A Comprehensive Comparison and Analysis of OpenACC and OpenMP 4.5 for NVIDIA GPUs

Usha, R.; Pandey, Prachi; Mangala, N.

doi:10.1109/hpec43674.2020.9286203

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…OpenACC and OpenMP are not equivalent, and each of them has its own advantages and disadvantages. The references [5,11,[15][16][17][18][19][20] provide additional details regarding the comparison of the two programming models. Several of these studies show that OpenACC is efficient at using accelerators.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Basloom¹,

Dahab²,

AL-Ghamdi³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems (HPCs) to achieve exascale by applying parallelism at multiple levels. Combining different programming paradigms, such as Message Passing Interface (MPI), Open Multiple Processing (OpenMP), and Open Accelerators (OpenACC), can increase computation speed and improve performance. During the integration of multiple models, the probability of runtime errors increases, making their detection difficult, especially in the absence of testing techniques that can detect these errors. Numerous studies have been conducted to identify these errors, but no technique exists for detecting errors in three-level programming models. Despite the increasing research that integrates the three programming models, MPI, OpenMP, and OpenACC, a testing technology to detect runtime errors, such as deadlocks and race conditions, which can arise from this integration has not been developed. Therefore, this paper begins with a definition and explanation of runtime errors that result from integrating the three programming models that compilers cannot detect. For the first time, this paper presents a classification of operational errors that can result from the integration of the three models. This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++ programming language that uses the triple programming models MPI, OpenMP, and OpenACC. This hybrid technology combines static technology and dynamic technology, given that some errors can be detected using static techniques, whereas others can be detected using dynamic technology. The hybrid technique can detect more errors because it combines two distinct technologies. The proposed static technology detects a wide range of error types in less time, whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC, 2023, vol.74, no.2 operating environment are left to the dynamic technology, which completes the validation.

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Section: Introductionmentioning

confidence: 99%

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Basloom¹,

Dahab²,

AL-Ghamdi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Errors Classification and Static Detection Techniques for Dual-Programming Model (OpenMP and OpenACC)

et al. 2022

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Recently, incorporating more than one programming model into a system designed for high performance computing (HPC) has become a popular solution to implementing parallel systems. Since traditional programming languages, such as C, C++, and Fortran, do not support parallelism, many programmers add one or more programming models to achieve parallelism and accelerate computation efficiently. These models include Open Accelerators (OpenACC) and Open Multi-Processing (OpenMP), which have recently been used with various models, including Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA). Due to the difficulty of predicting the behavior of threads, runtime errors cannot be predicted. The compiler cannot identify runtime errors such as data races, race conditions, deadlocks, or livelocks. Many studies have been conducted on the development of testing tools to detect runtime errors when using programming models, such as the combinations of OpenACC with MPI models and OpenMP with MPI. Although more applications use OpenACC and OpenMP together, no testing tools have been developed to test these applications to date. This paper presents a testing tool for detecting runtime using a static testing technique. This tool can detect actual and potential runtime errors during the integration of the OpenACC and OpenMP models into systems developed in C++. This tool implement error dependency graphs, which are proposed in this paper. Additionally, a dependency graph of the errors is provided, along with a classification of runtime errors that result from combining the two programming models mentioned earlier.

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Leveraging Compiler-Based Translation to Evaluate a Diversity of Exascale Platforms

Lambert

Monil

Lee

et al. 2022

2022 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)

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A Comprehensive Comparison and Analysis of OpenACC and OpenMP 4.5 for NVIDIA GPUs

Cited by 5 publications

References 13 publications

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Errors Classification and Static Detection Techniques for Dual-Programming Model (OpenMP and OpenACC)

Leveraging Compiler-Based Translation to Evaluate a Diversity of Exascale Platforms

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