Implementation and performance aspects of Kahn process networks

Zeljko Vrba
2010 ACM SIGMultimedia Records  
Non quia difficilia sunt non audemus, sed quia non audemus, difficilia sunt. Abstract The appearance of commodity multi-core processors, has spawned a wide interest in parallel programming, which is widely-regarded as more challenging than sequential programming. KPNs are a model of concurrency that relies exclusively on message passing, and that has some advantages over parallel programming tools in wide use today: simplicity, graphical representation, and determinism. Because of determinism,
more » ... t is possible to reliably reproduce faults, an otherwise notoriously difficult problem with parallel programs. KPNs have gained acceptance in simulation and signal-processing communities. In this thesis, we investigate the applicability of KPNs to implementing general-purpose parallel computations for multi-core machines. In particular, we investigate 1) how KPNs can be used for modeling general-purpose problems; 2) how an efficient KPN run-time can be implemented; 3) what KPN scheduling strategies give good run-time performance. For these purposes, we have developed Nornir, an efficient run-time system for executing KPNs. With Nornir, we show that it is possible to develop a high-performance KPN run-time for multi-core machines. We experimentally demonstrate that problems expressed in the Kahn model resemble very much their sequential implementations, yet perform much better than when expressed in the MapReduce model, which has become widely-recognized as a simple parallel programming model. Lastly, we use Nornir to evaluate several load-balancing methods: static assignment, work-stealing, our improvement of work-stealing, and a method based on graph partitioning. The understanding brought by this evaluation is significant not only in the context of the Kahn model, but also in the more general context of load-balancing (potentially distributed) applications written in message-passing style.
doi:10.1145/1874413.1874418 fatcat:sm5wsqpyyfevtarf6336elhyny