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directive-based GPU programming for computational fluid dynamics

COMPUTERS & FLUIDS 2015年 114卷 242-253页

作者： Pickering, Brent P. Jackson, Charles W. Scogland, Thomas R. W. Feng, Wu-Chun Roy, Christopher J. Virginia Tech Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA

directive-based programming of graphics processing units (CPUs) has recently appeared as *** alter. native to using specialized low-level languages such as CUDA C and OpenCL for general-purpose GPU programming. This technique, which uses "directive" or "pragma" statements to annotate source codes written in traditional high-level languages, is designed to permit a unified code base to serve multiple computational platforms. In this work we analyze the popular OpenACC programming standard, as implemented by the PGI compiler suite, in order to evaluate its utility and performance potential in computational fluid dynamics (CFD) applications. We examine the process of applying the OpenACC Fortran API to a test CFD code that serves as a proxy for a full-scale research code developed at Virginia Tech;this test code is used to asses the performance improvements attainable for our CFD algorithm on common GPU platforms, as well as to determine the modifications that must be made to the original source code in order to run efficiently on the GPU. Performance is measured on several recent GPU architectures from NVIDIA and AMD (using both double and single precision arithmetic) and the accelerator code is bench-marked against a multithreaded CPU version constructed from the same Fortran source code using OpenMP directives. A single NVIDIA Kepler CPU card is found to perform approximately 20x faster than a single CPU core and more than 2x faster than a 16-core Xeon server. An analysis of optimization techniques for OpenACC reveals cases in which manual intervention by the programmer can improve accelerator performance by up to 30% over the default compiler heuristics, although these optimizations are relevant only for specific platforms. Additionally, the use of multiple accelerators with OpenACC is investigated, including an experimental high-level interface for multi-GPU programming that automates scheduling tasks across multiple devices. While the overall performance of

关键词： Graphics processing unit (GPU) directive-based programming OpenACC Fortran Finite-difference method

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ACC Saturator: Automatic Kernel Optimization for directive-based GPU Code

ACC Saturator: Automatic Kernel Optimization for Directive-B...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Matsumura, Kazuaki De Gonzalo, Simon Garcia Pena, Antonio J. Spain Sandia National Laboratories United States

ISBN: (纸本)9798350355543

Automatic code optimization is a complex process that typically involves the application of multiple discrete algorithms that modify the program structure irreversibly. However, the design of these algorithms is often monolithic, and they require repetitive implementation to perform similar analyses due to the lack of cooperation. To address this issue, modern optimization techniques, such as equality saturation, allow for exhaustive term rewriting at various levels of inputs, thereby simplifying compiler *** this paper, we propose equality saturation to optimize sequential codes utilized in directive-based programming for GPUs. Our approach realizes less computation, less memory access, and high memory throughput simultaneously. Our fully-automated framework constructs single-assignment forms from inputs to be entirely rewritten while keeping dependencies and extracts optimal cases. Through practical benchmarks, we demonstrate a significant performance improvement on several compilers. Furthermore, we highlight the advantages of computational reordering and emphasize the significance of memory-access order for modern GPUs. © 2024 IEEE.

关键词： Code Generation Compiler directive-based programming GPUs Program Optimization

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Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs 7th

Performance Assessment of OpenMP Compilers Targeting NVIDIA ...

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7th International Workshop on Accelerator programming using directives (WACCPD)

作者： Davis, Joshua Hoke Daley, Christopher Pophale, Swaroop Huber, Thomas Chandrasekaran, Sunita Wright, Nicholas J. Univ Delaware Newark DE 19716 USA Lawrence Berkeley Natl Lab Natl Energy Res Sci Comp Ctr Berkeley CA 94720 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9783030742232;9783030742249

Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today's systems to tomorrow's. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other;a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC's Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

关键词： directive-based programming Performance portability Heterogeneous systems OpenMP GPU NVIDIA V100

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JACC: An OpenACC Runtime Framework with Kernel-Level and Multi-GPU Parallelization 28

JACC: An OpenACC Runtime Framework with Kernel-Level and Mul...

