Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
On-the-fly elimination of dynamic irregularities for GPU computing
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
PACT '11 Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques
Characterizing and improving the use of demand-fetched caches in GPUs
Proceedings of the 26th ACM international conference on Supercomputing
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
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Data cache is introduced to GPUs to mitigate the irregular memory access problem. But few studies have investigated how to exploit its full potential. In this work, we consider some important GPU applications that feature data sharing across thread blocks. We show that the sharing is not well exploited because current GPU runtime ignores such a factor when scheduling threads. We then present an application-level transformation to remap thread blocks to data on the fly. With the software-level scheduler, thread blocks with much data sharing are scheduled to share the cache on a streaming multiprocessor (SM). Experiments on four benchmarks show 1.23X speedup on average.