Automating Topology Aware Task Mapping on Large Supercomputers

Abstract

Parallel computing is entering the era of petascale machines. This era brings enormous computing power to us and new challenges to harness this power efficiently. Machines with hundreds of thousands of processors already exist, connected by complex interconnect topologies. Network contention is becoming an increasingly important factor affecting overall performance. The farther different messages travel on the network, greater is the chance of resource sharing between messages and hence, of contention. Recent studies on IBM Blue Gene and Cray XT machines have shown that under contention, message latencies can be severely affected. Mapping of communicating tasks on nearby processors can minimize contention and lead to better application performance. In this talk, I will propose algorithms and techniques for automatic mapping of parallel applications to relieve the application developers of this burden. I will first demonstrate the effect of contention on message latencies and use these studies to guide the design of mapping algorithms. I will introduce the hop-bytes metric for the evaluation of mapping algorithms and suggest that it is a better metric than the previously used maximum dilation metric. I will then discuss in some detail, the mapping framework which comprises of topology aware mapping algorithms for parallel applications with regular and irregular communication patterns.