Towards Parallel Application Classification using Quantitative Metrics

When computer architects re-invented parallelism through multi-core processors, application parallelization became mandatory. The challenge of “mass-parallelization” forced the software community to react quickly. Unfortunately, instead of tackling such a large-scale problem in a systematic way, we have rather experienced various random search, non-portable and non-reusable approaches. We believe that the way to effectively address the problem is to limit its scale by splitting the potentially infinite application space into a limited set of application classes. This idea is not entirely new, as a similaritybased taxonomy has been recently proposed. However, in this paper, we purpose a different approach that focuses on a mostly quantitative application characterization. We introduce a set of metrics to characterize applications and we show how they can be evaluated for three case-studies. Once a wide range of applications have been characterized, we can identify a limited number of application classes. Next, for these classes, dedicated programming tools can be used to write high-performance parallel software in a productive way.