{"id":7866,"date":"2021-11-27T04:17:57","date_gmt":"2021-11-26T19:17:57","guid":{"rendered":"https:\/\/wp.study3.biz\/?p=7866"},"modified":"2025-12-13T18:29:20","modified_gmt":"2025-12-13T09:29:20","slug":"amd-epyc-7763-64-core-processor-x2-256gb-almalinux-release-8-4-titan-rtx-x2-cuda-11-3-samples%e3%81%a7simplep2p-p2pbandwidthlatencytest-bandwidthtest-devicequery%e3%82%92%e8%a1%a8%e7%a4%ba%e3%81%95","status":"publish","type":"post","link":"https:\/\/wp.study3.biz\/?p=7866","title":{"rendered":"\u7b2c3\u4e16\u4ee3 AMD EPYC 7763 64-Core Processor x2 256GB AlmaLinux release 8.4 TITAN RTX x2 CUDA 11.3 Samples\u3067simpleP2P p2pBandwidthLatencyTest bandwidthTest deviceQuery\u3092\u8868\u793a\u3055\u305b\u3066\u307f\u305f"},"content":{"rendered":"

\"\"
\n[chibi@alma8 simpleP2P]$ .\/simpleP2P
\n[.\/simpleP2P] – Starting…
\nChecking for multiple GPUs…
\nCUDA-capable device count: 2<\/p>\n

Checking GPU(s) for support of peer to peer memory access…
\n> Peer access from NVIDIA TITAN RTX (GPU0) -> NVIDIA TITAN RTX (GPU1) : Yes
\n> Peer access from NVIDIA TITAN RTX (GPU1) -> NVIDIA TITAN RTX (GPU0) : Yes
\nEnabling peer access between GPU0 and GPU1…
\nAllocating buffers (64MB on GPU0, GPU1 and CPU Host)…
\nCreating event handles…
\ncudaMemcpyPeer \/ cudaMemcpy between GPU0 and GPU1: 43.53GB\/s<\/strong>
\nPreparing host buffer and memcpy to GPU0…
\nRun kernel on GPU1, taking source data from GPU0 and writing to GPU1…
\nRun kernel on GPU0, taking source data from GPU1 and writing to GPU0…
\nCopy data back to host from GPU0 and verify results…
\nDisabling peer access…
\nShutting down…
\nTest passed
\n[chibi@alma8 simpleP2P]$
\n[chibi@alma8 p2pBandwidthLatencyTest]$ .\/p2pBandwidthLatencyTest
\n[P2P (Peer-to-Peer) GPU Bandwidth Latency Test]
\nDevice: 0, NVIDIA TITAN RTX, pciBusID: 1, pciDeviceID: 0, pciDomainID:0
\nDevice: 1, NVIDIA TITAN RTX, pciBusID: 41, pciDeviceID: 0, pciDomainID:0
\nDevice=0 CAN Access Peer Device=1
\nDevice=1 CAN Access Peer Device=0<\/p>\n

***NOTE: In case a device doesn’t have P2P access to other one, it falls back to normal memcopy procedure.
\nSo you can see lesser Bandwidth (GB\/s) and unstable Latency (us) in those cases.<\/p>\n

P2P Connectivity Matrix
\n D\\D 0 1
\n 0 1 1
\n 1 1 1
\nUnidirectional P2P=Disabled Bandwidth Matrix (GB\/s)
\n D\\D 0 1
\n 0 564.63 6.01
\n 1 6.03 562.86
\nUnidirectional P2P=Enabled Bandwidth (P2P Writes) Matrix (GB\/s)
\n D\\D 0 1
\n 0 541.78 47.10<\/strong>
\n 1 47.10<\/strong>
\n 562.56
\nBidirectional P2P=Disabled Bandwidth Matrix (GB\/s)
\n D\\D 0 1
\n 0 549.79 8.91
\n 1 8.87 552.93
\nBidirectional P2P=Enabled Bandwidth Matrix (GB\/s)
\n D\\D 0 1
\n 0 552.95 94.13<\/strong>
\n 1 94.11<\/strong> 551.51
\nP2P=Disabled Latency Matrix (us)
\n GPU 0 1
\n 0 1.38 24.39
\n 1 14.22 1.37<\/p>\n

CPU 0 1
\n 0 3.26 10.13
\n 1 9.99 3.15
\nP2P=Enabled Latency (P2P Writes) Matrix (us)
\n GPU 0 1
\n 0 1.30 1.79
\n 1 1.79 1.36<\/p>\n

CPU 0 1
\n 0 3.20 2.62
\n 1 2.67 3.28<\/p>\n

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
\n[chibi@alma8 p2pBandwidthLatencyTest]$
\n[chibi@alma8 bandwidthTest]$ .\/bandwidthTest
\n[CUDA Bandwidth Test] – Starting…
\nRunning on…<\/p>\n

Device 0: NVIDIA TITAN RTX
\n Quick Mode<\/p>\n

Host to Device Bandwidth, 1 Device(s)
\n PINNED Memory Transfers
\n Transfer Size (Bytes) Bandwidth(GB\/s)
\n 32000000 13.1<\/p>\n

Device to Host Bandwidth, 1 Device(s)
\n PINNED Memory Transfers
\n Transfer Size (Bytes) Bandwidth(GB\/s)
\n 32000000 13.2<\/p>\n

