Communication optimizations for irregular scientific computations on distributed memory architectures

This paper describes a number of optimizations that can be used to support the efficient execution of irregular problems on distributed memory parallel machines. These primitives (1) coordinate interprocessor data movement, (2) manage the storage of, and access to, copies of off-processor data, (3) minimize interprocessor communication requirements, and (4) support a shared name space. We present a detailed performance and scalability analysis of the communication primitives. This performance and scalability analysis is carried out using a workload generator, kernels from real applications, and a large unstructured adaptive application (the molecular dynamics code CHARMM).