Mapping a Single Assignment Programming Language to Reconfigurable Systems
The Journal of Supercomputing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Synthesizing Transformations for Locality Enhancement of Imperfectly-Nested Loop Nests
International Journal of Parallel Programming
A practical automatic polyhedral parallelizer and locality optimizer
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Edge-centric modulo scheduling for coarse-grained reconfigurable architectures
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Architecture enhancements for the ADRES coarse-grained reconfigurable array
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
EPIMap: using epimorphism to map applications on CGRAs
Proceedings of the 49th Annual Design Automation Conference
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The coarse-grained reconfigurable architecture (CGRA) is a promising platform that provides both high performance and high power-efficiency. The compute-intensive portions of an application (e.g. loops) are often mapped onto CGRA for acceleration. To optimize the mapping of loop nests to CGRA, this paper makes two contributions: i) Establishing a precise CGRA performance model and formulating the loop nests mapping as a nonlinear optimization problem based on polyhedral model, ii) Extracting an efficient heuristic loop transformation and mapping algorithm (PolyMAP) to improve mapping performance. Experiment results on most kernels of the PolyBench and real-life applications show that our proposed approach can improve the performance of the kernels by 21% on average, as compared to one of the best existing mapping algorithm, EPIMap. The runtime complexity of PolyMAP is also acceptable.