Grafikkarte: unknown, AMD Radeon HD 6250, ARM Mali-450 MP4, ARM Mali-T720 MP2, Intel Graphics Media Accelerator (GMA) 3600, Intel Graphics Media Accelerator (GMA) 3650, Intel Graphics Media Accelerator (GMA) HD Graphics, Intel HD Graphics (Bay Trail), Intel HD Graphics 3000, Intel HD Graphics 4000, NVIDIA GeForce ULP (Tegra 2), NVIDIA GeForce ULP (Tegra 3), PowerVR SGX544, PowerVR SGX544MP2, PowerVR SGX545īildschirm: 7.00 Zoll, 7.90 Zoll, 8.00 Zoll, 8.10 Zoll, 10.10 Zoll, 11.60 Zoll p2pBandwidthLatencyTestĭevice: 0, Graphics Device MIG 1g.10gb, pciBusID: 1, pciDeviceID: 0, pciDomainID:0Acer Iconia Tab Serie Prozessor:, unknown, Mediatek MT6589, Mediatek MT8125, Mediatek MT8317T, Mediatek MT8389, Mediatek MT8735, AMD C-Series C-50, Intel Atom Z2560, Intel Atom Z2760, Intel Atom Z3735F, Intel Atom Z3735G, Intel Atom Z3740, Intel Atom Z3745, Intel Core i3 2365M, Intel Core i3 2375M, Intel Core i5 3317U, Intel Core i5 3337U, Mediatek MT8163 V/A 1.5 GHz, Mediatek MT8163 V/B 1.3 GHz, NVIDIA Tegra 2 (250), NVIDIA Tegra 3 In Docker, enter the MIG UUID from this output, in which GPU 0 andĭevice 0 have been /usr/local/cuda-11.2/samples/bin/x86_64/linux/release$ CUDA_VISIBLE_DEVICES=MIG-GPU-269d95f8-328a-08a7-5985-ab09e6e2b751/7/0.
Results may vary when GPU Boost is nvidia-smi -L NOTE: The CUDA Samples are not meant for performance measurements. Which limits the number of GPUs that the application can /usr/local/cuda-11.2/samples/bin/x86_64/linux/release$ CUDA_VISIBLE_DEVICES=0,1,2,3. Run the example one more time by using the CUDA_VISIBLE_DEVICES variable, This indicates that GPUĤ should not be used for high-performance workloads. The application also shows that there is no peer-to-peerĬonnectivity between any GPU and GPU 4. The example above shows the peer-to-peer bandwidth and latency test across all five GPUs, Results may vary when GPU Boost is enabled. P2P=Enabled Latency (P2P Writes) Matrix (us) Unidirectional P2P=Enabled Bandwidth (P2P Writes) Matrix (GB/s)īidirectional P2P=Disabled Bandwidth Matrix (GB/s)īidirectional P2P=Enabled Bandwidth Matrix (GB/s) Unidirectional P2P=Disabled Bandwidth Matrix (GB/s) So you can see lesser Bandwidth (GB/s) and unstable Latency (us) in those cases. ***NOTE: In case a device doesn't have P2P access to other one, it falls back to normal memcopy procedure. p2pBandwidthLatencyTestĭevice: 0, Graphics Device, pciBusID: 1, pciDeviceID: 0, pciDomainID:0ĭevice: 1, Graphics Device, pciBusID: 47, pciDeviceID: 0, pciDomainID:0ĭevice: 2, Graphics Device, pciBusID: 81, pciDeviceID: 0, pciDomainID:0ĭevice: 3, Graphics Device, pciBusID: c2, pciDeviceID: 0, pciDomainID:0ĭevice: 4, DGX Display, pciBusID: c1, pciDeviceID: 0, pciDomainID:0 gencode arch=compute_86,code=compute_86 -o p2pBandwidthLatencyTest p2pBandwidthLatencyTest.oĬp p2pBandwidthLatencyTest $ cd $. gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 Nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning). gencode arch=compute_86,code=compute_86 -o p2pBandwidthLatencyTest.o -c p2pBandwidthLatencyTest.cu gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52
usr/local/cuda/bin/nvcc -ccbin g++ -I././common/inc -m64 -threadsĠ -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 Output, GPU 0 is the fastest in a DGX Station A100, and GPU In the following example, a CUDA application that comes with CUDA samples is run.
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