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Performance and energy effects on task-based parallelized applications

User-directed versus manual vectorization

The Journal of Supercomputing volume 74, pages 2627–2637 (2018)Cite this article

Abstract

Heterogeneity, parallelization and vectorization are key techniques to improve the performance and energy efficiency of modern computing systems. However, programming and maintaining code for these architectures poses a huge challenge due to the ever-increasing architecture complexity. Task-based environments hide most of this complexity, improving scalability and usage of the available resources. In these environments, while there has been a lot of effort to ease parallelization and improve the usage of heterogeneous resources, vectorization has been considered a secondary objective. Furthermore, there has been a swift and unstoppable burst of vector architectures at all market segments, from embedded to HPC. Vectorization can no longer be ignored, but manual vectorization is tedious, error-prone and not practical for the average programmer. This work evaluates the feasibility of user-directed vectorization in task-based applications. Our evaluation is based on the OmpSs programming model, extended to support user-directed vectorization for different SIMD architectures (i.e., SSE, AVX2, AVX512). Results show that user-directed codes achieve manually optimized code performance and energy efficiency with minimal code modifications, favoring portability across different SIMD architectures.

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Authors and Affiliations

  1. Computer Systems Laboratory, Cornell University, Ithaca, NY, 14853, USA

    Helena Caminal

  2. Barcelona Supercomputing Center (BSC), Barcelona, Spain

    Diego Caballero, Juan M. Cebrián, Roger Ferrer, Marc Casas, Miquel Moretó, Xavier Martorell & Mateo Valero

  3. Universitat Politecnica de Catalunya (UPC), Barcelona, Spain

    Miquel Moretó

Authors
  1. Helena Caminal
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  2. Diego Caballero
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  3. Juan M. Cebrián
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  4. Roger Ferrer
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  5. Marc Casas
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  6. Miquel Moretó
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  7. Xavier Martorell
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  8. Mateo Valero
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Corresponding author

Correspondence to Helena Caminal.

Additional information

This work was done at Barcelona Supercomputing Center (BSC).

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Caminal, H., Caballero, D., Cebrián, J.M. et al. Performance and energy effects on task-based parallelized applications. J Supercomput 74, 2627–2637 (2018). https://doi.org/10.1007/s11227-018-2294-9

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  • DOI: https://doi.org/10.1007/s11227-018-2294-9

Keywords

  • Data-level parallelism
  • Task-level parallelism
  • Vectorization
  • Energy efficiency