No abstract

Abstract. We present an automatic, static program transformation that schedules and generates ecient memory transfers between a computer host and its hardware accelerator, addressing a well-known performance bottleneck. Our automatic approach uses two simple heuristics: to perform transfers to the accelerator as early as possible and to delay transfers back from the accelerator as late as possible. We implemented this transformation as a middle-end compilation pass in the pips/Par4All compiler. In the generated code, redundant communications due to data reuse between kernel executions are avoided. Instructions that initiate transfers are scheduled eectively at compile-time. We present experimental results obtained with the Polybench 2.0, some Rodinia benchmarks, and with a real numerical simulation. We obtain an average speedup of 4 to 5 when compared to a naïve parallelization using a modern gpu with Par4All, hmpp, and pgi, and 3.5 when compared to an OpenMP version using a 12-core multiprocessor.

Pythran is a young open source static compiler that turns modules written in a subset of Python into native ones. Based on the fact that scientific modules do not rely much on the dynamic features of the language, it trades them in favor of powerful, eventually inter procedural, optimizations. These include detection of pure functions, temporary allocation removal, constant folding, Numpy ufunc fusion and parallelization, explicit thread-level parallelism through OpenMP annotations, false variable polymorphism pruning, and automatic vector instruction generation such as AVX or SSE.In addition to these compilation steps, Pythran provides a C++ runtime library that leverages the C++ STL to provide generic containers, and the Numeric Template Toolbox (NT2) for Numpy support. It takes advantage of modern C++11 features such as variadic templates, type inference, move semantics and perfect forwarding, as well as classical ones such as expression templates.The input code remains compatible with the Python interpreter, and output code is generally as efficient as the annotated Cython equivalent, if not more, without the backward compatibility loss of Cython. Numpy expressions run faster than when compiled with numexpr, without any change of the original code.

No abstract

Pythran is a young open source static compiler that turns modules written in a subset of Python into native ones. Based on the fact that scientific modules do not rely much on the dynamic features of the language, it trades them in favor of powerful, eventually inter procedural, optimizations. These include detection of pure functions, temporary allocation removal, constant folding, Numpy ufunc fusion and parallelization, explicit thread-level parallelism through OpenMP annotations, false variable polymorphism pruning, and automatic vector instruction generation such as AVX or SSE. In addition to these compilation steps, Pythran provides a C++ runtime library that leverages the C++ STL to provide generic containers, and the Numeric Template Toolbox (NT2) for Numpy support. It takes advantage of modern C++11 features such as variadic templates, type inference, move semantics and perfect forwarding, as well as classical ones such as expression templates. The input code remains compatible with the Python interpreter, and output code is generally as efficient as the annotated Cython equivalent, if not more, without the backward compatibility loss of Cython. Numpy expressions run faster than when compiled with numexpr, without any change of the original code.