A Pilot Study to Compare Programming Effort for Two Parallel Programming Models (PREPRINT)

TL;DR: In this paper, the authors measured the effect of parallel programming models (message-passing vs. PRAM-like) on programmer effort and found that XMTC programs were more likely to correct, but the difference in correctness rates was not statistically significant.

Abstract: : CONTEXT: Writing software for the current generation of parallel systems requires significant programmer effort, and the community is seeking alternatives that reduce effort while still achieving good performance. OBJECTIVE: Measure the effect of parallel programming models (message-passing vs. PRAM-like) on programmer effort. DESIGN, SETTING, and SUBJECTS: One group of subjects implemented sparse-matrix dense-vector multiplication using message-passing (MPI), and a second group solved the same problem using a PRAM-like model (XMTC). The subjects were students in two graduate-level classes: one class was taught MPI and the other was taught XMTC. MAIN OUTCOME MEASURES: Development time, program correctness. RESULTS: Mean XMTC development time was 4.8 hours less than mean MPI development time (95% confidence interval, 2.0-7.7), a 46% reduction. XMTC programs were more likely to be correct, but the difference in correctness rates was not statistically significant (p=.16). CONCLUSIONS: XMTC solutions for this particular problem required less effort than MPI equivalents, but further studies are necessary which examine different types of problems and different levels of programmer experience.