Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

Jarmusch, Aaron; Liu, Aaron; Munley, Christian; Horta, Daniel; Ravichandran, Vaidhyanathan; Denny, Joel E.; Friedline, Kyle; Chandrasekaran, Sunita

doi:10.1109/waccpd56842.2022.00006

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…For example the some support category, which is mostly used for incomplete support in Figure 1. OpenACC C++ support on NVIDIA GPUs (7) was rated complete, while OpenMP C++ support showed some ambivalence (9). Here, the assessment was made because NVIDIA is upfront in acknowledging that some features of OpenMP for GPU offloading are still missing.…”

Section: Method Categoriesmentioning

confidence: 99%

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Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Herten

2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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In recent history, GPUs became a key driver of compute performance in HPC. With the installation of the Frontier supercomputer, they became the enablers of the Exascale era; further largest-scale installations are in progress (Aurora, El Capitan, JUPITER). But the early-day dominance by NVIDIA and their CUDA programming model has changed: The current HPC GPU landscape features three vendors (AMD, Intel, NVIDIA), each with native and derived programming models. The choices are ample, but not all models are supported on all platforms, especially if support for Fortran is needed; in addition, some restrictions might apply. It is hard for scientific programmers to navigate this abundance of choices and limits. This paper gives a guide by matching the GPU platforms with supported programming models, presented in a concise table and further elaborated in detailed comments. An assessment is made regarding the level of support of a model on a platform.

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Section: Method Categoriesmentioning

confidence: 99%

“…A very detailed comparison of GPU support through various OpenMP-capable compilers was given at the 2022 ECP Community BOF Days [7]. Deeper, more technical insights can be gained by dedicated validation suites [8,9]. Further examples are discussed in section 4.…”

Section: Related Workmentioning

confidence: 99%

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Herten

2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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“…The alternatives include gcc compiler, which supports OpenACC from version 10 and Clacc based on Clang and LLVM [33]. However, those OpenACC compiler implementations are not stable, bug-free, and well documented, as reported in several studies such as [28,34,35], which indicate the highest conformance of the PGI compiler with the OpenACC standard.…”

Section: Related Workmentioning

confidence: 99%

“…OpenACC specification has been constantly improving. The current specification 3.2 includes a number of new features [35], such as enhanced support for C++ lambda functions, array reduction operators, new constructs, and similar. New features have also been proposed by the research community, such as the concept of static graphs [41].…”

Section: Openacc Programming Modelmentioning

confidence: 99%

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Đukić,

Mišić

2023

Electronics

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Heterogeneous architectures consisting of both central processing units and graphics processing units are common in contemporary computer systems. For that reason, several programming models have been developed to exploit available parallelism, such as low-level CUDA and OpenCL, and directive-based OpenMP and OpenACC. In this paper we explore and evaluate the applicability of OpenACC, which is a directive-based programming model for GPUs. We focus both on the performance and programming effort needed to parallelize the existing sequential algorithms for GPU execution. The evaluation is based on the benchmark suite Parboil, which consists of 11 different mini-applications from different scientific domains, both compute- and memory-bound. The results show that mini-apps parallelized with OpenACC can achieve significant speedups over sequential implementations and in some cases, even outperform CUDA implementations. Furthermore, there is less of a programming effort compared to low-level models, such as CUDA and OpenCL, because a majority of the work is left to the compiler and overall, the code needs less restructuring.

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Method for scalable and performant GPU-accelerated simulation of multiphase compressible flow

Radhakrishnan,

Le Berre,

Wilfong

et al. 2024

Computer Physics Communications

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Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

Cited by 3 publications

References 16 publications

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Method for scalable and performant GPU-accelerated simulation of multiphase compressible flow

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