这是我的纹理课:
struct GPUAllocation {
uint ID,VectorType,DataType,Width,Height,Format,IntFormat;
//ID is the output from glGen* and Format is the format of the texture and IntFormat
//is the Internal Format of this texture. (Width and height and too obvious)
//DataType is like GL_FLOAT...
我有一个基于的Dockerfile,像这样:
FROM nvidia/cuda:11.0-base
...
我希望能够在没有Nvidia GPU的CI服务器上构建此Dockerfile。当我尝试这样做的时候,我得到了这个错误:
------
> [1/6] FROM docker.io/nvidia/cuda:11.0-base:
------
failed to solve with frontend dockerfile.v0: failed to solve with frontend gateway.v0: rpc error: code = Unknown desc = f
运行2 (2GB) AMD 270 x卡时,是否可以绕过R9对VRAM大小的限制?
我犯了一个错误,以为我的单台2GB的R9 270 x和我的6GB GTX 1060一起运行就行了。然而,DAG已经超过了2GB的限制,到这篇文章,现在我不能运行这两个。
参见Claymore的输出:
ETH: Authorized
Setting DAG epoch #133...
Setting DAG epoch #133 for GPU0
Create GPU buffer for GPU0
Setting DAG epoch #133 for GPU1
GPU0 - not enough GPU mem
我正试图为cuSOLVER函数使用scikit-cuda的包装器,特别是我想要执行cusolverDnSgesvd来在实数矩阵上计算完全矩阵的单精度SVD。
使用代码和作为参考,我设法做到了以下几点:
import pycuda.autoinit
import pycuda.driver as drv
import pycuda.gpuarray as gpuarray
import numpy as np
from skcuda import cusolver
handle = cusolver.cusolverDnCreate()
m = 50
n = 25
a = np.asa
我正在尝试做一些挖掘,但当我运行start.bat时,我一直收到以下错误(在cmd中)
╔════════════════════════════════════════════════════════════════╗
║ Claymore's Dual ETH + DCR/SC/LBC/PASC GPU Miner v10.0 ║
╚════════════════════════════════════════════════════════════════╝
ETH: 5 pools are specified
Main Ethereum pool is e
我一直试图在我的GPU上运行我的神经网络,但由于某些原因,在创建设备时,Tensorflow不会看到全部的RAM内存,而是专注于2 2GB的空闲内存。
Using TensorFlow backend.
2018-05-25 11:00:56.992852: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this Ten
sorFlow binary was not compiled to use: AVX2
20
我注意到,一个大型复杂数组在GPU上占用的内存是CPU上内存的两倍。
下面是一个很小的例子:
%-- First Try: Complex Single
gpu = gpuDevice(1);
m1 = gpu.FreeMemory;
test = complex(single(zeros(600000/8,1000))); % 600 MByte complex single
whos('test')
test = gpuArray(test);
fprintf(' Used memory on GPU: %e\n', m1-gpu.FreeMemory);