Iterative machine learning (IterML) for effective parameter pruning and tuning in accelerators
Xuewen Cui, Wu-chun Feng · 2019
With the rise of accelerators (e.g., GPUs, FPGAs, and APUs) in computing systems, the parallel computing community needs better tools and mechanisms with which to productively extract performance. While modern compilers provide flags to activate different optimizations to improve performance, the effectiveness of such automated optimization depends on the algorithm and its mapping to the underlying accelerator architecture. Currently, however, extracting the best performance from an algorithm on an accelerator requires significant expertise and manual effort to exploit both spatial and temporal sharing of computing resources in order to improve overall performance. In particular, maximizing the performance on an algorithm on an accelerator requires extensive hyperparameter (e.g., thread-block size) selection and tuning. Given the myriad of hyperparameter dimensions to optimize across, the search space of optimizations is generally extremely large, making it infeasible to exhaustively evaluate each optimization configuration.