{"id":5063,"date":"2021-04-09T02:09:53","date_gmt":"2021-04-08T17:09:53","guid":{"rendered":"https:\/\/wp.study3.biz\/?p=5063"},"modified":"2021-04-09T02:10:55","modified_gmt":"2021-04-08T17:10:55","slug":"amd-epyc-7302p-16-core-processor-red-hat-enterprise-linux-release-8-3-rtx2080ti-x2-cuda-11-1-simplep2p-p2pbandwidthlatencytest-bandwidthtest-devicequery%e3%82%92%e5%8b%95%e4%bd%9c%e3%81%95%e3%81%9b","status":"publish","type":"post","link":"https:\/\/wp.study3.biz\/?p=5063","title":{"rendered":"AMD EPYC 7302P 16-Core Processor Red Hat Enterprise Linux release 8.3 RTX2080Ti x2 CUDA 11.1 simpleP2P p2pBandwidthLatencyTest bandwidthTest deviceQuery\u3092\u52d5\u4f5c\u3055\u305b\u3066\u307f\u305f"},"content":{"rendered":"

\"\"\"\"<\/p>\n

[chibi@rhel8 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 GeForce RTX 2080 Ti (GPU0) -> GeForce RTX 2080 Ti (GPU1) : Yes
\n> Peer access from GeForce RTX 2080 Ti (GPU1) -> GeForce RTX 2080 Ti (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: 43.54GB\/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@rhel8 simpleP2P]$
\n[chibi@rhel8 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: “GeForce RTX 2080 Ti”<\/strong>
\n CUDA Driver Version \/ Runtime Version 11.1 \/ 11.1
\n CUDA Capability Major\/Minor version number: 7.5
\n Total amount of global memory: 11019 MBytes (11554324480 bytes)
\n (68) Multiprocessors, ( 64) CUDA Cores\/MP: 4352 CUDA Cores
\n GPU Max Clock rate: 1635 MHz (1.63 GHz)
\n Memory Clock rate: 7000 Mhz
\n Memory Bus Width: 352-bit
\n L2 Cache Size: 5767168 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 \/ 129 \/ 0
\n Compute Mode:
\n < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) ><\/p>\n

Device 1: “GeForce RTX 2080 Ti”<\/strong>
\n CUDA Driver Version \/ Runtime Version 11.1 \/ 11.1
\n CUDA Capability Major\/Minor version number: 7.5
\n Total amount of global memory: 11019 MBytes (11554717696 bytes)
\n (68) Multiprocessors, ( 64) CUDA Cores\/MP: 4352 CUDA Cores
\n GPU Max Clock rate: 1635 MHz (1.63 GHz)
\n Memory Clock rate: 7000 Mhz
\n Memory Bus Width: 352-bit
\n L2 Cache Size: 5767168 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 \/ 130 \/ 0
\n Compute Mode:
\n < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
\n> Peer access from GeForce RTX 2080 Ti (GPU0) -> GeForce RTX 2080 Ti (GPU1) : Yes
\n> Peer access from GeForce RTX 2080 Ti (GPU1) -> GeForce RTX 2080 Ti (GPU0) : Yes<\/strong><\/p>\n

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.1, CUDA Runtime Version = 11.1, NumDevs = 2
\nResult = PASS
\n[chibi@rhel8 deviceQuery]$
\nAMD EPYC 7302P 16-Core Processor Red Hat Enterprise Linux release 8.3 RTX2080Ti x2 CUDA 11.1 simpleP2P p2pBandwidthLatencyTest bandwidthTest deviceQuery<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

[chibi@rhel8 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":[18,22],"tags":[],"class_list":["post-5063","post","type-post","status-publish","format-standard","hentry","category-nvidia","category-rhel8"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/5063","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=5063"}],"version-history":[{"count":5,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/5063\/revisions"}],"predecessor-version":[{"id":5075,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=\/wp\/v2\/posts\/5063\/revisions\/5075"}],"wp:attachment":[{"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5063"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5063"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.study3.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}