PARSECSs

Author:

Chasapis Dimitrios1,Casas Marc1,Moretó Miquel1,Vidal Raul1,Ayguadé Eduard1,Labarta Jesús1,Valero Mateo1

Affiliation:

1. Barcelona Supercomputing Center and Universitat Politecnica de Catalunya--BarcelonaTech, Barcelona, Spain

Abstract

In this work, we show how parallel applications can be implemented efficiently using task parallelism. We also evaluate the benefits of such parallel paradigm with respect to other approaches. We use the PARSEC benchmark suite as our test bed, which includes applications representative of a wide range of domains from HPC to desktop and server applications. We adopt different parallelization techniques, tailored to the needs of each application, to fully exploit the task-based model. Our evaluation shows that task parallelism achieves better performance than thread-based parallelization models, such as Pthreads. Our experimental results show that we can obtain scalability improvements up to 42% on a 16-core system and code size reductions up to 81%. Such reductions are achieved by removing from the source code application specific schedulers or thread pooling systems and transferring these responsibilities to the runtime system software.

Funder

Spanish Miinistry of Science and Innovation

Eropean Unions 7th FP, ERC

Spanish Goverment Severo Ochoa

Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Goverment of Catalonia

Juan de la Cierva post-doctoral fellowship

Marie Curie Actions of the 7th R&D Framework Programme of teh European Union

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Information Systems,Software

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2. An Interplay of Energy and Temperature Minimization Techniques for Heterogeneous Multiprocessor Systems;TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON);2023-10-31

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4. Studying the expressiveness and performance of parallelization abstractions for linear pipelines;Proceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores;2023-02-25

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