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DOE OSTI · 1407089

Auto-Tuning Memory-Intensive Kernels for Multicore

Williams, Samuel W.·Datta, Kaushik·Oliker, Leonid·Carter, Jonathan·Shalf, John·Yelick, Katherine
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Abstract

In this, chapter, we discuss the optimization of three memory-intensive computational kernels — sparse matrix-vector multiplication, the Laplacian differential operator applied to structured grids, and the collision() operator with the lattice Boltzmann magnetohydrodynamics (LBMHD) application. They are all implemented using a single-process, (POSIX) threaded, SPMD model. Unlike their computationally-intense dense linear algebra cousins, performance is ultimately limited by DRAM bandwidth and the volume of data that must be transfered. To provide performance portability across current and future multicore architectures, we utilize automatic performance tuning, or auto-tuning.

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Williams, Samuel W., Datta, Kaushik, Oliker, Leonid, Carter, Jonathan, Shalf, John, Yelick, Katherine. 2010-11-23. Auto-Tuning Memory-Intensive Kernels for Multicore. https://doi.org/10.1201/b10509

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