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Re:Optimizing Python – A Case Study by p1esk on Friday May 29, 2015 @12:49AM (#9622734)
Same question to you: if you have some code that really has to be fast, is it viable to keep it in C++, or should you ultimately end up rewriting it for GPU?
Re:Optimizing Python – A Case Study by wallstop on Friday May 29, 2015 @01:00AM (#9622754)
Is this really a suitable counter-question? GPUs lend themselvse towards specific kinds of programming problems + added latency of GPU communication. On the contrary, the cost between switching from any on-CPU language is programmer time that may result in significant runtime advantages.
Re:Optimizing Python – A Case Study by vetinari on Friday May 29, 2015 @07:20AM (#9623887)
Also disadvantages: increased programmer time for implementing new features. Risks: you might optimize the wrong part (i.e. you invest into rewrite, but it will not solve performance problems).

That's why you must quantify advantages and disadvantages, including risks minimization and only then you'll see, whether given course of action is viable.

Re:Optimizing Python – A Case Study by jermy on Friday May 29, 2015 @08:13AM (#9624086)
You should probably first consider seeing if there are critical bits of code that can be rewritten to use MMX/SSE instructions, since your data is in the right cache already, without needing to move it anywhere else.