UC San Diego

Software engineers now face the difficult task of parallelizing serial programs for parallel execution on multicore processors. Parallelization is a complex task that typically requires considerable time and effort. However, even after extensive engineering efforts, the resulting speedup is often fundamentally limited due to the lack of parallelism in the target program or the inability of the target platform to exploit existing parallelism. Unfortunately, little guidance is available as to how much benefit may come from parallelization, making it hard for a programmer to answer this critical question: "Should I parallelize my code?". In this dissertation, we examine the design and implementation of Kismet, a tool that creates parallel speedup estimates for unparallelized serial programs. Our approach differs from previous approaches in that it does not require any changes or manual analysis of the serial program. This difference allows quick profitability analysis of a program, helping programmers make informed decisions in the initial stages of parallelization. To provide parallel speedup estimates from serial programs, we developed a dynamic program analysis named hierarchical critical path analysis (HCPA). HCPA extends a classic technique called critical path analysis to quantify localized parallelism of each region in a program. Based on the parallelism information from HCPA, Kismet incorporates key parallelization constraints that can significantly affect the parallel speedup, providing realistic speedup upperbounds. The results are compelling. Kismet can significantly improve the accuracy of parallel speedup estimates relative to prior work based on critical path analysis