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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Clang UPC2C Translator (Clang UPC2C) v9.0.1-1

Clang Unified Parallel C 2 C (Clang UPC2C) translator compiles programs written in the UPC (Unified Parallel C) language to ISO C99, with calls to the Berkeley UPC runtime system. The Clang UPC2C compiler extends the capabilities of the Clang LLVM C frontend to comply with the UPC Language Specification version 1.3. The compiler generates programs that run on a wide variety of systems ranging from workstations to leadership-class supercomputers, in conjunction with the Berkeley UPC runtime and GASNet communication system. In addition to the standard UPC libraries, Clang UPC2C also provides access to Berkeley UPC library extensions.

Hargrove, Paul↗
DOE OSTI2022

Clang UPC Compiler (Clang UPC) v3.9.1-1

Clang Unified Parallel C (Clang UPC) provides a compilation and execution environment for programs written in the UPC (Unified Parallel C) language. The Clang UPC compiler extends the capabilities of the Clang LLVM C compiler to comply with the UPC Language Specification version 1.3. It includes support for UPC collectives and a configurable pointer-to-shared representation. The compiler generates programs that run on a wide variety of systems ranging from workstations to leadership-class supercomputers, in conjunction with the Berkeley UPC runtime and GASNet communication system. This compiler generates assembly code / object code directly for Intel processors and IBM PowerPC.

Hargrove, Paul↗
DOE OSTI2024

Clacc: OpenACC for C/C++ in Clang

The Clacc project has developed OpenACC compiler, runtime, and profiling interface support for C/C++ by extending Clang and LLVM. A key Clacc design feature is that it translates OpenACC to OpenMP to leverage the OpenMP offloading support that is actively being developed for Clang and LLVM. A benefit of this design is support for two compilation modes: traditional compilation mode produces a binary, and source-to-source mode produces OpenMP source. Clacc has been deployed on Oak Ridge National Laboratory’s (ORNL’s) Frontier, on which Clacc is the only OpenACC implementation for C/C++. Clacc supports x86_64, POWER9, AMD GPUs, and NVIDIA GPUs. Clacc’s OpenACC profiling interface support has been integrated with TAU, which is also deployed on Frontier. While Clacc has always supported C as a base language, Clacc also has increasing C++ support, including support for Kokkos’s OpenACC back end. Clacc itself is hosted publicly on GitHub. In this paper, we describe Clacc’s design and mapping from OpenACC directives to OpenMP. We also present a performance evaluation on ORNL’s Frontier (AMD MI250x GPU offload) and Argonne National Laboratory’s (ANL’s) Polaris (NVIDIA A100 GPU offload) for various SPEC ACCEL and Kokkos OpenACC back end benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization

Here, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning methods within Bayesian optimization and evaluate their effectiveness. We select six of the most complex PolyBench benchmarks and apply the newly developed LLVM Clang/Polly loop optimization pragmas to the benchmarks to optimize them. We then use the autotuning framework to optimize the pragma parameters to improve their performance. The experimental results show that our autotuning approach outperforms the other compiling methods to provide the smallest execution time for the benchmarks syr2k, 3mm, heat-3d, lu, and covariance with two large datasets in 200 code evaluations for effectively searching the parameter spaces with up to 170,368 different configurations. We find that the Floyd-Warshall benchmark did not benefit from autotuning. To cope with this issue, we provide some compiler option solutions to improve the performance. Then we present loop autotuning without a user's knowledge using a simple mctree autotuning framework to further improve the performance of the Floyd-Warshall benchmark. We also extend the ytopt autotuning framework to tune a deep learning application.

79 ASTRONOMY AND ASTROPHYSICS↗