Improving the efficiency of graph algorithm executions on high‐performance computing
Summary The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in...
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| Veröffentlicht in: | Concurrency and computation Jg. 35; H. 18 |
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| Sprache: | Englisch |
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15.08.2023
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| Abstract | Summary
The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKat
works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to 434.26×$$ 434.26\times $$) and energy‐saving (up to 245.21×$$ \times $$), and reduces the graph applications' execution time (up to 17.70×$$ 17.70\times $$) and energy consumption (up to 6.64×$$ \times $$) compared to the default execution of parallel applications on HPC systems. |
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| AbstractList | The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKatworks in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to 434.26×$$ 434.26\times $$) and energy‐saving (up to 245.21×$$ \times $$), and reduces the graph applications' execution time (up to 17.70×$$ 17.70\times $$) and energy consumption (up to 6.64×$$ \times $$) compared to the default execution of parallel applications on HPC systems. Summary The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKat works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to 434.26×$$ 434.26\times $$) and energy‐saving (up to 245.21×$$ \times $$), and reduces the graph applications' execution time (up to 17.70×$$ 17.70\times $$) and energy consumption (up to 6.64×$$ \times $$) compared to the default execution of parallel applications on HPC systems. The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat , a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKat works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to ) and energy‐saving (up to 245.21), and reduces the graph applications' execution time (up to ) and energy consumption (up to 6.64) compared to the default execution of parallel applications on HPC systems. |
| Author | Lorenzon, Arthur F. Schwarzrock, Janaina A. Rocha, Hiago Mayk G. Moori, Marcelo K. Beck, Antonio Carlos S. |
| Author_xml | – sequence: 1 givenname: Marcelo K. orcidid: 0000-0001-6011-610X surname: Moori fullname: Moori, Marcelo K. email: mkmoori@inf.ufrgs.br organization: Federal University of Rio Grande do Sul – sequence: 2 givenname: Hiago Mayk G. orcidid: 0000-0002-0827-0131 surname: A. Rocha fullname: A. Rocha, Hiago Mayk G. organization: Federal University of Rio Grande do Sul – sequence: 3 givenname: Janaina orcidid: 0000-0002-7070-1297 surname: Schwarzrock fullname: Schwarzrock, Janaina organization: Federal University of Rio Grande do Sul – sequence: 4 givenname: Arthur F. orcidid: 0000-0002-2412-3027 surname: Lorenzon fullname: Lorenzon, Arthur F. organization: Federal University of Pampa – sequence: 5 givenname: Antonio Carlos S. orcidid: 0000-0002-4492-1747 surname: Beck fullname: Beck, Antonio Carlos S. organization: Federal University of Rio Grande do Sul |
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| PublicationTitle | Concurrency and computation |
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The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly... The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular... |
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| SubjectTerms | Algorithms Efficiency Energy consumption energy‐saving graph analytics Graphs High performance computing Microprocessors Multiprocessing multiprogramming parallel graph processing system efficiency |
| Title | Improving the efficiency of graph algorithm executions on high‐performance computing |
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