AMD Ryzen 9 3950X + GeForce RTX 3080 AMD Ryzen 9 3950X 16-Core testing with a ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1302 BIOS) and NVIDIA GeForce RTX 3080 10GB on Ubuntu 20.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2010077-PTS-AMDRYZEN73 .
AMD Ryzen 9 3950X + GeForce RTX 3080 Processor Motherboard Chipset Memory Disk Graphics Audio Monitor Network OS Kernel Desktop Display Server Display Driver OpenGL OpenCL Vulkan Compiler File-System Screen Resolution GeForce RTX 3080 AMD Ryzen 9 3950X 16-Core @ 3.50GHz (16 Cores / 32 Threads) ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1302 BIOS) AMD Starship/Matisse 16GB 2000GB Corsair Force MP600 + 2000GB NVIDIA GeForce RTX 3080 10GB (1710/9501MHz) NVIDIA Device 1aef DELL P2415Q Realtek RTL8125 2.5GbE + Intel I211 + Intel Wi-Fi 6 AX200 Ubuntu 20.04 5.4.0-48-generic (x86_64) GNOME Shell 3.36.4 X Server 1.20.8 NVIDIA 455.23.05 4.6.0 OpenCL 1.2 CUDA 11.1.70 1.2.142 GCC 9.3.0 + CUDA 11.1 ext4 3840x2160 OpenBenchmarking.org - --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none,hsa --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: acpi-cpufreq performance - CPU Microcode: 0x8701013 - GPU Compute Cores: 8704 - Python 3.8.2 - itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full AMD retpoline IBPB: conditional STIBP: conditional RSB filling + srbds: Not affected + tsx_async_abort: Not affected
AMD Ryzen 9 3950X + GeForce RTX 3080 vkfft: financebench: Black-Scholes OpenCL mixbench: OpenCL - Integer mixbench: NVIDIA CUDA - Integer mixbench: OpenCL - Double Precision mixbench: OpenCL - Single Precision mixbench: NVIDIA CUDA - Half Precision mixbench: NVIDIA CUDA - Double Precision mixbench: NVIDIA CUDA - Single Precision gromacs-gpu: Water Benchmark viennacl: OpenCL LU Factorization cl-mem: Copy cl-mem: Read cl-mem: Write namd-cuda: ATPase Simulation - 327,506 Atoms octanebench: Total Score redshift: luxcorerender-cl: DLSC luxcorerender-cl: Food luxcorerender-cl: LuxCore Benchmark luxcorerender-cl: Rainbow Colors and Prism fahbench: lczero: OpenCL rodinia: OpenCL Particle Filter arrayfire: Conjugate Gradient OpenCL caffe: AlexNet - NVIDIA CUDA - 100 caffe: AlexNet - NVIDIA CUDA - 200 caffe: AlexNet - NVIDIA CUDA - 1000 caffe: GoogleNet - NVIDIA CUDA - 100 caffe: GoogleNet - NVIDIA CUDA - 200 caffe: GoogleNet - NVIDIA CUDA - 1000 plaidml: No - Inference - VGG16 - OpenCL plaidml: No - Inference - VGG19 - OpenCL plaidml: No - Training - Mobilenet - OpenCL plaidml: No - Inference - IMDB LSTM - OpenCL plaidml: No - Inference - Mobilenet - OpenCL plaidml: No - Inference - ResNet 50 - OpenCL plaidml: Yes - Inference - Mobilenet - OpenCL plaidml: No - Inference - DenseNet 201 - OpenCL plaidml: No - Inference - Inception V3 - OpenCL plaidml: No - Inference - NASNer Large - OpenCL blender: BMW27 - CUDA blender: Classroom - CUDA blender: Fishy Cat - CUDA blender: Barbershop - CUDA blender: BMW27 - NVIDIA OptiX blender: Classroom - NVIDIA OptiX blender: Fishy Cat - NVIDIA OptiX blender: Barbershop - NVIDIA OptiX blender: Pabellon Barcelona - CUDA blender: Pabellon Barcelona - NVIDIA OptiX mandelgpu: GPU clpeak: Integer Compute INT clpeak: Single-Precision Float clpeak: