Two prevalent models of parallel programming are data parallelism and task
parallelism. Data parallelism is the simultaneous application of a single operation to a data
set. This model fits best with regular computations. Task parallelism is the simultaneous
application of possibly different operations to possibly different data sets. This fits best
with irregular computations. Efficient solution of some problems require both regular and
irregular computations. Implementing efficient and portable parallel solutions to these
problems requires a high-level language that can accommodate both task and data
parallelism. We have extended the data-parallel language Dataparallel C to include task
parallelism so that programmers may now use data and task parallelism within the same
program. The extension permits the nesting of data-parallel constructs inside a task-parallel
framework. We present a banded linear system to analyze the benefits of our
language extensions. / Graduation date: 1995
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/35274 |
| Date | 28 October 1994 |
| Creators | Macielinski, Damien D. |
| Contributors | Rudd, Walter G. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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