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28th Annual IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Matsumura, Kazuaki de Gonzalo, Simon Garcia Pena, Antonio J. Barcelona Supercomp Ctr BSC Barcelona Spain

ISBN: (纸本)9781665410168

The rapid development in computing technology has paved the way for directive-based programming models towards a principal role in maintaining software portability of performance-critical applications. Efforts on such models involve a least engineering cost for enabling computational acceleration on multiple architectures while programmers are only required to add meta information upon sequential code. Optimizations for obtaining the best possible efficiency, however, are often challenging. The insertions of directives by the programmer can lead 10 side-effects that limit the available compiler optimization possible, which could result in performance degradation. This is exacerbated when targeting multi-GPU systems, as pragmas do not automatically adapt to such systems, and require expensive and lime consuming code adjustment by programmers. This paper introduces JACC, an OpenACC runtime framework which enables the dynamic extension of OpenACC programs by serving as a transparent layer between the program and the compiler. We add a versatile code-translation method for multi-device utilization by which manually-optimized applications can he distributed automatically while keeping original code structure and parallelism. We show in some cases nearly linear scaling on the part of kernel execution with the NVIDIA V100 GPUs. While adaptively using multi-GPUs, the resulting performance improvements amortize the latency of GPU-to-GPU communications.

关键词： Multi-GPUs Runtime System Code Generation directive-based programming

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directive-based, High-Level programming and Optimizations for High-Performance Computing with FPGAs 18

Directive-Based, High-Level Programming and Optimizations fo...

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32nd International Conference on Supercomputing (ICS)

作者： Lambert, Jacob Lee, Seyong Kim, Jungwon Vetter, Jeffrey S. Malony, Allen D. Univ Oregon Eugene OR 97403 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781450357838

Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations from several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing, primarily because of their programming complexity and difficulties in optimizing performance. In this paper, we present a directive-based, high-level optimization framework for high-performance computing with FPGAs, built on top of an OpenACC-to-FPGA translation framework called OpenARC. We propose directive extensions and corresponding compile-time optimization techniques to enable the compiler to generate more efficient FPGA hardware configuration files. Empirical evaluation of the proposed framework on an Intel Stratix V with five OpenACC benchmarks from various application domains shows that FPGA-specific optimizations can lead to significant increases in performance across all tested applications. We also demonstrate that applying these high-level directive-based optimizations can allow OpenACC applications to perform similarly to lower-level OpenCL applications with hand-written FPGA-specific optimizations, and offer runtime and power performance benefits compared to CPUs and GPUs.

关键词： OpenACC FPGA OpenCL directive-based programming reconfigurable computing OpenARC sliding window

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Optimization with the OpenACC-to-FPGA framework on the Arria 10 and Stratix 10 FPGAs

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PARALLEL COMPUTING 2021年 104卷 102784-102784页

作者： Lambert, Jacob Lee, Seyong Vetter, Jeffrey S. Malony, Allen D. Univ Oregon Eugene OR 97403 USA Oak Ridge Natl Lab Oak Ridge TN USA

The reconfigurable computing paradigm with field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. We perform this exploration on Intel Arria 10 and Stratix 10 FPGAs. We also explored the relationships between performance, resource usages, and compilation times, and investigated implications for performance portability. Finally, we present an initial evaluation of a real-world proxy application, LULESH.

关键词： FPGA OpenACC OpenARC directive-based programming Compiler optimization

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In-Depth Optimization with the OpenACC-to-FPGA Framework on an Arria 10 FPGA 34

In-Depth Optimization with the OpenACC-to-FPGA Framework on ...

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34th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Lambert, Jacob Lee, Seyong Vetter, Jeffrey S. Malony, Allen Univ Oregon Eugene OR 97403 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174457

The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.

关键词： FPGA OpenACC OpenARC directive-based programming compiler optimization

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CCAMP: OpenMP and OpenACC Interoperable Framework 25th

CCAMP: OpenMP and OpenACC Interoperable Framework

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25th International Conference on Parallel and Distributed Computing (Euro-Par)

作者： Lambert, Jacob Lee, Seyong Malony, Allen Vetter, Jeffrey S. Univ Oregon Eugene OR 97403 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9783030483401;9783030483395

Heterogeneous systems have become a staple of the HPC environment. Several directive-based solutions, such as OpenMP and OpenACC, have been developed to alleviate the challenges of programming heterogeneous systems, and these standards strive to provide a single portable programming solution across heterogeneous environments. However, in many ways this goal has yet to be realized due to device-specific implementations and different levels of language support across compilers. In this framework we aim to analyze and address the different levels of optimization and compatibility between OpenACC and OpenMP programs and device compilers. We introduce the CCAMP framework, built on the OpenARC compiler, which implements language translation between OpenACC and OpenMP, with the goal of exploiting the maturity of different device-specific compilers to maximize performance for a given architecture. We show that CCAMP allows us to generate code for a specific device-compiler combination given a device-agnostic OpenMP or OpenACC program, allowing compilation and execution of programs with specific directives on otherwise incompatible devices. CCAMP also provides a starting point for a more advanced interoperable framework that can effectively provide directive translation and device, compiler, and application specific optimizations.

关键词： OpenMP OpenACC directive-based programming Heterogeneous computing CCAMP

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Multi-level Parallelism with MPI and OpenACC for CFD Applications

Multi-level Parallelism with MPI and OpenACC for CFD Applica...

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作者： McCall, Andrew James Virginia Tech | University

High-level parallel programming approaches, such as OpenACC, have recently become popular in complex fluid dynamics research since they are cross-platform and easy to implement. OpenACC is a directive-based programming model that, unlike low-level programming models, abstracts the details of implementation on the GPU. Although OpenACC generally limits the performance of the GPU, this model significantly reduces the work required to port an existing code to any accelerator platform, including GPUs. The purpose of this research is twofold: to investigate the effectiveness of OpenACC in developing a portable and maintainable GPU-accelerated code, and to determine the capability of OpenACC to accelerate large, complex programs on the GPU. In both of these studies, the OpenACC implementation is optimized and extended to a multi-GPU implementation while maintaining a unified code base. OpenACC is shown as a viable option for GPU computing with CFD problems. In the first study, a CFD code that solves incompressible cavity flows is accelerated using OpenACC. Overlapping communication with computation improves performance for the multi-GPU implementation by up to 21%, achieving up to 400 times faster performance than a single CPU and 99% weak scalability efficiency with 32 GPUs. The second study ports the execution of a more complex CFD research code to the GPU using OpenACC. Challenges using OpenACC with modern Fortran are discussed. Three test cases are used to evaluate performance and scalability. The multi-GPU performance using 27 GPUs is up to 100 times faster than a single CPU and maintains a weak scalability efficiency of 95%.

关键词： Graphics processing unit directive-based programming OpenACC Lid-driven cavity Multi-GPU Parallel computing Thesis

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OpenACC to FPGA: A Framework for directive-based High-Performance Reconfigurable Computing 30

OpenACC to FPGA: A Framework for Directive-based High-Perfor...

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30th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Lee, Seyong Kim, Jungwon Vetter, Jeffrey S. Oak Ridge Natl Lab POB 2008 MS-6173 Oak Ridge TN 37831 USA

ISBN: (纸本)9781509021406

This paper presents a directive-based, high-level programming framework for high-performance reconfigurable computing. It takes a standard, portable OpenACC C program as input and generates a hardware configuration file for execution on FPGAs. We implemented this prototype system using our open-source OpenARC compiler;it performs source-to-source translation and optimization of the input OpenACC program into an OpenCL code, which is further compiled into a FPGA program by the backend Altera Offline OpenCL compiler. Internally, the design of OpenARC uses a high-level intermediate representation that separates concerns of program representation from underlying architectures, which facilitates portability of OpenARC. In fact, this design allowed us to create the OpenACC-to-FPGA translation framework with minimal extensions to our existing system. In addition, we show that our proposed FPGA-specific compiler optimizations and novel OpenACC pragma extensions assist the compiler in generating more efficient FPGA hardware configuration files. Our empirical evaluation on an Altera Stratix V FPGA with eight OpenACC benchmarks demonstrate the benefits of our strategy. To demonstrate the portability of OpenARC, we show results for the same benchmarks executing on other heterogeneous platforms, including NVIDIA GPUs, AMD GPUs, and Intel Xeon Phis. This initial evidence helps support the goal of using a directive-based, high-level programming strategy for performance portability across heterogeneous HPC architectures.

关键词： OpenACC FPGA OpenCL directive-based programming reconfigurable computing OpenARC

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