Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
List scheduling of parallel tasks
Information Processing Letters
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Global optimizations for parallelism and locality on scalable parallel machines
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
A threshold scheduling strategy for Sisal on distributed memory machines
Journal of Parallel and Distributed Computing
A Scalable Scheduling Scheme for Functional Parallelism on Distributed Memory Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
Program repartitioning on varying communication cost parallel architectures
Journal of Parallel and Distributed Computing
Optimal Scheduling Algorithm for Distributed-Memory Machines
IEEE Transactions on Parallel and Distributed Systems
A task duplication based scalable scheduling algorithm for distributed memory systems
Journal of Parallel and Distributed Computing
A comparison of list schedules for parallel processing systems
Communications of the ACM
Partitioning and Scheduling Parallel Programs for Multiprocessors
Partitioning and Scheduling Parallel Programs for Multiprocessors
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
On Mapping Systolic Algorithms onto the Hypercube
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
On the Granularity and Clustering of Directed Acyclic Task Graphs
IEEE Transactions on Parallel and Distributed Systems
Building Synthetic Parallel Programs: the Project ALPES
Proceedings of the IFIP WG 10.3 Workshop on Programming Environments for Parallel Computing
A fast and scalable scheduling algorithm for distributed memory systems
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Static task scheduling and grain packing in parallel processing systems
Static task scheduling and grain packing in parallel processing systems
Task scheduling algorithms for distributed memory systems
Task scheduling algorithms for distributed memory systems
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The problem of compile time scheduling of tasks of a program represented as a directed acyclic graph (DAG) is NP-hard in its general form. A number of approaches have been proposed which attempt to solve the problem either sub-optimally for general cases or optimally for restrictive special cases. But all the compile timeapproaches suffer due to the inability to accurately model the computation and communication costs of the target architecture. A desirable property of a compile time scheduling algorithm is robustness against the variations in the computation and communication costs so that the run time performance is close to the compile time estimates; this aspect of scheduling has been left open in the literature.This paper first introduces a compile time scheduling algorithm for a variable number of available processors and then examines the impact of change of computation and communication costs on the generated schedule. The cost variations for all the nodes and all the edges are assumed to be uniform (in other words, all the node costs change by the same ratio and the edge costs change by the same ratio). This sort of variations could occur due to the inaccuracies in estimating the instruction execution times or the message passing delays. The ratio of theschedule length obtained by the new schedule based on the modified costs to the schedule length obtained by using the modified costs on the original schedule (obtained by initial costs) is used as a measure of the robustness of thealgorithm. The essential conditions for robustness of the proposed algorithm are discussed and are demonstrated through an experimental study.