TreadMarks: shared memory computing on networks of workstations

Abstract

Shared memory facilitates the transition from sequential to parallel processing. Since most data structures can be retained, simply adding synchronization achieves correct, efficient programs for many applications. We discuss our experience with parallel computing on networks of workstations using the TreadMarks distributed shared memory system. DSM allows processes to assume a globally shared virtual memory even though they execute on nodes that do not physically share memory. We illustrate a DSM system consisting of N networked workstations, each with its own memory. The DSM software provides the abstraction of a globally shared memory, in which each processor can access any data item without the programmer having to worry about where the data is or how to obtain its value.