Rye-GPU
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Nvidia GPU
Farmshare has GPU's via two systems, rye01 and rye02. You can use these two systems as you would a corn system:
ssh rye01.stanford.edu
or
ssh rye02.stanford.edu
hardware
rye01 and rye02 are Intel CPU systems with 8 GPU's each.
- 8 core (2x E5620)
- 48GB Ram
- 250GB local disk
FarmShare would like to extend special thanks to Jon Pilat, Brian Tempero and Margot Gerritsen for their support.
example 1
An easy first thing to try is to login, load the cuda module and run deviceQuery to see what kind of cuda device it is. Then we run a matrix multiply sample that comes with the cuda toolkit.
$ ssh rye01.stanford.edu rye01.stanford.edu - Ubuntu 13.04, amd64 8-core Xeon E5620 @ 2.40GHz (FT72-B7015, empty); 47.16GB RAM, 10GB swap Puppet environment: rec_master; kernel 3.8.0-30-generic (x86_64) --*-*- Stanford University Research Computing -*-*-- _____ ____ _ | ___|_ _ _ __ _ __ ___ / ___|| |__ __ _ _ __ ___ | |_ / _` | '__| '_ ` _ \\___ \| '_ \ / _` | '__/ _ \ | _| (_| | | | | | | | |___) | | | | (_| | | | __/ |_| \__,_|_| |_| |_| |_|____/|_| |_|\__,_|_| \___| http://farmshare.stanford.edu ### ## # new to Ubuntu 13.04 Farmshare? # follow this link to get started: # https://www.stanford.edu/group/farmshare/cgi-bin/wiki/index.php/Ubuntu13TransitionGuide ## ### Last login: Sun Sep 15 20:52:27 2013 from scorn.stanford.edu your cuda device is: CUDA_VISIBLE_DEVICES=6 device last used: unused bishopj@rye01:~$ module load cuda bishopj@rye01:~$ /usr/local/cuda/samples/bin/x86_64/linux/release/deviceQuery /usr/local/cuda/samples/bin/x86_64/linux/release/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "Tesla C2070" CUDA Driver Version / Runtime Version 5.5 / 5.5 CUDA Capability Major/Minor version number: 2.0 Total amount of global memory: 5375 MBytes (5636554752 bytes) (14) Multiprocessors, ( 32) CUDA Cores/MP: 448 CUDA Cores GPU Clock rate: 1147 MHz (1.15 GHz) Memory Clock rate: 1494 Mhz Memory Bus Width: 384-bit L2 Cache Size: 786432 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65535), 3D=(2048, 2048, 2048) 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: 32768 Warp size: 32 Maximum number of threads per multiprocessor: 1536 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): (65535, 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 Device PCI Bus ID / PCI location ID: 132 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 5.5, CUDA Runtime Version = 5.5, NumDevs = 1, Device0 = Tesla C2070 Result = PASS bishopj@rye01:~$ /usr/local/cuda/samples/bin/x86_64/linux/release/matrixMulCUBLAS [Matrix Multiply CUBLAS] - Starting... GPU Device 0: "Tesla C2070" with compute capability 2.0 MatrixA(320,640), MatrixB(320,640), MatrixC(320,640) Computing result using CUBLAS...done. Performance= 507.76 GFlop/s, Time= 0.258 msec, Size= 131072000 Ops Computing result using host CPU...done. Comparing CUBLAS Matrix Multiply with CPU results: PASS
example 2
We can also login, load the cuda module and run the same deviceQuery and matrix multiply sample in matlab
it has its own page, so click MatlabGPUDemo1
