| This thesis provides a systematic study of application sensitivity to network performance. Our aim is to investigate the impact of communication performance on real applications. Using the LogGP model as an abstract framework, we set out to understand which aspects of communication performance are most important. The focus of our investigation thus centers on a quantification of the sensitivity of applications to the parameters of the LogGP model: network latency, software overhead, per-message and per-byte bandwidth.; Sensitive applications will exhibit a high rate of "slowdown" as we scale a given parameter. Insensitive applications will show little or no difference in performance as we change the parameters. In addition, we can categorize the shape of the slowdown curve because our apparatus allows us to observe plateaus or other discontinuities.; We use three distinct application suites in order to broaden the applicability of our results. The first suite consists of parallel programs designed for low-overhead Massively Parallel Processors (MPPs) and Networks of Workstations (NOWs). The second suite is a sub-set of the NAS parallel benchmarks, which were designed on older MPPs. The final suite consists of the SPECsfs benchmark, which is designed to measure Network File System (NFS) performance over local area networks.; Our results show that applications display the strongest sensitivity to software overhead, slowing down by as much as a factor of 50 when overhead is increased by a factor of 20. Even lightly communicating applications can suffer a factor of 3--5 slowdown.; The effect of added latency is qualitatively different from the effect of added overhead and bandwidth. Further, the effects are harder to predict because they are more dependent on application structure. For our measured applications, the sensitivity to overhead and various bandwidths is much stronger than sensitivity to latency. We found that this result stemmed from programmers who are quite adept at using latency tolerating techniques such as pipelining, overlapping, batching and caching.; We conclude that computer systems are complex enough to warrant our perturbation based methodology, and speculate how the methodology might be applied to other computer systems areas. (Abstract shortened by UMI.)... |
