k-kawakami / rnn_benchmark

Which GPU should we buy?
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Tesla M40 vs P100 #1

Open fujish opened 7 years ago

fujish commented 7 years ago

Below are my benchmark results with Tesla M40 and P100. M40 got a higher score on Eval than P100.

Results

# python run.py -rnn LSTM -nlayers 1 -emb_dim 1024 -hid_dim 1024 -tied 0 -epochs 10 -optimizer Adam -lr 0.0002 -dropout 0.5 -batch_size 128 -seq_len 128 -clip 0.1 -seed 1234 -cudnn

Details

cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

cat: /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor: No such file or directory

cat /proc/cpuinfo

... processor : 55 vendor_id : GenuineIntel cpu family : 6 model : 79 model name : Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz stepping : 1 microcode : 0xb00001e cpu MHz : 2596.992 cache size : 35840 KB physical id : 110 siblings : 1 core id : 0 cpu cores : 1 apicid : 110 initial apicid : 110 fpu : yes fpu_exception : yes cpuid level : 20 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts nopl xtopology tsc_reliable nonstop_tsc aperfmperf eagerfpu pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch epb fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 invpcid rtm rdseed adx smap xsaveopt dtherm ida arat pln pts bugs : bogomips : 5193.98 clflush size : 64 cache_alignment : 64 address sizes : 42 bits physical, 48 bits virtual power management:


- M40

/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery/deviceQuery

/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery/deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla M40 24GB" CUDA Driver Version / Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 22940 MBytes (24054136832 bytes) (24) Multiprocessors, (128) CUDA Cores/MP: 3072 CUDA Cores GPU Max Clock rate: 1112 MHz (1.11 GHz) Memory Clock rate: 3004 Mhz Memory Bus Width: 384-bit L2 Cache Size: 3145728 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 Device PCI Domain ID / Bus ID / location ID: 0 / 20 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Tesla M40 24GB Result = PASS

- P100

/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery/deviceQuery

/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery/deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla P100-PCIE-16GB" CUDA Driver Version / Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 6.0 Total amount of global memory: 16276 MBytes (17066885120 bytes) (56) Multiprocessors, ( 64) CUDA Cores/MP: 3584 CUDA Cores GPU Max Clock rate: 1329 MHz (1.33 GHz) Memory Clock rate: 715 Mhz Memory Bus Width: 4096-bit L2 Cache Size: 4194304 bytes Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384) Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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 Device PCI Domain ID / Bus ID / location ID: 0 / 20 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Tesla P100-PCIE-16GB Result = PASS

hitmusic100 commented 2 years ago

thanks for doing this