Abstract
Scientific codes are usually parallelized by partitioning a grid among processors. To achieve top performance it is necessary to partition the grid so as to balance workload and minimize communication/synchronization costs. This problem is particularly acute when the grid is irregular, changes over the course of the computation, and is not known until load-time. Critical mapping and remapping decisions rest on our ability to accurately predict performance, given a description of a grid and its partition. This paper discusses one approach to this problem, and illustrates its use on a one-dimensional fluids code. The models we construct are shown empirically to be accurate, and are used to find optimal remapping schedules.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 165-170 |
| Number of pages | 6 |
| Journal | Performance Evaluation Review |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| State | Published - 1989 |
| Externally published | Yes |
| Event | ACM Sigmetrics and Performance '89 International Conference on Measurement and Modeling of Computer Systems - Proceedings - Berkeley, CA, USA Duration: May 23 1989 → May 26 1989 |
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications