Streaming nested data parallelism on multicores

Frederik Meisner Madsen; Andrzej Filinski

doi:10.1145/2975991.2975998

Streaming nested data parallelism on multicores

Frederik Meisner Madsen, Andrzej Filinski

Department of Computer Science

1 Citation (Scopus)

Abstract

The paradigm of nested data parallelism (NDP) allows a variety of semi-regular computation tasks to be mapped onto SIMD-style hardware, including GPUs and vector units. However, some care is needed to keep down space consumption in situations where the available parallelism may vastly exceed the available computation resources. To allow for an accurate space-cost model in such cases, we have previously proposed the Streaming NESL language, a refinement of NESL with a high-level notion of streamable sequences.

In this paper, we report on experience with a prototype implementation of Streaming NESL on a 2-level parallel platform, namely a multicore system in which we also aggressively utilize vector instructions on each core. We show that for several examples of simple, but not trivially parallelizable, text-processing tasks, we obtain single-core performance on par with off-the-shelf GNU Coreutils code, and near-linear speedups for multiple cores.

Original language	English
Title of host publication	Proceedings of the 5th International Workshop on Functional High-Performance Computing
Number of pages	8
Publisher	Association for Computing Machinery
Publication date	8 Sept 2016
Pages	44-51
ISBN (Electronic)	978-1-4503-4433-3
DOIs	https://doi.org/10.1145/2975991.2975998
Publication status	Published - 8 Sept 2016
Event	International Workshop on Functional High-Performance Computing - Nara, Japan Duration: 22 Sept 2016 → 22 Sept 2016 Conference number: 5 https://sites.google.com/site/fhpcworkshops/

Workshop

Workshop	International Workshop on Functional High-Performance Computing
Number	5
Country/Territory	Japan
City	Nara
Period	22/09/2016 → 22/09/2016
Internet address	https://sites.google.com/site/fhpcworkshops/

Access to Document

10.1145/2975991.2975998

Cite this

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abstract = "The paradigm of nested data parallelism (NDP) allows a variety of semi-regular computation tasks to be mapped onto SIMD-style hardware, including GPUs and vector units. However, some care is needed to keep down space consumption in situations where the available parallelism may vastly exceed the available computation resources. To allow for an accurate space-cost model in such cases, we have previously proposed the Streaming NESL language, a refinement of NESL with a high-level notion of streamable sequences.In this paper, we report on experience with a prototype implementation of Streaming NESL on a 2-level parallel platform, namely a multicore system in which we also aggressively utilize vector instructions on each core. We show that for several examples of simple, but not trivially parallelizable, text-processing tasks, we obtain single-core performance on par with off-the-shelf GNU Coreutils code, and near-linear speedups for multiple cores.",

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