DOE Funds Enthought to Develop Python HPC Framework

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Today Enthought announced that the company has been awarded a $1M Small Business Innovation and Research grant by the United States Department of Energy to expand the capabilities of Python and NumPy for high-performance distributed computing.

The open-source Python HPC framework being developed under this Phase II SBIR will help address the growing need to easily access parallel computing resources by bringing the strengths and ease-of-use of the popular Python programming language and NumPy multidimensional arrays to high-performance and parallel computing. Comprised of three packages, the framework will address current issues hindering software computing on HPC systems: accessibility and ease-of use for non-computer scientists to leverage existing codes and resources for developing solutions, distributed array computing, and coding for node-level speed-up of computations. The first component will improve accessibility to the “The Trilinos Project”, a set of sophisticated algorithms and technologies used for solving large-scale, complex physics, engineering and scientific problems, such as those encountered in ocean modeling, Formula 1 race car design, nuclear engineering, digital dentistry and medical imaging. By wrapping key Trilinos packages in Python, a barrier-to-entry to Trilinos is removed for Python developers.

These Trilinos packages, developed primarily at Sandia National Laboratories, allow scientists to solve partial differential equations and large linear, nonlinear, and optimization problems in parallel, from desktops to distributed clusters to supercomputers, with active research on modern architectures such as GPUs,” states Bill Spotz, senior research scientist at Sandia. “This next phase of the project will improve and continue to expand these PyTrilinos interfaces, making Trilinos easier to use.” Spotz will lead the PyTrilinos effort for the Python HPC framework.

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