Device to Device Bandwidth, 1 Device(s)
\n PINNED Memory Transfers
\n Transfer Size (Bytes) Bandwidth(GB\/s)
\n 32000000 540.2<\/strong><\/p>\n

Result = PASS<\/p>\n

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
\n[chibi@alma8 bandwidthTest]$
\n[chibi@alma8 deviceQuery]$ .\/deviceQuery
\n.\/deviceQuery Starting…<\/p>\n

CUDA Device Query (Runtime API) version (CUDART static linking)<\/p>\n

Detected 2 CUDA Capable device(s)<\/p>\n

Device 0: “NVIDIA TITAN RTX”
\n CUDA Driver Version \/ Runtime Version 11.3 \/ 11.3
\n CUDA Capability Major\/Minor version number: 7.5
\n Total amount of global memory: 24220 MBytes (25396838400 bytes)
\n (072) Multiprocessors, (064) CUDA Cores\/MP: 4608 CUDA Cores
\n GPU Max Clock rate: 1770 MHz (1.77 GHz)
\n Memory Clock rate: 7001 Mhz
\n Memory Bus Width: 384-bit
\n L2 Cache Size: 6291456 bytes
\n Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
\n Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
\n Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
\n Total amount of constant memory: 65536 bytes
\n Total amount of shared memory per block: 49152 bytes
\n Total shared memory per multiprocessor: 65536 bytes
\n Total number of registers available per block: 65536
\n Warp size: 32
\n Maximum number of threads per multiprocessor: 1024
\n Maximum number of threads per block: 1024
\n Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
\n Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
\n Maximum memory pitch: 2147483647 bytes
\n Texture alignment: 512 bytes
\n Concurrent copy and kernel execution: Yes with 3 copy engine(s)
\n Run time limit on kernels: Yes
\n Integrated GPU sharing Host Memory: No
\n Support host page-locked memory mapping: Yes
\n Alignment requirement for Surfaces: Yes
\n Device has ECC support: Disabled
\n Device supports Unified Addressing (UVA): Yes
\n Device supports Managed Memory: Yes
\n Device supports Compute Preemption: Yes
\n Supports Cooperative Kernel Launch: Yes
\n Supports MultiDevice Co-op Kernel Launch: Yes
\n Device PCI Domain ID \/ Bus ID \/ location ID: 0 \/ 1 \/ 0
\n Compute Mode:
\n < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) ><\/p>\n

Device 1: “NVIDIA TITAN RTX”
\n CUDA Driver Version \/ Runtime Version 11.3 \/ 11.3
\n CUDA Capability Major\/Minor version number: 7.5
\n Total amount of global memory: 24218 MBytes (25394348032 bytes)
\n (072) Multiprocessors, (064) CUDA Cores\/MP: 4608 CUDA Cores
\n GPU Max Clock rate: 1770 MHz (1.77 GHz)
\n Memory Clock rate: 7001 Mhz
\n Memory Bus Width: 384-bit
\n L2 Cache Size: 6291456 bytes
\n Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
\n Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
\n Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
\n Total amount of constant memory: 65536 bytes
\n Total amount of shared memory per block: 49152 bytes
\n Total shared memory per multiprocessor: 65536 bytes
\n Total number of registers available per block: 65536
\n Warp size: 32
\n Maximum number of threads per multiprocessor: 1024
\n Maximum number of threads per block: 1024
\n Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
\n Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
\n Maximum memory pitch: 2147483647 bytes
\n Texture alignment: 512 bytes
\n Concurrent copy and kernel execution: Yes with 3 copy engine(s)
\n Run time limit on kernels: No
\n Integrated GPU sharing Host Memory: No
\n Support host page-locked memory mapping: Yes
\n Alignment requirement for Surfaces: Yes
\n Device has ECC support: Disabled
\n Device supports Unified Addressing (UVA): Yes
\n Device supports Managed Memory: Yes
\n Device supports Compute Preemption: Yes
\n Supports Cooperative Kernel Launch: Yes
\n Supports MultiDevice Co-op Kernel Launch: Yes
\n Device PCI Domain ID \/ Bus ID \/ location ID: 0 \/ 65 \/ 0
\n Compute Mode:
\n < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
\n> Peer access from NVIDIA TITAN RTX (GPU0) -> NVIDIA TITAN RTX (GPU1) : Yes
\n> Peer access from NVIDIA TITAN RTX (GPU1) -> NVIDIA TITAN RTX (GPU0) : Yes<\/p>\n

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.3, CUDA Runtime Version = 11.3, NumDevs = 2
\nResult = PASS
\n[chibi@alma8 deviceQuery]$
\nAMD EPYC 7763 64-Core Processor x2 256GB AlmaLinux release 8.4 TITAN RTX x2 CUDA 11.3 Samples simpleP2P p2pBandwidthLatencyTest bandwidthTest deviceQuery nvidia-smi nvlink -c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

[chibi@alma8 simpleP2P]$ .\/simpleP2P [.\/simpleP2P] – Starting… Checking for multiple GPUs… C … \u7d9a\u304d\u3092\u8aad\u3080 →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[30,5,18],"tags":[],"class_list":["post-7866","post","type-post","status-publish","format-standard","hentry","category-2-sockets","category-centos8","category-nvidia"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/7866","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7866"}],"version-history":[{"count":4,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/7866\/revisions"}],"predecessor-version":[{"id":29177,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/7866\/revisions\/29177"}],"wp:attachment":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7866"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7866"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7866"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}