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Speculative Multithreading (SpMT) is a thread level automatic parallelization technique to accelerate sequential programs. Conventional thread partition algorithms primarily include heuristic-based and machine learning-based. The existing heuristic-based approaches are only suitable for one kind of programs and can not guarantee to get the optimal solution of thread partitioning, and the existing...
Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The start-of-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted...
Speculative multithreading (SpMT) is a thread-level automatic parallelization technique to accelerate sequential programs on multi-core. The existing heuristic-based approaches are only suitable for one kind of programs and cannot guarantee to get the optimal solution of thread partitioning. In this paper, we propose a novel thread partitioning approach based on machine learning to partition irregular...
Speculative multithreading (SpMT) is a thread level automatic parallelization technique to accelerate sequential programs. Since approaches based on heuristic rules only get the local optimal speculative thread solution and have reached their speedup performance limit, machine learning approaches have been introduced into speculative multithreading to avoid the shortcomings of the heuristic rules...
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