Solving Parameter Selection Problem of Helper Thread Prefetching via Realtime Hardware Performance Monitoring

Helper thread prefetching have the potential of improving the performance of irregular data intensive applications, but the prefetching effect depends on how efficiently and swiftly the control parameters can be selected. The parameter selection and optimization was done by executing the application exhaustively in prior works. In this study, we propose a helper thread prefetching control framework, which adjusts the control parameters of helper thread automatically, called HPCF. We present the idea, initial design and implementation of HPCF. In particular, we establish a dynamic control model of helper thread prefetching and develop a two-level parameter selection algorithm. We evaluate the proposed HPCF framework on commodity multi-core platforms by using selected benchmarks which come from SPEC2006, Olden and SSCA2. Results show that our approach performs almost equal to our prior static Skip Helper Thread prefetching scheme, while the parameter selection was done by executing the application only once. And it achieves up to 33.3%, 18.2% and 18.6% performance improvement for MST, MCF, and SSCA2 benchmarks, respectively.