Workstation clusters for parallel computing

Abstract

Workstation clusters have become an increasingly popular alternative to traditional parallel supercomputers for many workloads requiring high performance computing. The use of parallel computing for scientific simulations has increased tremendously in the last ten years, and parallel implementations of scientific simulation codes are now in widespread use. There are two dominant parallel hardware/software architectures in use today: distributed memory, and shared memory. Systems implementing shared memory provide cooperating processes with a shared memory address space that can be accessed by all processors. In shared memory systems, parallel processing occurs through the use of shared data structures, or through emulation of message passing semantics in software. Distributed memory systems are composed of a number of interconnected computational nodes, which do not share memory, but can communicate with each other through a high-performance network of some kind. Parallelism is achieved on distributed memory systems with multiple copies of the parallel program running on different nodes, sending messages to each other to coordinate computations. The messages used in a distributed memory parallel program typically contain application data, synchronization information, and other data that controls the execution of the parallel program.