Double-Precision Double clpeak: Global Memory Bandwidth GeForce RTX 3080 51300 4.833 14598.78 14054.59 452.60 32208.54 32025.09 416.25 29033.22 8.333 76.0948 355.5 673.1 648.1 0.17074 563.903165 166 9.91 4.02 8.00 21.81 317.5070 29307 4.303 1.564 790.029 1560.75 7717.03 2468.99 4902.08 24479.6 279.68 223.43 241.61 1012.67 3047.67 726.38 3536.94 259.43 398.11 79.54 25.93 68.16 47.13 286.16 11.93 37.88 21.95 417.36 175.88 54.74 419973775.9 15440.89 29199.72 541.03 662.35 OpenBenchmarking.org
VkFFT OpenBenchmarking.org Benchmark Score, More Is Better VkFFT 2020-09-29 GeForce RTX 3080 11K 22K 33K 44K 55K SE +/- 172.40, N = 3 51300
FinanceBench Benchmark: Black-Scholes OpenCL OpenBenchmarking.org ms, Fewer Is Better FinanceBench 2016-06-06 Benchmark: Black-Scholes OpenCL GeForce RTX 3080 1.0874 2.1748 3.2622 4.3496 5.437 SE +/- 0.002, N = 3 4.833 1. (CXX) g++ options: -O3 -lOpenCL
Mixbench Backend: OpenCL - Benchmark: Integer OpenBenchmarking.org GIOPS, More Is Better Mixbench 2020-06-23 Backend: OpenCL - Benchmark: Integer GeForce RTX 3080 3K 6K 9K 12K 15K SE +/- 126.18, N = 3 14598.78 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: NVIDIA CUDA - Benchmark: Integer OpenBenchmarking.org GIOPS, More Is Better Mixbench 2020-06-23 Backend: NVIDIA CUDA - Benchmark: Integer GeForce RTX 3080 3K 6K 9K 12K 15K SE +/- 272.77, N = 15 14054.59 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: OpenCL - Benchmark: Double Precision OpenBenchmarking.org GFLOPS, More Is Better Mixbench 2020-06-23 Backend: OpenCL - Benchmark: Double Precision GeForce RTX 3080 100 200 300 400 500 SE +/- 0.07, N = 3 452.60 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: OpenCL - Benchmark: Single Precision OpenBenchmarking.org GFLOPS, More Is Better Mixbench 2020-06-23 Backend: OpenCL - Benchmark: Single Precision GeForce RTX 3080 7K 14K 21K 28K 35K SE +/- 359.89, N = 12 32208.54 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: NVIDIA CUDA - Benchmark: Half Precision OpenBenchmarking.org GFLOPS, More Is Better Mixbench 2020-06-23 Backend: NVIDIA CUDA - Benchmark: Half Precision GeForce RTX 3080 7K 14K 21K 28K 35K SE +/- 606.80, N = 15 32025.09 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: NVIDIA CUDA - Benchmark: Double Precision OpenBenchmarking.org GFLOPS, More Is Better Mixbench 2020-06-23 Backend: NVIDIA CUDA - Benchmark: Double Precision GeForce RTX 3080 90 180 270 360 450 SE +/- 8.23, N = 15 416.25 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
Mixbench Backend: NVIDIA CUDA - Benchmark: Single Precision OpenBenchmarking.org GFLOPS, More Is Better Mixbench 2020-06-23 Backend: NVIDIA CUDA - Benchmark: Single Precision GeForce RTX 3080 6K 12K 18K 24K 30K SE +/- 513.16, N = 15 29033.22 1. (CXX) g++ options: -lm -lstdc++ -lOpenCL -lrt -O2
GROMACS Water Benchmark OpenBenchmarking.org Ns Per Day, More Is Better GROMACS 2020.3 Water Benchmark GeForce RTX 3080 2 4 6 8 10 SE +/- 0.022, N = 3 8.333 1. (CXX) g++ options: -O3 -lpthread -ldl -lrt -lm
ViennaCL OpenCL LU Factorization OpenBenchmarking.org GFLOPS, More Is Better ViennaCL 1.4.2 OpenCL LU Factorization GeForce RTX 3080 20 40 60 80 100 SE +/- 0.64, N = 3 76.09 1. (CXX) g++ options: -rdynamic -lOpenCL
