Introduction to parallel programming
Introduction to parallel programming
MIN-graph: a tool for monitoring and visualizing MIN-based multiprocessor performance
Journal of Parallel and Distributed Computing - Special issue on tools and methods for visualization of parallel systems and computations
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Semi-empirical multiprocessor performance predictions
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures
IEEE Parallel & Distributed Technology: Systems & Technology
Visualizing the Performance of Parallel Programs
IEEE Software
Scalability of Parallel Algorithm-Machine Combinations
IEEE Transactions on Parallel and Distributed Systems
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Hi-index | 0.00 |
Parallel computing scalability evaluates the extent to which parallel programs and architectures can effectively utilize increasing numbers of processors. In this paper, we compare a group of existing scalability metrics and evaluation models with an experimental metric which uses network latency to measure and evaluate the scalability of parallel programs and architectures. To provide insight into dynamic system performance, we have developed an integrated software environment prototype for measuring and evaluating multiprocessor scalability performance, called Scale-Graph. Scale-Graph uses a graphical instrumentation monitor to collect, measure and analyze latency-related data, and to display scalability performance based on various program execution patterns. The graphical software tool is X-window based and currently implemented on standard workstations to analyze performance data of the KSR-1, a hierarchical ring-based shared-memory architecture.