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Python is an interpreted language that has become more commonly used within HPC applications. Python benefits from the ability to write extension modules in C, which can further use optimized libraries that have been written in other compiled languages. For HPC users, two of the most common extensions are NumPy and mpi4py. It is possible to write a full computational kernel in a compiled language...
We propose a new architecture for optimization modeling frameworks in which solvers are expressed as computation graphs in a framework like TensorFlow rather than as standalone programs built on a low-level linear algebra interface. Our new architecture makes it easy for modeling frameworks to support high performance computational platforms like GPUs and distributed clusters, as well as to generate...
We present a case study of optimizing a Python-based music recognition application on Intel Haswell Xeon processor. With support from Numpy and Scipy, Python addresses the requirements of the music recognition problem with math library utilization and special structures for data access. However, a general optimized Python application cannot fully utilize the latest high performance multicore processors...
The use of Python as a high level productivity language on top of high performance libraries written in C++ requires efficient, highly functional, and easy-to-use cross-language bindings. C++ was standardized in 1998 and up until 2011 it saw only one minor revision. Since then, the pace of revisions has increased considerably, with a lot of improvements made to expressing semantic intent in interface...
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