cl-mem Benchmark: Copy OpenBenchmarking.org GB/s, More Is Better cl-mem 2017-01-13 Benchmark: Copy GeForce RTX 3080 80 160 240 320 400 SE +/- 0.17, N = 3 355.5 1. (CC) gcc options: -O2 -flto -lOpenCL
cl-mem Benchmark: Read OpenBenchmarking.org GB/s, More Is Better cl-mem 2017-01-13 Benchmark: Read GeForce RTX 3080 150 300 450 600 750 SE +/- 0.46, N = 3 673.1 1. (CC) gcc options: -O2 -flto -lOpenCL
cl-mem Benchmark: Write OpenBenchmarking.org GB/s, More Is Better cl-mem 2017-01-13 Benchmark: Write GeForce RTX 3080 140 280 420 560 700 SE +/- 0.22, N = 3 648.1 1. (CC) gcc options: -O2 -flto -lOpenCL
NAMD CUDA ATPase Simulation - 327,506 Atoms OpenBenchmarking.org days/ns, Fewer Is Better NAMD CUDA 2.14 ATPase Simulation - 327,506 Atoms GeForce RTX 3080 0.0384 0.0768 0.1152 0.1536 0.192 SE +/- 0.00020, N = 3 0.17074
OctaneBench Total Score OpenBenchmarking.org Score, More Is Better OctaneBench 2020.1 Total Score GeForce RTX 3080 120 240 360 480 600 563.90
RedShift Demo OpenBenchmarking.org Seconds, Fewer Is Better RedShift Demo 3.0 GeForce RTX 3080 40 80 120 160 200 166
LuxCoreRender OpenCL Scene: DLSC OpenBenchmarking.org M samples/sec, More Is Better LuxCoreRender OpenCL 2.3 Scene: DLSC GeForce RTX 3080 3 6 9 12 15 SE +/- 0.01, N = 3 9.91 MIN: 9.73 / MAX: 10.03
LuxCoreRender OpenCL Scene: Food OpenBenchmarking.org M samples/sec, More Is Better LuxCoreRender OpenCL 2.3 Scene: Food GeForce RTX 3080 0.9045 1.809 2.7135 3.618 4.5225 SE +/- 0.02, N = 3 4.02 MIN: 0.32 / MAX: 4.93
LuxCoreRender OpenCL Scene: LuxCore Benchmark OpenBenchmarking.org M samples/sec, More Is Better LuxCoreRender OpenCL 2.3 Scene: LuxCore Benchmark GeForce RTX 3080 2 4 6 8 10 SE +/- 0.07, N = 3 8.00 MIN: 0.27 / MAX: 9.09
LuxCoreRender OpenCL Scene: Rainbow Colors and Prism OpenBenchmarking.org M samples/sec, More Is Better LuxCoreRender OpenCL 2.3 Scene: Rainbow Colors and Prism GeForce RTX 3080 5 10 15 20 25 SE +/- 0.24, N = 3 21.81 MIN: 20.2 / MAX: 23.23
FAHBench OpenBenchmarking.org Ns Per Day, More Is Better FAHBench 2.3.2 GeForce RTX 3080 70 140 210 280 350 SE +/- 0.27, N = 3 317.51
LeelaChessZero Backend: OpenCL OpenBenchmarking.org Nodes Per Second, More Is Better LeelaChessZero 0.26 Backend: OpenCL GeForce RTX 3080 6K 12K 18K 24K 30K SE +/- 227.90, N = 3 29307 1. (CXX) g++ options: -flto -pthread
Rodinia Test: OpenCL Particle Filter OpenBenchmarking.org Seconds, Fewer Is Better Rodinia 3.1 Test: OpenCL Particle Filter GeForce RTX 3080 0.9682 1.9364 2.9046 3.8728 4.841 SE +/- 0.017, N = 3 4.303 1. (CXX) g++ options: -m64 -lm -lcuda -lcudart -lcudadevrt -lcudart_static -lrt -lpthread -ldl
ArrayFire Test: Conjugate Gradient OpenCL OpenBenchmarking.org ms, Fewer Is Better ArrayFire 3.7 Test: Conjugate Gradient OpenCL GeForce RTX 3080 0.3519 0.7038 1.0557 1.4076 1.7595 SE +/- 0.004, N = 3 1.564 1. (CXX) g++ options: -rdynamic
