PackStealLB: A scalable distributed load balancer based on work stealing and workload discretization

Abstract

The scalability of high-performance, parallel iterative applications is directly affected by how well they use the available computing resources. These applications are subject to load imbalance due to the nature and dynamics of their computations. It is common that high performance systems employ periodic load balancing to tackle this issue. Dynamic load balancing algorithms redistribute the application's workload using heuristics to circumvent the NP-hard complexity of the problem However, scheduling heuristics must be fast to avoid hindering application performance when distributing the workload on large and distributed environments. In this work, we present a technique for low overhead, high quality scheduling decisions for parallel iterative applications. The technique relies on combined application workload information paired with distributed scheduling algorithms. An initial distributed step among scheduling agents group application tasks in packs of similar load to minimize messages among them. This information is used by our scheduling algorithm, Pack-StealLB, for its distributed-memory work stealing heuristic. Experimental results showed that PackStealLB is able to improve the performance of a molecular dynamics benchmark by up to 41%, outperforming other scheduling algorithms in most scenarios over almost one thousand cores.

From reactive to proactive load balancing for task‐based parallel applications in distributed memory machines

“…4 Also, Freitas et al analyzed workload information to combine with distributed scheduling algorithms. 37 The authors reduced migration overhead by packing similar tasks to minimize messages.…”

“…Menon et al proposed using partial information about the global system state to improve stealing decisions as well as balance the load by randomized work‐stealing 4 . Also, Freitas et al analyzed workload information to combine with distributed scheduling algorithms 37 . The authors reduced migration overhead by packing similar tasks to minimize messages.…”