Pub Date : 2022-11-01DOI: 10.1109/ESPM256814.2022.00008
K. Huck
APEX (Autonomic Performance Environment for eXascale) is a performance measurement library for distributed, asynchronous multitasking runtime systems. It provides support for both lightweight measurement and high concurrency. To support performance measurement in systems that employ user-level threading, APEX uses a dependency chain in addition to the call stack to produce traces and task dependency graphs. APEX also provides a runtime adaptation system based on the observed system performance. In this paper, we describe the evolution of APEX from its design for HPX to support an array of programming models and abstraction layers and describe some of the features that have evolved to help understand the asynchrony and high concurrency of asynchronous tasking models.
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Pub Date : 2022-11-01DOI: 10.1109/ESPM256814.2022.00006
Valentin Le Fèvre, Tetsuzo Usui, Marc Casas
Sparse linear algebra routines are fundamental building blocks of a large variety of scientific applications. Direct solvers, which are methods for solving linear systems via the factorization of matrices into products of triangular matrices, are commonly used in many contexts. The Cholesky factorization is the fastest direct method for symmetric and positive definite matrices. This paper presents selective nesting, a method to determine the optimal task granularity for the parallel Cholesky factorization based on the structure of sparse matrices. We propose the Opt-D algorithm, which automatically and dynamically applies selective nesting. Opt-D leverages matrix sparsity to drive complex task-based parallel workloads in the context of direct solvers. We run an extensive evaluation campaign considering a heterogeneous set of 35 sparse matrices and a parallel machine featuring the A64FX processor. Opt-D delivers an average performance speedup of 1.75× with respect to the best state-of-the-art parallel methods to run direct solvers.
{"title":"A Selective Nesting Approach for the Sparse Multi-threaded Cholesky Factorization","authors":"Valentin Le Fèvre, Tetsuzo Usui, Marc Casas","doi":"10.1109/ESPM256814.2022.00006","DOIUrl":"https://doi.org/10.1109/ESPM256814.2022.00006","url":null,"abstract":"Sparse linear algebra routines are fundamental building blocks of a large variety of scientific applications. Direct solvers, which are methods for solving linear systems via the factorization of matrices into products of triangular matrices, are commonly used in many contexts. The Cholesky factorization is the fastest direct method for symmetric and positive definite matrices. This paper presents selective nesting, a method to determine the optimal task granularity for the parallel Cholesky factorization based on the structure of sparse matrices. We propose the Opt-D algorithm, which automatically and dynamically applies selective nesting. Opt-D leverages matrix sparsity to drive complex task-based parallel workloads in the context of direct solvers. We run an extensive evaluation campaign considering a heterogeneous set of 35 sparse matrices and a parallel machine featuring the A64FX processor. Opt-D delivers an average performance speedup of 1.75× with respect to the best state-of-the-art parallel methods to run direct solvers.","PeriodicalId":340754,"journal":{"name":"2022 IEEE/ACM 7th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)","volume":"25 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114037472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/ESPM256814.2022.00007
Gregor Daiß, Srinivas Yadav Singanaboina, Patrick Diehl, H. Kaiser, D. Pflüger
Octo-Tiger, a large-scale 3D AMR code for the merger of stars, uses a combination of HPX, Kokkos and explicit SIMD types, aiming to achieve performance-portability for a broad range of heterogeneous hardware. However, on A64FX CPUs, we encountered several missing pieces, hindering performance by causing problems with the SIMD vectorization. Therefore, we add std::experimental::simd as an option to use in Octo-Tiger’s Kokkos kernels alongside Kokkos SIMD, and further add a new SVE (Scalable Vector Extensions) SIMD backend. Additionally, we amend missing SIMD implementations in the Kokkos kernels within Octo-Tiger’s hydro solver. We test our changes by running Octo-Tiger on three different CPUs: An A64FX, an Intel Icelake and an AMD EPYC CPU, evaluating SIMD speedup and node-level performance. We get a good SIMD speedup on the A64FX CPU, as well as noticeable speedups on the other two CPU platforms. However, we also experience a scaling issue on the EPYC CPU.
{"title":"From Merging Frameworks to Merging Stars: Experiences using HPX, Kokkos and SIMD Types","authors":"Gregor Daiß, Srinivas Yadav Singanaboina, Patrick Diehl, H. Kaiser, D. Pflüger","doi":"10.1109/ESPM256814.2022.00007","DOIUrl":"https://doi.org/10.1109/ESPM256814.2022.00007","url":null,"abstract":"Octo-Tiger, a large-scale 3D AMR code for the merger of stars, uses a combination of HPX, Kokkos and explicit SIMD types, aiming to achieve performance-portability for a broad range of heterogeneous hardware. However, on A64FX CPUs, we encountered several missing pieces, hindering performance by causing problems with the SIMD vectorization. Therefore, we add std::experimental::simd as an option to use in Octo-Tiger’s Kokkos kernels alongside Kokkos SIMD, and further add a new SVE (Scalable Vector Extensions) SIMD backend. Additionally, we amend missing SIMD implementations in the Kokkos kernels within Octo-Tiger’s hydro solver. We test our changes by running Octo-Tiger on three different CPUs: An A64FX, an Intel Icelake and an AMD EPYC CPU, evaluating SIMD speedup and node-level performance. We get a good SIMD speedup on the A64FX CPU, as well as noticeable speedups on the other two CPU platforms. However, we also experience a scaling issue on the EPYC CPU.","PeriodicalId":340754,"journal":{"name":"2022 IEEE/ACM 7th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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