Dear users and developers Currently I am using two Tesla K40m cards for my computational work on quantum espresso (QE). My GPU enabled QE code running very slower than normal version. My question was weather particular application will be fast only in some versions of CUDA toolkit? (as mentioned in previous post: http://qe-forge.org/pipermail/pw_forum/2015-May/106889.html) OR is there any other reason hindering performance (memory) of GPU? (when I am hitting top command in my server, option of 'VIRT' showing different values (top command pasted in attached file))
Some error was generating while submitting code that "A high-performance Open MPI point-to-point messaging module was unable to find any relevant network interfaces: Module: OpenFabrics (openib) Host: XXXX Another transport will be used instead, although this may result in lower performance". Is this MPI thread hindering GPU performance ? (P.S: We don't have any Infiband adapter HCA in server) Current details of server are (full details attached): Server: FUJITSU PRIMERGY RX2540 M2 CUDA version: 9.0 NVIDIA driver: 384.9 openmpi version: 2.0.4 with intel mkl libraries QE-gpu version : 5.4.0 Thanks in advance Regards Phanikumar
################################################################################################################################################## SERVER architecture information (from "lscpu" command in terminal) ################################################################################################################################################## Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 40 On-line CPU(s) list: 0-39 Thread(s) per core: 2 Core(s) per socket: 10 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 79 Model name: Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz Stepping: 1 CPU MHz: 1200.000 BogoMIPS: 4788.53 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 25600K NUMA node0 CPU(s): 0-9,20-29 NUMA node1 CPU(s): 10-19,30-39 ################################################################################################################################################## After I run device quiry in CUDA_samples I got this information about my GPU accelerators ################################################################################################################################################## CUDA Device Query (Runtime API) version (CUDART static linking) Detected 2 CUDA Capable device(s) Device 0: "Tesla K40m" CUDA Driver Version / Runtime Version 9.0 / 9.0 CUDA Capability Major/Minor version number: 3.5 Total amount of global memory: 11440 MBytes (11995578368 bytes) (15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores GPU Max Clock rate: 745 MHz (0.75 GHz) Memory Clock rate: 3004 Mhz Memory Bus Width: 384-bit L2 Cache Size: 1572864 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Enabled Device supports Unified Addressing (UVA): Yes Supports Cooperative Kernel Launch: No Supports MultiDevice Co-op Kernel Launch: No Device PCI Domain ID / Bus ID / location ID: 0 / 2 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > Device 1: "Tesla K40m" CUDA Driver Version / Runtime Version 9.0 / 9.0 CUDA Capability Major/Minor version number: 3.5 Total amount of global memory: 11440 MBytes (11995578368 bytes) (15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores GPU Max Clock rate: 745 MHz (0.75 GHz) Memory Clock rate: 3004 Mhz Memory Bus Width: 384-bit L2 Cache Size: 1572864 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Enabled Device supports Unified Addressing (UVA): Yes Supports Cooperative Kernel Launch: No Supports MultiDevice Co-op Kernel Launch: No Device PCI Domain ID / Bus ID / location ID: 0 / 129 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > > Peer access from Tesla K40m (GPU0) -> Tesla K40m (GPU1) : No > Peer access from Tesla K40m (GPU1) -> Tesla K40m (GPU0) : No deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 9.0, NumDevs = 2 Result = PASS ################################################################################################################################################## GPU performance after 'nvidia-smi' command in terminal ################################################################################################################################################## +-----------------------------------------------------------------------------+ | NVIDIA-SMI 384.90 Driver Version: 384.90 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K40m Off | 00000000:02:00.0 Off | 0 | | N/A 42C P0 75W / 235W | 11381MiB / 11439MiB | 83% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K40m Off | 00000000:81:00.0 Off | 0 | | N/A 46C P0 75W / 235W | 11380MiB / 11439MiB | 87% Default | +-------------------------------+----------------------+----------------------+ ################################################################################################################################################## TOP command if my server ################################################################################################################################################## PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 20019 xxxxx 20 0 0.158t 426080 152952 R 100.3 0.3 36:29.44 pw-gpu.x 20023 xxxxx 20 0 0.158t 422380 153328 R 100.0 0.3 36:29.42 pw-gpu.x 20025 xxxxx 20 0 0.158t 418256 153376 R 100.0 0.3 36:27.74 pw-gpu.x 20042 xxxxx 20 0 0.158t 416912 153104 R 100.0 0.3 36:24.63 pw-gpu.x 20050 xxxxx 20 0 0.158t 412564 153084 R 100.0 0.3 36:25.68 pw-gpu.x 20064 xxxxx 20 0 0.158t 408012 153100 R 100.0 0.3 36:25.54 pw-gpu.x 20098 xxxxx 20 0 0.158t 398404 153436 R 100.0 0.3 36:27.92 pw-gpu.x
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