December 2014
Intermediate to advanced
698 pages
19h 8m
English
Content preview from Multicore and GPU Programming
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Chapter 1
Introduction
Abstract
Multicore chips in their various guises, have been powering all kinds of computing devices since the mid 2000s. In this chapter we present the most influential multicore designs, and explain how they fit into the overall realm of computing, as categorized by Flynn’s taxonomy. The metrics used to measure and assess the performance of a parallel program, i.e. speedup and efficiency, are also explained.
While speedup and efficiency are simple metrics, the process for obtaining them is fraught with pitfalls. For this reason, we explicitly address the issue of how one should design experiments that properly evaluate the potential of a parallel algorithm and its implementation.
We complete this chapter with a description ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.