Autoscheduling in a distributed shared-memory environment

Abstract

The ease of programming and compiling for the shared memory multiprocessor model, coupled with the scalability and cost advantages of distributed memory computers, give an obvious appeal to distributed shared memory architectures. In this paper we discuss the design and implementation issues of a dynamic data management and scheduling environment for distributed shared memory architectures. In contrast to the predominantly static approaches used on distributed and message passing machines, we advocate the advantages of dynamic resource allocation, especially in the case of multi-user environments. We propose hybrid data and work distribution techniques that adjust to variations in the physical partition and achieve better load balance than purely static schemes. We present the architecture of our execution environment and discuss implementation details of some of the critical components. Preliminary results using benchmarks of representative execution profiles support our main thesis: With minimal contrl, the load balancing and resource utilization advantages offered by dynamic methods often outweigh the disadvantage of increased memory latency stemming from slightly compromised data locality, and perhaps additional run-time overhead.

This work was supported by the Office of Naval Research under grant N00014-94-1-0234.