cuda

从零开始学Pytorch(零)之安装Pytorch

时光毁灭记忆、已成空白 提交于 2021-02-08 08:47:09
点击上方“ 计算机视觉cv ”即可“进入公众号” 重磅干货第一时间送达 Pytorch优势   聊聊为什么使用Pytorch,个人觉得Pytorch比Tensorflow对新手更为友善,而且现在Pytorch在学术界使用的得更多,大有逆袭Tensorflow之势。最近两年的顶会文章中,代码用Pytorch的比Tensorflow多。大家如果对Tensorflow也感兴趣,完全可以学习了Pytorch之后继续学习Tensorflow,基本原理都是相通的。让我们开始开启愉快的Pytorch学习之旅吧! 在Ubuntu系统或是windows系统安装Pytorch   首先打开Pytorch的官网:https://pytorch.org/。在首页我们可以看到有各种配置可选,我们这里选择CPU或是GPU的版本都可以。一般选择GPU版本的Pytorch,这样运行大型的程序速度会快很多。而要运行GPU的Pytorch,就需要在电脑(不管是Ubuntu系统还是windows系统)安装相应的CUDA9和cudnn7,这两个安装起来有时候会遇到一大堆问题,所以需要一步一步解决。推荐一个我写的在Ubuntu系统安装教程的博客,按照博客上写的一步步来就可以啦。 博客链接:https://blog.csdn.net/xiewenrui1996/article/details/102736238 。  

Access GPU hardware specifications in Python?

随声附和 提交于 2021-02-08 08:31:35
问题 I want to access various NVidia GPU specifications using Numba or a similar Python CUDA pacakge. Information such as available device memory, L2 cache size, memory clock frequency, etc. From reading this question, I learned I can access some of the information (but not all) through Numba's CUDA device interface. from numba import cuda device = cuda.get_current_device() attribs = [s for s in dir(device) if s.isupper()] for attr in attribs: print(attr, '=', getattr(device, attr)) Output on a

“unknown error” while using dynamic allocation inside __device__ function in CUDA

这一生的挚爱 提交于 2021-02-08 05:24:34
问题 I'm trying to implement a linked list in a CUDA application to model a growing network. In oder to do so I'm using malloc inside the __device__ function, aiming to allocate memory in the global memory. The code is: void __device__ insereviz(Vizinhos **lista, Nodo *novizinho, int *Gteste) { Vizinhos *vizinho; vizinho=(Vizinhos *)malloc(sizeof(Vizinhos)); vizinho->viz=novizinho; vizinho->proxviz=*lista; *lista=vizinho; novizinho->k=novizinho->k+1; } After a certain number of allocated elements

Cuda coalesced memory load behavior

拟墨画扇 提交于 2021-02-08 05:08:29
问题 I am working with an array of structure, and I want for each block to load in shared memory one cell of the array. For example : block 0 will load array[0] in shared memory and block 1 will load array[1]. In order to do that I cast the array of structure in float* in order to try to coalesce memory access. I have two version of the code Version 1 __global__ void load_structure(float * label){ __shared__ float shared_label[48*16]; __shared__ struct LABEL_2D* self_label; shared_label[threadIdx

Cuda coalesced memory load behavior

一笑奈何 提交于 2021-02-08 05:02:03
问题 I am working with an array of structure, and I want for each block to load in shared memory one cell of the array. For example : block 0 will load array[0] in shared memory and block 1 will load array[1]. In order to do that I cast the array of structure in float* in order to try to coalesce memory access. I have two version of the code Version 1 __global__ void load_structure(float * label){ __shared__ float shared_label[48*16]; __shared__ struct LABEL_2D* self_label; shared_label[threadIdx

switch cuda compute mode to default mode

橙三吉。 提交于 2021-02-07 23:12:12
问题 I use nvidia-smi to see the status of each GPU on a computing node but find one of them is E. Thread . Is there any easy way to switch it back to default mode? ------------------------------------------------------+ | NVIDIA-SMI 346.46 Driver Version: 346.46 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |======================

BLAS equivalent of a LAPACK function for GPUs

半城伤御伤魂 提交于 2021-02-07 15:17:26
问题 In LAPACK there is this function for diagonalization SUBROUTINE DSPGVX( ITYPE, JOBZ, RANGE, UPLO, N, AP, BP, VL, VU, $ IL, IU, ABSTOL, M, W, Z, LDZ, WORK, IWORK, $ IFAIL, INFO ) * I am looking for its GPU implementation. I am trying to find whether this function has been already implemented in CUDA (or OpenCL), but have only found CULA, which is not open source. Therefore and side CUBLAS exists, I wonder how could I know whether a BLAS or CUBLAS equivalent of this subroutine is available. 回答1

Cuda atomics change flag

好久不见. 提交于 2021-02-07 11:13:35
问题 I have a piece of serial code which does something like this if( ! variable ) { do some initialization here variable = true; } I understand that this works perfectly fine in serial and will only be executed once. What atomics operation would be the correct one here in CUDA? 回答1: It looks to me like what you want is a "critical section" in your code. A critical section allows one thread to execute a sequence of instructions while preventing any other thread or threadblock from executing those

Cuda atomics change flag

丶灬走出姿态 提交于 2021-02-07 11:10:20
问题 I have a piece of serial code which does something like this if( ! variable ) { do some initialization here variable = true; } I understand that this works perfectly fine in serial and will only be executed once. What atomics operation would be the correct one here in CUDA? 回答1: It looks to me like what you want is a "critical section" in your code. A critical section allows one thread to execute a sequence of instructions while preventing any other thread or threadblock from executing those

Cuda atomics change flag

做~自己de王妃 提交于 2021-02-07 11:06:51
问题 I have a piece of serial code which does something like this if( ! variable ) { do some initialization here variable = true; } I understand that this works perfectly fine in serial and will only be executed once. What atomics operation would be the correct one here in CUDA? 回答1: It looks to me like what you want is a "critical section" in your code. A critical section allows one thread to execute a sequence of instructions while preventing any other thread or threadblock from executing those