Cache-conscious scheduling of streaming pipelines on parallel machines with private caches


This paper studies the problem of scheduling a streaming pipeline on a multicore machine with private caches to maximize throughput. The theoretical contribution includes lower and upper bounds in the parallel external-memory model. We show that a simple greedy scheduling strategy is asymptotically optimal with a constant-factor memory augmentation. More specifically, we show that if our strategy has a running time of Q cache misses on a machine with size-M caches, then every “static” scheduling policy must have time at least that of Q(Q) cache misses on a machine with size-M/6 caches. Our experimental study considers the question of whether scheduling based on cache effects is more important than scheduling based on only the number of computation steps. Using synthetic pipelines with a range of parameters, we compare our cache-based partitioning against several other static schedulers that load-balance computation. In most cases, the cache-based partitioning indeed beats the other schedulers, but there are some cases that go the other way. We conclude that considering cache effects is a good idea, but other features of the streaming pipeline are also important.

In International Conference on High Performance Computing (HiPC), IEEE.