Starting to work on runtime plugin system for plugin (automatic sse optimization, etc...)
Hi, I've just started working on a prototype for a plugin system for numpy. The plugin aims at providing a framework for the following user cases: - runtime selection of blas/lapack/etc...: instead of harcoding in the binary one blas/lapack implementation, numpy could choose the SSE optimized if the CPU supports SSE, etc... - this could also be used for core numpy, for example ufuncs: if we want to start implementing some tight loop with aggressively optimized code (SSE, etc...), we could again ship with a default pure C implementation, and choose the best one at runtime. - we could even have a system to choose a different implementation (for example, right now, scipy is shipped with a slow fft for licensing issues mainly, and people installing fftw could then tell scipy to use fftw instead of the included one). Right now, the prototype does not do much, and only works for linux; I mainly focused on automatic generation of the plugin from a list of functions, and transparent use from numpy point of view. It provides the plugin api through pure function pointers, without the need for the user to be aware of it. For example, if you have an api with the following functions: void foo1(); int foo2(); int foo3(int); int foo4(double* , double*); int foo5(double* , double*, int); The current implementation would build the boilerplate to load those functions, etc... and you would just use those functions in numpy like the following: init_foo(); /* all functions are prefixed with npyw, for numpy wrapper */ npyw_foo1(); npyw_foo2(n); etc... The code can be found there: https://code.launchpad.net/~david-ar/+junk/numplug And some thinking (pretty low content for now): http://www.scipy.org/RuntimeOptimization cheers, David
participants (6)
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Andreas Klöckner
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David Cournapeau
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David Cournapeau
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Lisandro Dalcin
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Matthew Brett
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Stéfan van der Walt