楼主: igs816
404 39

[其他] Mastering Parallel Programming with R (True PDF) [推广有奖]

学术权威

78%

还不是VIP/贵宾

-

威望
6
论坛币
633815 个
学术水平
1579 点
热心指数
1725 点
信用等级
1178 点
经验
268044 点
帖子
1985
精华
51
在线时间
1563 小时
注册时间
2007-8-6
最后登录
2017-7-27

igs816 在职认证  发表于 2017-7-17 16:38:05 |显示全部楼层
VgPHDShO9t9ZBEnoY4duxfrpKTzB0muW.jpg

English | 2016 | ISBN: 1784394009 | 244 Pages | True PDF | 3.7 MB
Master the robust features of R parallel programming to accelerate your data science computations.

This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks.

What You Will Learn:

- Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package
- Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS)
- Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code
- Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages
- Build and extend a parallel R package (SPRINT) with your own MPI-based routines
- Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL
- Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them
- Build a task farm master-worker, spatial grid, and hybrid parallel R programs

R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources.

Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems.

游客,如果您要查看本帖隐藏内容请回复


支持楼主:购买VIP购买贵宾 购买后,论坛将奖励 10 元论坛资金给楼主,以表示您对TA发好贴的支持
 
载入中......
已有 2 人评分经验 学术水平 热心指数 信用等级 收起 理由
kongqingbao280 + 60 精彩帖子
accumulation + 100 + 1 + 1 + 1 精彩帖子

总评分: 经验 + 160  学术水平 + 1  热心指数 + 1  信用等级 + 1   查看全部评分

本帖被以下文库推荐

stata SPSS
jinyizhe282 发表于 2017-7-17 16:41:01 |显示全部楼层
thanks                                      
回复

使用道具 举报

ermutuxia 发表于 2017-7-17 18:49:13 |显示全部楼层
Wonderful
回复

使用道具 举报

bearfighting 发表于 2017-7-17 19:14:47 |显示全部楼层
好书好书啊
回复

使用道具 举报

军旗飞扬 发表于 2017-7-17 19:47:10 |显示全部楼层
谢谢楼主分享!
回复

使用道具 举报

lianqu 发表于 2017-7-17 19:47:38 |显示全部楼层
回复

使用道具 举报

kavakava 发表于 2017-7-17 19:53:38 |显示全部楼层
Thanks
回复

使用道具 举报

sukiyou2000 发表于 2017-7-17 20:13:42 |显示全部楼层
谢谢分享!
回复

使用道具 举报

dxystata 发表于 2017-7-17 20:31:13 |显示全部楼层
谢谢分享!
回复

使用道具 举报

bocm 发表于 2017-7-17 21:49:23 |显示全部楼层
Thanks for sharing
回复

使用道具 举报

您需要登录后才可以回帖 登录 | 我要注册

GMT+8, 2017-7-28 02:55