The objective of the Bench-testing Environment for Automated Software Tuning (BEAST) project is to embrace the nature of accelerators, such as Graphics Processing Units (GPUs) from NVIDIA and AMD, and Xeon Phi coprocessors from Intel, which offer an order of magnitude more computing power and an order of magnitude more memory bandwidth than standard processors, and create an unprecedented opportunity for breakthroughs in science and technology.
Accelerators are a different kind of beast when it comes to performance tuning, and their architectural features usually pose unique programming challenges. For example, accelerators have massive numbers of simple cores, with static pipelines, and no branch prediction, and a multitude of constraints that can obliterate performance in numerous situations. BEAST allows a user to write high performance kernels in a tunable manner, sweep through a large search space, collect massive amounts of performance data, and plow through that data with machine learning techniques. These features enable a user to optimize code for extreme performance, without descending into the dark abyss of assembly programming.
Find out more at http://icl.utk.edu/beast/
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