Abstract
Numerous challenges have to be mastered as applications in scientific computing are being developed for post-petascale parallel systems. While ample parallelism is usually available in the numerical problems at hand, the efficient use of supercomputer resources requires not only good scalability but also a verifiably effective use of resources on the core, the processor, and the accelerator level. Furthermore, power dissipation and energy consumption are becoming further optimization targets besides time-to-solution. Performance Engineering (PE) is the pivotal strategy for developing effective parallel code on all levels of modern architectures. In this paper we report on the development and use of low-level parallel building blocks in the GHOST library (“General, Hybrid, and Optimized Sparse Toolkit”). We demonstrate the use of PE in optimizing a density of states computation using the Kernel Polynomial Method, and show that reduction of runtime and reduction of energy are literally the same goal in this case. We also give a brief overview of the capabilities of GHOST and the applications in which it is being used successfully.
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The term “MPI+X” denotes the combination of the Message Passing Interface (MPI) and a node-level programming model.
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http://www.mcs.anl.gov/petsc/features/gpus.html, accessed 02-16-2016
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Acknowledgements
The research reported here was funded by Deutsche Forschungsgemeinschaft via the priority program 1648 “Software for Exascale Computing” (SPPEXA). The authors gratefully acknowledge support by the Gauss Centre for Supercomputing e.V. (GCS) for providing computing time on their SuperMUC system at Leibniz Supercomputing Centre through project pr84pi, and by the CSCS Lugano for providing access to their Piz Daint supercomputer. Work at Los Alamos is performed under the auspices of the USDOE.
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Kreutzer, M. et al. (2016). Performance Engineering and Energy Efficiency of Building Blocks for Large, Sparse Eigenvalue Computations on Heterogeneous Supercomputers. In: Bungartz, HJ., Neumann, P., Nagel, W. (eds) Software for Exascale Computing - SPPEXA 2013-2015. Lecture Notes in Computational Science and Engineering, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-40528-5_14
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