forked from cheng/wallet
75 lines
4.0 KiB
HTML
75 lines
4.0 KiB
HTML
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<link rel="shortcut icon" href="../rho.ico">
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<title>Multicore</title>
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<p><a href="./index.html"> To Home page</a> </p>
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<h1>Multicore</h1>
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<p> At the same time as the security crisis has hit, the multicore crisis
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has hit. A short while ago we were dealing with isolated serial
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machines, now our programs must utilize a sea of machines. </p>
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<p> Multithreading is hard. Doing it routinely, doing it with large
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programs, invariably fails. </p>
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<p> Intel, NVIDIA, and Google however have this crisis well in hand. </p>
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<p> Big Businesses are attacking the problem, with competence and success,
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and we can leave them to it and not worry too much. Google is pioneering
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the way, and Intel and NVIDIA are making similar tools available to the
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masses. </p>
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<p> Since massive parallelism is a hard problem, requiring good people, much
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thought, and much care, the meta solution is to solve that problem as few
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times as possible, and re-use the resulting solutions as much as
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possible. If, for example, one uses the hash table provided by
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Intel’s threaded building blocks library, the Intel library and Intel
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compiler takes care of hash table related coordination issues that
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otherwise the programmer would have to take care of, and would probably
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foul up. </p>
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<p> Intel has provided a bunch of utilities that make it a good deal easier,
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Vtune, thread checker, OpenMP, compiler auto parallelism, Intel Thread
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Checker, Intel VTune Performance Analyzer, and most importantly, Threaded
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Building Blocks. it is still hard – but no longer damn near
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impossible. </p>
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<p> Back in the days when there was one hardware thread of execution driving
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multiple software threads, locking worked well. These day, not so
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well. Rather, it is often more desirable to use a lockless
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transactional approach to handle any shared state. Shared state is
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hard, better to share nothing – or to leave any sharing to those utilities
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that someone else has already written and debugged. If rolling your
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own, better to use InterlockedXxx than Lock. Note that you construct
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your <a href="interlockedxxx.html">own InterlockedXxx</a> operation for
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any Xxx using InterlockedCompareExchange. </p>
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<p> The big solution, however is that pioneered by Google. Rather than each
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programmer designing his own multithreading and multicore design, one has
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a small number of very general massively parallel algorithms embodied in
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useful software for massaging masses of data. The programmer then
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calls that software and lets it handle the parallelism. Google’s Map
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Reduce is the classic example of this, but every database servicing a web
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application is also an example of this, since one typically has many web
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servers running many processes all of which might potentially update the
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same data at the same time, and the database is supposed to sort out any
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resulting problems, while the developers write in single threaded python
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or ruby on rails, and let the database handle any problems related to
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massive parallelism. </p>
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<p> Google’s “app engine” allows programmers to write straightforward single
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threaded python code in the easy to use Django framework that can be
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executed in a massively parallel manner with coordination between many
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parallel processes being performed by Google’s datastore. </p>
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<p> In short, the multicore crisis, unlike the other crises I describe in
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this group of web pages, is well in hand. </p>
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<p style="background-color : #ccffcc; font-size:80%">These documents are
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licensed under the <a rel="license" href="http://creativecommons.org/licenses/by-sa/3.0/">Creative
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Commons Attribution-Share Alike 3.0 License</a></p>
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