Performance Modeling and Tuning Sample Clauses

Performance Modeling and Tuning. Porting applications is complicated by the heterogeneity of GPU-accelerated platforms with distributed memory, and the restrictions of the GPGPU programming models. Ryoo et al. have shown that the optimization space for CUDA programs can be highly non-linear, and highly challenging for model-based heuristics, performance modeling, or feedback-directed methods [39, 38]. G-ADAPT is a compiler framework to search and predict the best configuration for different input sizes for GPGPU programs [28]; it starts from already optimized code and aims to adapt the code to different input sizes. The previous methods are not fully automatic or integrated into a GPGPU compiler. Progress towards fully automatic performance tuning have been achieved by the Crest [43] and CHiLL [17] projects. Rather than targeting a low-level programming model like OpenCL or CUDA, Apricot [35] focuses on the automatic insertion of offload constructs, targeting XXX. It rounds some of the performance tuning difficulties by performing selective offloading at runtime. As the hardware and tool flow mature, we believe the performance tuning aspects will become more and more important and challenging.
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