Caffe Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 100 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 100 GeForce RTX 3080 200 400 600 800 1000 SE +/- 4.71, N = 3 790.03 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 200 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 200 GeForce RTX 3080 300 600 900 1200 1500 SE +/- 1.58, N = 3 1560.75 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 1000 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: AlexNet - Acceleration: NVIDIA CUDA - Iterations: 1000 GeForce RTX 3080 1700 3400 5100 6800 8500 SE +/- 10.46, N = 3 7717.03 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 100 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 100 GeForce RTX 3080 500 1000 1500 2000 2500 SE +/- 3.32, N = 3 2468.99 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 200 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 200 GeForce RTX 3080 1100 2200 3300 4400 5500 SE +/- 5.73, N = 3 4902.08 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
Caffe Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 1000 OpenBenchmarking.org Milli-Seconds, Fewer Is Better Caffe 2020-02-13 Model: GoogleNet - Acceleration: NVIDIA CUDA - Iterations: 1000 GeForce RTX 3080 5K 10K 15K 20K 25K SE +/- 25.28, N = 3 24479.6 1. (CXX) g++ options: -fPIC -O3 -rdynamic -lglog -lgflags -lprotobuf -lpthread -lsz -lz -ldl -lm -llmdb -lopenblas
PlaidML FP16: No - Mode: Inference - Network: VGG16 - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: VGG16 - Device: OpenCL GeForce RTX 3080 60 120 180 240 300 SE +/- 0.15, N = 3 279.68
PlaidML FP16: No - Mode: Inference - Network: VGG19 - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: VGG19 - Device: OpenCL GeForce RTX 3080 50 100 150 200 250 SE +/- 0.16, N = 3 223.43
PlaidML FP16: No - Mode: Training - Network: Mobilenet - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Training - Network: Mobilenet - Device: OpenCL GeForce RTX 3080 50 100 150 200 250 SE +/- 0.19, N = 3 241.61
PlaidML FP16: No - Mode: Inference - Network: IMDB LSTM - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: IMDB LSTM - Device: OpenCL GeForce RTX 3080 200 400 600 800 1000 SE +/- 1.97, N = 3 1012.67
PlaidML FP16: No - Mode: Inference - Network: Mobilenet - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: Mobilenet - Device: OpenCL GeForce RTX 3080 700 1400 2100 2800 3500 SE +/- 6.69, N = 3 3047.67
PlaidML FP16: No - Mode: Inference - Network: ResNet 50 - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: ResNet 50 - Device: OpenCL GeForce RTX 3080 160 320 480 640 800 SE +/- 0.92, N = 3 726.38
PlaidML FP16: Yes - Mode: Inference - Network: Mobilenet - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: Yes - Mode: Inference - Network: Mobilenet - Device: OpenCL GeForce RTX 3080 800 1600 2400 3200 4000 SE +/- 4.18, N = 3 3536.94
PlaidML FP16: No - Mode: Inference - Network: DenseNet 201 - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: DenseNet 201 - Device: OpenCL GeForce RTX 3080 60 120 180 240 300 SE +/- 0.26, N = 3 259.43
PlaidML FP16: No - Mode: Inference - Network: Inception V3 - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: Inception V3 - Device: OpenCL GeForce RTX 3080 90 180 270 360 450 SE +/- 0.31, N = 3 398.11
PlaidML FP16: No - Mode: Inference - Network: NASNer Large - Device: OpenCL OpenBenchmarking.org FPS, More Is Better PlaidML FP16: No - Mode: Inference - Network: NASNer Large - Device: OpenCL GeForce RTX 3080 20 40 60 80 100 SE +/- 0.12, N = 3 79.54
