Exploiting superword level parallelism with multimedia instruction sets
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
C++ Templates
TOOLS '97 Proceedings of the Tools-23: Technology of Object-Oriented Languages and Systems
Software Vectorization Handbook, The: Applying Intel Multimedia Extensions for Maximum Performance
Software Vectorization Handbook, The: Applying Intel Multimedia Extensions for Maximum Performance
Python for Scientific Computing
Computing in Science and Engineering
Tracing the meta-level: PyPy's tracing JIT compiler
Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems
The NumPy Array: A Structure for Efficient Numerical Computation
Computing in Science and Engineering
Cython: The Best of Both Worlds
Computing in Science and Engineering
An Evaluation of Vectorizing Compilers
PACT '11 Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques
Parakeet: a just-in-time parallel accelerator for python
HotPar'12 Proceedings of the 4th USENIX conference on Hot Topics in Parallelism
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The Python language is highly dynamic, most notably due to late binding. As a consequence, programs using Python typically run an order of magnitude slower than their C counterpart. It is also a high level language whose semantic can be made more static without much change from a user point of view in the case of mathematical applications. In that case, the language provides several vectorization opportunities that are studied in this paper, and evaluated in the context of Pythran, an ahead-of-time compiler that turns Python module into C++ meta-programs.