935.68,2213.76,7501.76 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,,34.27,,27.74 "Rodinia - Test: OpenMP Leukocyte (sec)",LIB,45.498,42.010,,34.661 "Algebraic Multi-Grid Benchmark - (Figure Of Merit)",HIB,962889833,920427767,1032893667,1843444333 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,226211,247255,234046,282593 "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,271795,278973,239735,330633 "NAS Parallel Benchmarks - Test / Class: FT.C (Mop/s)",HIB,39647.47,54846.18,21386.37,103413.02 "Remhos - Test: Sample Remap Example (sec)",LIB,33.362,16.326,20.816,13.867 "Coremark - CoreMark Size 666 - Iterations Per Second (Iterations/Sec)",HIB,1445843.521552,1730658.449440,1259870.716902,2158639.274883 "libxsmm - M N K: 32 (GFLOPS/s)",HIB,255.4,289.2,312.7,643.4 "NAS Parallel Benchmarks - Test / Class: CG.C (Mop/s)",HIB,19597.86,16649.37,13343.35,42007.57 "libxsmm - M N K: 64 (GFLOPS/s)",HIB,489.7,554.2,589.5,1201.8 "Rodinia - Test: OpenMP Streamcluster (sec)",LIB,6.448,6.423,14.212,5.930 "Rodinia - Test: OpenMP CFD Solver (sec)",LIB,10.025,7.368,5.983,6.480 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,6.889,7.639,8.879,5.678 "Xcompact3d Incompact3d - Input: input.i3d 129 Cells Per Direction (sec)",LIB,5.87157885,5.63057327,5.61811686,2.89602661 "NAS Parallel Benchmarks - Test / Class: MG.C (Mop/s)",HIB,42701.83,47291.96,25661.04,121293.80 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,3.250,3.205,4.467,2.649 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,57.3116,60.0343,32.3575,71.1095 "LAMMPS Molecular Dynamics Simulator - Model: Rhodopsin Protein (ns/day)",HIB,17.423,27.828,26.041,32.785 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 (GFLOP/s)",HIB,93.6005,106.029,79.0156,124.363 "HeFFTe - Highly Efficient FFT for Exascale - Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,148.575,196.948,202.445,190.602 "HeFFTe - Highly Efficient FFT for Exascale - Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 (GFLOP/s)",HIB,88.6301,109.676,129.172,121.505 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,,,,