Blender Blend File: BMW27 - Compute: CUDA OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: BMW27 - Compute: CUDA GeForce RTX 3080 6 12 18 24 30 SE +/- 0.02, N = 3 25.93
Blender Blend File: Classroom - Compute: CUDA OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Classroom - Compute: CUDA GeForce RTX 3080 15 30 45 60 75 SE +/- 0.04, N = 3 68.16
Blender Blend File: Fishy Cat - Compute: CUDA OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Fishy Cat - Compute: CUDA GeForce RTX 3080 11 22 33 44 55 SE +/- 0.01, N = 3 47.13
Blender Blend File: Barbershop - Compute: CUDA OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Barbershop - Compute: CUDA GeForce RTX 3080 60 120 180 240 300 SE +/- 0.28, N = 3 286.16
Blender Blend File: BMW27 - Compute: NVIDIA OptiX OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: BMW27 - Compute: NVIDIA OptiX GeForce RTX 3080 3 6 9 12 15 SE +/- 0.04, N = 3 11.93
Blender Blend File: Classroom - Compute: NVIDIA OptiX OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Classroom - Compute: NVIDIA OptiX GeForce RTX 3080 9 18 27 36 45 SE +/- 0.02, N = 3 37.88
Blender Blend File: Fishy Cat - Compute: NVIDIA OptiX OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Fishy Cat - Compute: NVIDIA OptiX GeForce RTX 3080 5 10 15 20 25 SE +/- 0.00, N = 3 21.95
Blender Blend File: Barbershop - Compute: NVIDIA OptiX OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Barbershop - Compute: NVIDIA OptiX GeForce RTX 3080 90 180 270 360 450 SE +/- 3.39, N = 3 417.36
Blender Blend File: Pabellon Barcelona - Compute: CUDA OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Pabellon Barcelona - Compute: CUDA GeForce RTX 3080 40 80 120 160 200 SE +/- 0.03, N = 3 175.88
Blender Blend File: Pabellon Barcelona - Compute: NVIDIA OptiX OpenBenchmarking.org Seconds, Fewer Is Better Blender 2.90 Blend File: Pabellon Barcelona - Compute: NVIDIA OptiX GeForce RTX 3080 12 24 36 48 60 SE +/- 0.04, N = 3 54.74
MandelGPU OpenCL Device: GPU OpenBenchmarking.org Samples/sec, More Is Better MandelGPU 1.3pts1 OpenCL Device: GPU GeForce RTX 3080 90M 180M 270M 360M 450M SE +/- 2307804.65, N = 3 419973775.9 1. (CC) gcc options: -O3 -lm -ftree-vectorize -funroll-loops -lglut -lOpenCL -lGL
clpeak OpenCL Test: Integer Compute INT OpenBenchmarking.org GIOPS, More Is Better clpeak OpenCL Test: Integer Compute INT GeForce RTX 3080 3K 6K 9K 12K 15K SE +/- 249.34, N = 3 15440.89 1. (CXX) g++ options: -O3 -rdynamic -lOpenCL
clpeak OpenCL Test: Single-Precision Float OpenBenchmarking.org GFLOPS, More Is Better clpeak OpenCL Test: Single-Precision Float GeForce RTX 3080 6K 12K 18K 24K 30K SE +/- 61.87, N = 3 29199.72 1. (CXX) g++ options: -O3 -rdynamic -lOpenCL
clpeak OpenCL Test: Double-Precision Double OpenBenchmarking.org GFLOPS, More Is Better clpeak OpenCL Test: Double-Precision Double GeForce RTX 3080 120 240 360 480 600 SE +/- 0.09, N = 3 541.03 1. (CXX) g++ options: -O3 -rdynamic -lOpenCL
clpeak OpenCL Test: Global Memory Bandwidth OpenBenchmarking.org GBPS, More Is Better clpeak OpenCL Test: Global Memory Bandwidth GeForce RTX 3080 140 280 420 560 700 SE +/- 0.05, N = 3 662.35 1. (CXX) g++ options: -O3 -rdynamic -lOpenCL
Phoronix Test Suite v10.8.4