Scalability studies of parallel architectures have used scalar metrics to evaluate their performance. Very often, it is difficult to glean the sources of inefficiency resulting from the mismatch between the algorithmic and architectural requirements using such scalar metrics. Low-level performance studies of the hardware are also inadequate for predicting the scalability of the machine on real applications. We propose a top-down approach to scalability study that alleviates some of these problems. We characterize applications in terms of the frequently occurring kernels and their interaction with the architecture in terms of overheads in the parallel system. An overhead function is associated with each artifact of the parallel system that limits its scalability. We present a simulation platform called SPASM (Simulator for Parallel Architectural Scalability Measurements) that quantifies these overhead functions. SPASM separates the algorithmic overhead into its components (such as serial and work-imbalance overheads), and interaction overhead into its components (such as latency and contention). Such a separation is novel and has not been addressed in any previous study using real applications. We illustrate the top-down approach by considering a case study in implementing three NAS parallel kernels on two simulated message-passing platforms.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Hardware and Architecture
- Computer Networks and Communications
- Artificial Intelligence