MaSiF: Machine learning guided auto-tuning of Parallel Skeletons

We present MaSiF, a novel tool to auto-tune parallelization parameters of skeleton parallel programs. It reduces the cost of searching the optimization space using a combination of machine learning and linear dimensionality reduction. To auto-tune a new program, a set of program features is determined statically and used to compute k nearest neighbors from a set of training programs. Previously collected performance data for the nearest neighbors is used to reduce the size of the search space using Principal Components Analysis. This results in a set of eigenvectors that are used to search the reduced space. MaSiF achieves 88% of the performance of the oracle, which searches a random set of 10,000 parameter values. MaSiF searches just 45 points, or 0.45% of the optimization space, to achieve this performance. MaSiF provides an average speedup of 1.18x over parallelization parameters chosen by a human expert.