Dynamic load balancing of distributed SPMD computations with explicit message-passing

Distributed systems have the potentiality of becoming an alternative platform for parallel computations. However, there are still many obstacles to overcome, one of the most serious is that distributed systems typically consist of shared heterogeneous components with highly variable computational power. We present a load balancing support that checks the load status and, if necessary, adapts the workload to dynamic platform conditions through data migrations from overloaded to underloaded nodes. Unlike task migration supports for task parallelism and other data migration frameworks for master/slave-based parallel applications, our support works for the entire class of SPMD regular applications with explicit communications such as linear algebra problems, partial differential equation solvers, image processing algorithms. Although we considered several variants (three activation mechanisms, three load monitoring techniques and four decision policies), we implemented only the protocols that guarantee program consistency. The efficiency of the strategies is tested in the instance of two SPMD algorithms that are based on the PVM library enriched by special-purpose primitives for data management. As additional contribution, our research keeps the entire support for dynamic load balancing transparent to the programmer. The only visible interface of our support is the activation phase.