原作名:GPU Parallel Program Development Using CUDA 译者:唐杰 出版年:2019-7 页数:425 定价:179 装帧:精装 丛书:高性能计算技术丛书 ISBN:9787111630616 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 内容简介· ··· 近10年来,随着大数据、深度学习等相关领域的发展,对计算能力的需求呈...
GPU Parallel Program Development Using CUDA [美]托尔加·索亚塔(Tolga Soyata) 著 唐杰译 第1章 CPU并行编程概述 本书是一本适用于自学GPU和CUDA编程的教科书,我可以想象当读者发现第1章叫“CPU并行编程概述”时的惊讶。我们的想法是,本书希望读者具备较强的低级编程语言(如C语言)的编程能力,但并不需要具备...
清晰,有目录。 CUDA 近期出的书不多,这本还算不错,讲解的比较深入浅出,值得一读! CUDA Parallel 并行计算 GPU2018-08-30 上传大小:14.00MB 所需:50积分/C币 西门子PLC S7-1200/1500与优傲UR机器人Profinet通讯配置及应用实例 内容概要:本文详细介绍了西门子PLC(S7-1200/1500)与优傲UR机器人通过Profinet通讯的...
machine learning and inference on Windows through WSL. GPU acceleration also serves to bring down the performance overhead of running an application inside a WSL like environment close to near-native by being able to pipeline more parallel work on the GPU with less...
CUDA(Compute Unified Device Architecture) is a groundbreaking platform for parallel computing created by NVIDIA. It provides programmers and researchers direct access to NVIDIA GPUs’ virtual instruction set. CUDA improves the efficiency of complex operations such as training AI models, processing large ...
After adding this directive, change the compiler options to enable OpenACC explicitly by using -stdpar=gpu -acc=gpu -gpu=cc80,cuda11.5. This allows you to use only three OpenACC directives. This is the closest this code can come to not having directives at this time....
Multi-GPU Programming with Standard Parallel C++, Part 2 Using Fortran Standard Parallel Programming for GPU Acceleration But in many codes, you can’t get around doing some manual work. In these scenarios, you may consider using a domain-specific language like CUDA to target a specific accelerat...
In a massively parallel environment with hundreds or thousands of threads, it is critical to be able to narrow your breakpoints to just the areas of interest. The CUDA Debugger supports setting conditional breakpoints for GPU threads with arbitrary expressions. Expressions may use program variables, ...
在CPU计算部分我当时觉得自己C技术不太够,然后买了本《GPU Parallel Program Development Using CUDA》前面讲了很多CPU的优化方法,恶补了两天,将原本的优化通过Cache和OpenMP的多线程加强了一下。以下数据是在2048*2048的规模下,下面两个优化方法开了6线程,其中非Part版本我是用的动态分配的Buffer,而Part版本的Cache优...
import os import time import torch import torch.distributed as dist import torch.multiprocessing as mp def init_parallel(rank, world_size): os.environ['MASTER_ADDR'] = 'localhost' os.environ['MASTER_PORT'] = '12345' dist.init_process_group('nccl' if torch.cuda.is_available() else 'gloo...