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The PEPPHER composition tool: performance-aware composition for GPU-based systems

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Abstract

The PEPPHER (EU FP7 project) component model defines the notion of component, interface and meta-data for homogeneous and heterogeneous parallel systems. In this paper, we describe and evaluate the PEPPHER composition tool, which explores the application’s components and their implementation variants, generates the necessary low-level code that interacts with the runtime system, and coordinates the native compilation and linking of the various code units to compose the overall application code to optimize performance. We discuss the concept of smart containers and its benefits for reducing dispatch overhead, exploiting implicit parallelism across component invocations and runtime optimization of data transfers. In an experimental evaluation with several applications, we demonstrate that the composition tool provides a high-level programming front-end while effectively utilizing the task-based PEPPHER runtime system (StarPU) underneath for different usage scenarios on GPU-based systems.

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Notes

  1. For demonstration purpose, we have used CUBLAS [4] and CUSP [5] components for CUDA implementations as shown in Sect. 5.

  2. As the PEPPHER runtime system is C based and the C language does not permit to call functions with varying types depending on the actual task being run.

  3. The read and write accesses to container data are distinguished by implementing proxy classes for element data in C++ [11].

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Acknowledgments

This work was funded by EU FP7, project PEPPHER, grant #248481 (http://www.pep-pher.eu) and by SeRC. We would like to thank University of Vienna for providing access to their machine.

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  1. PELAB, Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Usman Dastgeer, Lu Li & Christoph Kessler

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  1. Usman Dastgeer
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  2. Lu Li
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  3. Christoph Kessler
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Correspondence to Usman Dastgeer.

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Dastgeer, U., Li, L. & Kessler, C. The PEPPHER composition tool: performance-aware composition for GPU-based systems. Computing 96, 1195–1211 (2014). https://doi.org/10.1007/s00607-013-0371-8

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