April 2016
Intermediate to advanced
170 pages
3h 48m
English
Content preview from Distributed Computing with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Closing thoughts
One of the main difficulties in developing parallel applications is getting data access right and avoiding race conditions or situations that would corrupt shared data. Sometimes, these situations are easy to spot as they lead to spectacular crashes. Other times, more worryingly, they are not—the application keeps plodding along, producing incorrect results.
It is always important to have good tests for our applications and their internal functions. It is even more so for parallel applications, where building a clear mental picture of what happens where and when can be particularly challenging.
Another difficulty in parallelizing algorithms is to know when to stop. Amdahl's law tells us very clearly that parallelization is, from ...
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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.