a tool for source-to-source transformation and optimisation of C++ programs. It is intended to be used as a test-bed for various high-level optimisations; the traditional textbook optimisations are assumed to be handled by the C++ compiler. ·
provides a software development platform that allows developers to take advantage of a new generation of high performance processors. These new processors, including GPUs, the IBM Cell, and other multi-core processors ·
it is possible to execute Python code at speeds approaching that of fully compiled languages, by "specialization". The current prototype operates on i386-compatible processors and shows 2 to 100 times speed-ups, depending on code. ·
by Mark Larson. "The most important thing to remember is to TIME your code. Trying different tricks might or might not speed up your code. So it is very important to time your code to see if you do get a speedup as you try each trick." ·
Gervasio Varela, Pilar Caamaño, Félix Orjales, Álvaro Deibe, Fernando López-Peña, and Richard J. Duro. Neurocomputing132(0):54 - 67 (2014)Innovations in Nature Inspired Optimization and Learning Methods Selected papers from the Third World Congress on Nature and Biologically Inspired Computing NaBIC2011 Machines learning for Non-Linear Processing Selected papers from the 2011 International Conference on Non-Linear Speech Processing NoLISP 2011.