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Chapel is an emerging scalable, productive parallel programming language. In this work, we analyze Chapel's performance using The Parallel Research Kernels on two different manycore architectures including a state-of-the-art Intel Knights Landing processor. We discuss implementation techniques in Chapel and their relation to the OpenMP implementations of the PRK. We also suggest and prototype several...
We explore the use of synthetic benchmarks for the training phase of machine-learning-based automatic performance tuning. We focus on the problem of predicting if the use of local memory on a GPU is beneficial for caching a single target array in a GPU kernel. We show that the use of only 13 real benchmarks leads to poor prediction accuracy (about to 58%) of the 13 leave-one-out models trained using...
We present a novel strategy for automatic performance tuning of GPU computational kernels. The strategy combines heuristic search with regression trees to prune the optimization space. It samples configurations in the space and uses these samples to build a regression tree. It then focuses the search on the leaf region of the tree with the best mean sample performance. Additional configurations are...
Equipped with the Chinese home-grown SW26010 many-core processor, TaihuLight claims the top place in the TOP500 list released in June 2016. Although some large-scale applications have been successfully running on the supercomputer, few studies have been conducted to analyze the performance impact caused by the extreme memory-bound architecture design. To facilitate native in-depth optimizations and...
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