![將cuda分配給特定的GPU](https://rvso.com/image/1087064/%E5%B0%87cuda%E5%88%86%E9%85%8D%E7%B5%A6%E7%89%B9%E5%AE%9A%E7%9A%84GPU.png)
安裝了兩張 NVIDIA 780Ti 卡。在 Ubuntu 14.04 上使用 cuda 7.5。安裝後檢查表顯示 cuda 安裝正確且功能正常。我的顯示器連接到裝置 0。 IT 的輸出顯示了預期的兩張 NVIDIA 卡:
Fri Apr 1 01:04:31 2016
+------------------------------------------------------+
| NVIDIA-SMI 352.79 Driver Version: 352.79 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 780 Ti Off | 0000:01:00.0 N/A | N/A |
| 38% 50C P2 N/A / N/A | 1084MiB / 3071MiB | N/A Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 780 Ti Off | 0000:03:00.0 N/A | N/A |
| 29% 34C P8 N/A / N/A | 11MiB / 3071MiB | N/A Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 Not Supported |
| 1 Not Supported |
+-----------------------------------------------------------------------------+
但是 deviceQuery 僅顯示 1 張卡:
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX 780 Ti"
CUDA Driver Version / Runtime Version 7.5 / 7.5
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 3072 MBytes (3221028864 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP: 2880 CUDA Cores
GPU Max Clock rate: 1084 MHz (1.08 GHz)
Memory Clock rate: 3500 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 1 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: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 1, Device0 = GeForce GTX 780 Ti
Result = PASS
我嘗試使用環境變數將 cuda 定向到其他顯示卡
CUDA_VISIBLE_DEVICES=1
我添加了這一行
export CUDA_VISIBLE_DEVICES=1
到 .bashrc 並開啟一個新的終端機視窗。 Printenv 向我展示了 CUDA_VISIBLE_DEVICES=1 等。
我運行了頻寬測試。其輸出為:
[CUDA Bandwidth Test] - Starting...
Running on...
Device 0: GeForce GTX 780 Ti
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 11618.3
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12909.9
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 265048.1
Result = PASS
我重新啟動並重新運行bandwidthTest,但它仍然以以下方式開始輸出:
[CUDA Bandwidth Test] - Starting...
Running on...
Device 0: GeForce GTX 780 Ti
Quick Mode
BandwidthTest 仍然使用裝置 0。我缺什麼?
答案1
看來這是 deviceQuery 的問題。
當我開始時
nvidia-smi -l 1 --query --display=PERFORMANCE >> gpu_utillization.log
然後啟動cuda編譯的範例應用程序,粒子
日誌顯示了一些有趣的事情。在「靜止」狀態下,在粒子啟動之前,GPU0 處於性能狀態 2,GPU1 處於性能狀態 8。
退出粒子後,性能狀態返回基線。 GPU0 正在調整我的顯示器,所以我想這就是它永遠不會進入狀態 8 的原因。
這解釋性能狀態。
P0/P1 - Maximum 3D performance
P2/P3 - Balanced 3D performance-power
P8 - Basic HD video playback
P10 - DVD playback
P12 - Minimum idle power consumption