SBIR/STTR Research Award

Portacy: Enabling Advanced Exascale Mapping Tool to Assist in Porting Legacy Codes

Award identifier e4f950e9f40c23e26c408ed6ef272e161b645261382286a6f0f6ee7588121bb1Awarding agency Department of Energy

Award amount

$1,499,995

Program and phase

SBIR · Phase II

Award year

2018

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
Reservoir Labs, Inc.
Proposal award date
2018-05-21
Contract end date
2020-05-20
UEI
J4DVUAMQJMW7
Solicitation number
DE-FOA-0001794
Topic code
02b

Research abstract

Fast evolution of supercomputer architecture lead to an increasingly error-prone, lengthy and costly problem of porting existing applications to new architectures. For a useful category of compute-intensive, loop-based codes such as found in DOE applications, solutions exist that take sequential code and optimize it for modern parallel computers, but these expect a “de-optimized” form as input, which results in users still having to rewrite code. Also they don’t accept some input languages used in most DOE applications, which are written mostly in the C, C++ and Fortran languages, and increasingly using Deep Learning frameworks. Project Objective The main objective is to turn a tool for automatic parallelization of compute-intensive loop-based C sequential codes into a tool that supports more input languages (C, C++ and Fortran) and Deep Learning codes as inputs. Another objective is to enable new optimizations based on data transformations and alternative parallelization in a leading Deep Learning framework. What was done in Phase I? In Phase I, we enhanced the porting tool front-end capabilities to: - enable the intake of a significant subset of C++ (mostly excludes Exceptions and atomics);- carry parallelism-related user annotations semantics through to the polyhedral representation of the program, in whichthe program is optimized;- automatically approximate irregular program constructs such as indirect array accesses;- automatically approximate array accesses performed during function calls -- including library calls -- within parallelizable code regions Commercial Applications and Other Benefits Portacy will provide the immediate benefit of being able to generate versions of applications for new accelerators from sequential and code parallelized using traditional parallel programming models. It will also enhance the performance of distributed Deep Learning codes. The degree to which a larger range of Deep Learning codes will be optimized will also increase with the use of the Portacy tool.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

71 SBIR/STTR records share this UEI (2004–2022). Reported award amounts total $38,801,244. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, Department of Energy

Agency intelligence

Department of Energy

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