Programmable and Scalable Reductions on Clusters

Reductions matter and they are here to stay. Wide adoption of parallel processing hardware in a broad range of computer applications has encouraged recent research efforts on their efficient parallelization. Furthermore, trends towards high productivity languages in mainstream computing increases th...

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Vydáno v:2013 IEEE 27th International Symposium on Parallel and Distributed Processing s. 560 - 568
Hlavní autoři: Ciesko, Jan, Bueno, Javier, Puzovic, Nikola, Ramirez, Alex, Badia, Rosa M., Labarta, Jesus
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2013
Témata:
Arrays
distributed systems
Histograms
parallel programming
Reactive power
reductions
Runtime
runtime systems
Scalability
Software
software cache
Vectors
ISBN:146736066X, 9781467360661
ISSN:1530-2075
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Shrnutí:Reductions matter and they are here to stay. Wide adoption of parallel processing hardware in a broad range of computer applications has encouraged recent research efforts on their efficient parallelization. Furthermore, trends towards high productivity languages in mainstream computing increases the demand for efficient programming support. In this paper we present a new approach on parallel reductions for distributed memory systems that provides both scalability and programmability. Using OmpSs, a task-based parallel programming model, the developer has the ability to express scalable reductions through a single pragma annotation. This pragma annotation is applicable for tasks as well as for work-sharing constructs (with implicit tasking) and instructs the compiler to generate the required runtime calls. The supporting runtime handles data and task distribution, parallel execution and data reduction. Scalability is achieved through a software cache that maximizes local and temporal data reuse and allows overlapped computation and communication. Results confirm scalability for up to 32 12-core cluster nodes.
ISBN:146736066X
9781467360661
ISSN:1530-2075
DOI:10.1109/IPDPS.2013.63