A topology-aware load balancing algorithm for clustered hierarchical multi-core machines

In this paper, we present a topology-aware load balancing algorithm for parallel multi-core machines and its proof of asymptotic convergence to an optimal solution. The algorithm, named HwTopoLB, aims to improve the application performance by reducing core idleness and communication delays. HwTopoLB was designed taking into account the properties of current parallel systems composed of multi-core compute nodes, namely their network interconnection, and their complex and hierarchical core topology. The latter comprises multiple levels of cache, and a memory subsystem with NUMA design. These systems provide high processing power at the expense of asymmetric communication costs, which can hamper the performance of parallel applications depending on their communication patterns if ignored. Our load balancing algorithm models asymmetries in terms of latencies and bandwidths, representing the distances and communication costs among hardware components. We have implemented HwTopoLB using the Charm++ Parallel Runtime System and evaluated its performance with two different benchmarks and one application. Our experimental results with HwTopoLB exhibit scalability over clustered multi-core compute nodes, and average performance improvements of 23% over execution without load balancers and 19% over the existing load balancing strategies on different multi-core systems.