SC Conference - Activity Details

Cy P Chan  (Massachusetts Institute of Technology)

Jason Ansel  (Massachusetts Institute of Technology)

Yee Lok Wong  (Massachusetts Institute of Technology)

Saman Amarasinghe  (Massachusetts Institute of Technology)

Alan Edelman  (Massachusetts Institute of Technology)

Autotuning and Compilers

Tuesday,  01:30PM - 02:00PM

Room PB256

Algorithmic choice is essential in any problem domain to realizing optimal computational performance. We present a programming language and autotuning system that address issues of algorithmic choice, accuracy requirements, and portability for multigrid applications in a near-optimal and efficient manner. We search the space of algorithmic choices and cycle shapes efficiently by utilizing a novel dynamic programming method to build tuned algorithms from the bottom up. The results are optimized multigrid algorithms that invest targeted computational power to yield the accuracy required by the user. Our implementation uses PetaBricks, an implicitly parallel programming language where algorithmic choices are exposed in the language. The PetaBricks compiler uses these choices to analyze, autotune, and verify the PetaBricks program. These language features, most notably the autotuner, were key in enabling our implementation to be clear, correct, and fast.

The full paper can be found in the ACM Digital Library and IEEE Computer Society