Parallel Computing for Data Science: With Examples in R, C++ by Norman Matloff PDF

By Norman Matloff

ISBN-10: 1466587032

ISBN-13: 9781466587038

Parallel Computing for facts technology: With Examples in R, C++ and CUDA is among the first parallel computing books to pay attention solely on parallel information buildings, algorithms, software program instruments, and purposes in information technology. It contains examples not just from the vintage "n observations, p variables" matrix structure but additionally from time sequence, community graph versions, and diverse different buildings universal in info technological know-how. The examples illustrate the variety of matters encountered in parallel programming.

With the main target on computation, the publication exhibits easy methods to compute on 3 kinds of systems: multicore structures, clusters, and pix processing devices (GPUs). It additionally discusses software program applications that span a couple of form of and will be used from a couple of form of programming language. Readers will locate that the basis validated during this booklet will generalize good to different languages, equivalent to Python and Julia.

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Additional info for Parallel Computing for Data Science: With Examples in R, C++ and CUDA

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As will be seen in detail later, a cluster is an R object that contains information on the various workers and how to reach them. So, if I run c l s <− i n i t m c ( 4 ) I create a 4-node snow cluster (for 4 workers) and save its information in an R object cls (of class “cluster”), which will be used in my subsequent calls to snow functions. There is one component in cls for each worker. 4. EXTENDED EXAMPLE: MUTUAL WEB OUTLINKS 13 length ( c l s ) prints out 4. , several machines connected via a network.

The latter is not a physical object, but rather a software interface from your program to the network. • The socket software will form a packet from your request, which will then go through several layers of the network protocol stack in your OS. Along the way, the packet will grow, as more information is being added, but also it will split into multiple, smaller packets. • Eventually the packets will reach your computer’s network interface hardware, from which they go onto the network. • A gateway on the network will notice that the ultimate destination is external to this network, so the packets will be transferred to another network that the gateway is also attached to.

But suppose Moe’s code is threaded, running two threads. Now we will have three threads—Moe’s two and Manny’s one (even a non-threaded program consists of one thread)—competing to use three cores. Moe’s two threads will sometimes run in parallel with each other but sometimes not. 10 There are also possible cache issues. When a thread starts a new turn, it may be on a different core than that used in the last turn. If there is a separate cache for each core, the cache as the new core probably contains little if anything useful to this thread.

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Parallel Computing for Data Science: With Examples in R, C++ and CUDA by Norman Matloff


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