  Performance modeling of parallel applications on multicore processors remains
a challenge in computational co-design due to multicore processors' complex
design. Multicores include complex private and shared memory hierarchies. We
present a Scalable Analytical Shared Memory Model (SASMM). SASMM can predict
the performance of parallel applications running on a multicore. SASMM uses a
probabilistic and computationally-efficient method to predict the reuse
distance profiles of caches in multicores. SASMM relies on a stochastic, static
basic block-level analysis of reuse profiles. The profiles are calculated from
the memory traces of applications that run sequentially rather than using
multi-threaded traces. The experiments show that our model can predict private
L1 cache hit rates with 2.12% and shared L2 cache hit rates with about 1.50%
error rate.
