A Software Cache Autotuning Strategy for Dataflow Computing with UPC++ DepSpawn
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A Software Cache Autotuning Strategy for Dataflow Computing with UPC++ DepSpawnFecha
2021Cita bibliográfica
Fraguela, BB, Andrade, D. A software cache autotuning strategy for dataflow computing with UPC++ DepSpawn. Comp and Math Methods. 2021; 3:e1148. https://doi.org/10.1002/cmm4.1148
Resumen
[Abstract] Dataflow computing allows to start computations as soon as all their dependencies are satisfied. This is particularly useful in applications with irregular or complex patterns of dependencies which would otherwise involve either coarse grain synchronizations which would degrade performance, or high programming costs. A recent proposal for the easy development of performant dataflow algorithms in hybrid shared/distributed memory systems is UPC++ DepSpawn. Among the many techniques it applies to provide good performance is a software cache that minimizes the communications among the processes involved. In this article we provide the details of the implementation and operation of this cache and we present an autotuning strategy that simplifies its usage by freeing the user from having to estimate an adequate size for this cache. Rather, the runtime is now able to define reasonably sized caches that provide near optimal behavior.
Palabras clave
Autotuning
Dataflow computing
Distributed memory
Locality
PGAS
Runtimes
Dataflow computing
Distributed memory
Locality
PGAS
Runtimes
Descripción
This is the accepted version of the following article: B. B. Fraguela, D. Andrade. A software cache autotuning strategy for dataflow computing with UPC++ DepSpawn. Computational and Mathematical Methods, 3(6), e1148. November 2021, which has been published in final form at http://dx.doi.org/10.1002/cmm4.1148. This article may be used for noncommercial purposes in accordance with the Wiley Self-Archiving Policy [http://www.wileyauthors.com/self-archiving].