Modeling Shared Cache Performance of OpenMP Programs using Reuse Distance
This work addresses performance modeling for parallel applications in computational co-design, though it appears incremental as it builds on existing reuse distance methods.
The paper tackles the challenge of modeling shared cache performance for OpenMP programs on multicore computers by developing a Scalable Analytical Shared Memory Model that predicts cache hit-rates using reuse distance profiles, with results showing accurate predictions.
Performance modeling of parallel applications on multicore computers remains a challenge in computational co-design due to the complex design of multicore processors including private and shared memory hierarchies. We present a Scalable Analytical Shared Memory Model to predict the performance of parallel applications that runs on a multicore computer and shares the same level of cache in the hierarchy. This model uses a computationally efficient, probabilistic method to predict the reuse distance profiles, where reuse distance is a hardware architecture-independent measure of the patterns of virtual memory accesses. It relies on a stochastic, static basic block-level analysis of reuse profiles measured from the memory traces of applications ran sequentially on small instances rather than using a multi-threaded trace. The results indicate that the hit-rate predictions on the shared cache are accurate.