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Abstract: Mixed task and data parallelism exists naturally in many applications, but utilizing it may require sophisticated scheduling algorithms and software support. Recently, significant research effort has been applied to exploiting mixed parallelism in both theory and systems communities. In this paper, we ask how much mixed parallelism will improve performance in practice, and how architectural evolution impacts these estimates. First, we build and validate a performance model for a class of mixed... (Update)
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BibTeX entry: (Update)
Chakrabarti, S., Demmel, J., and Yelick, K. Modeling the benefits of mixed data and task parallelism. In Seventh Annual ACM Symposium on Parallel Algorithms and Architectures (Santa Barbara, CA, July 1995). http://citeseer.ist.psu.edu/chakrabarti95modeling.html More
@inproceedings{ chakrabarti95modeling,
author = "Soumen Chakrabarti and James Demmel and Katherine A. Yelick",
title = "Modeling the Benefits of Mixed Data and Task Parallelism",
booktitle = "{ACM} Symposium on Parallel Algorithms and Architectures",
pages = "74-83",
year = "1995",
url = "citeseer.ist.psu.edu/chakrabarti95modeling.html" }
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