Browse Theses

Parallel loops are one of the main sources of parallelism in scientific applications, and many parallel loops do not have a uniform iteration execution time. To achieve good performance for such applications on a parallel computer, iterations of a parallel loop have to be assigned to processors in such a way that each processor has roughly the same amount of work in terms of execution time. A parallel computer with a large number of processors tends to have distributed-memory. To run a parallel loop on a distributed-memory machine, data distribution also needs to be considered. This research investigates the scheduling of non-uniform parallel loops on both shared-memory and distributed-memory parallel computers.

We present Safe Self-Scheduling (SSS), a new scheduling scheme that combines the advantages of both static and dynamic scheduling schemes. SSS has two phases: a static scheduling phase and a dynamic self-scheduling phase that together reduce the scheduling overhead while achieving a well balanced workload. The techniques introduced in SSS can be used by other self-scheduling schemes. The static scheduling phase further improves the performance by maintaining a high cache hit ratio resulting from increased affinity of iterations to processors. SSS is also very well suited for distributed-memory machines.

We introduce methods to duplicate data on a number of processors. The methods eliminate data movement during computation and increase the scalability of problem size. We discuss a systematic approach to implement a given self-scheduling scheme on a distributed-memory. We also show a multilevel scheduling scheme to self-schedule parallel loops on a distributed-memory machine with a large number of processors to eliminate the bottleneck resulting from a central scheduler.

We proposed a method using abstractions to automate both self-scheduling methods and data distribution methods in parallel programming environments. The abstractions are tested using CHARM, a real parallel programming environment. Methods are also developed to tolerate processor faults caused by both physical failure and reassignment of processors by the operating system during the execution of a parallel loop.

We tested the techniques discussed using simulations and real applications. Good results have been obtained on both shared-memory and distributed-memory parallel computers.