Brief announcement: locality-aware load balancing for speculatively-parallelized irregular applications

Load balancing is an important consideration when running data-parallel programs. While traditional techniques trade off the cost of load imbalance with the overhead of mitigating that imbalance, when speculatively parallelizing amorphous data-parallel applications, we must also consider the effects of load balancing decisions on locality and speculation accuracy. We present two data centric load balancing strategies which account for the intricacies of amorphous data-parallel execution. We implement these strategies as schedulers in the Galois system and demonstrate that they outperform traditional load balancing schedulers, as well as a data-centric, non-loadbalancing scheduler. Categories and Subject Descriptors: D.3.4 [Processors]: Runtime Environments General Terms: Languages

• Type: Conference
• Year: 2010
• Where: SPAA
• Authors: Youngjoon Jo, Milind Kulkarni
• Keywords: Distributed And Parallel Computing, Load Balancing, Load Balancing Decisions, Load Balancing Strategies, SPAA 2010