Shaping data for application performance and energy optimization in dynamic data view framework

Memory access bottlenecks are often due to the result of mismatch between the processor hardware's view of data and the algorithmic/logical view of data. This variation in data views is especially more pronounced in applications involving large datasets, leading to significantly increased latency and user response times. Previous attempts to tackle this problem were primarily targeted at execution time optimization. We present a dynamic technique piggybacked on the classical dynamic binary optimization (DBO) to shape the data view for each program phase differently resulting in program execution time reduction along with reductions in access energy. Our implementation rearranges non-adjacent data into a contiguous dataview. It uses wrappers to replace irregular data access patterns with spatially local dataview. DDV (Dynamic data view), a runtime dynamic binary translation and optimization framework has been used to perform runtime instrumentation and dynamic data optimization to achieve this goal. This scheme not only ensures a reduced program execution time, but also results in lower cache access energy. Some of the commonly used benchmarks from the SPEC 2006 and SPLASH-2 suite were profiled to determine irregular data accesses from procedures which contributed heavily to the overall execution time. Wrappers built to replace these accesses with spatially adjacent data led to a significant improvement in the total execution time. On average, 20% reduction in time was achieved along with a 5% reduction in energy for SPEC 2006 and 11% reduction in time was achieved along with a 6% reduction in energy for SPLASH-2.