Scaling alltoall collective on multi-core systems

Kumar, Rahul; Mamidala, Amith R.; Panda, D.K.

doi:10.1109/ipdps.2008.4536141

Cited by 30 publications

(16 citation statements)

References 7 publications

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“…Our approach cannot however be applied to some hierarchical algorithms that use multiple leaders per node for inter-node communication [10,8]. This approach has the advantage of distributing the leaders load across multiple cores.…”

Section: Discussion and Related Workmentioning

confidence: 99%

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Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Goglin

Moreaud

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Section: Discussion and Related Workmentioning

confidence: 99%

“…Intranode optimizations are now often combined with inter-node communication within hierarchical algorithms. For an Alltoall operation, this idea may be implemented through an intra-node Alltoall on each node, followed by inter-node Alltoall between all groups of corresponding local ranks [10].…”

Section: Collective Operations On Many-core Clustersmentioning

confidence: 99%

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Goglin

Moreaud

2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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“…MPI [22,17] and parallel programming languages such as UPC [19] provide optimized implementations of all-to-all collective operations. Most if not all of the existing implementations use multiple algorithms selected by message size.…”

Section: All-to-all Performancementioning

confidence: 99%

Congestion avoidance on manycore high performance computing systems

Luo

Panda

Ibrahim

et al. 2012

Proceedings of the 26th ACM International Conference on Supercomputing

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Efficient communication is a requirement for application scalability on High Performance Computing systems. In this paper we argue for incorporating proactive congestion avoidance mechanisms into the design of communication layers on manycore systems. This is in contrast with the status quo which employs a reactive approach, e.g. congestion control mechanisms are activated only when resources have been exhausted. We present a core stateless optimization approach based on open loop end-point throttling, implemented for two UPC runtimes (Cray and Berkeley UPC) and validated on InfiniBand and the Cray Gemini networks. Microbenchmark results indicate that throttling the number of messages in flight per core can provide up to 4X performance improvements, while throttling the number of active cores per node can provide additional 40% and 6X performance improvement for UPC and MPI respectively. We evaluate inline (each task makes independent decisions) and proxy (server) congestion avoidance designs. Our runtime provides both performance and performance portability. We improve allto-all collective performance by up to 4X and provide better performance than vendor provided MPI and UPC implementations. We also demonstrate performance improvements of up to 60% in application settings. Overall, our results indicate that modern systems accommodate only a surprisingly small number of messages in flight per node. As Exascale projections indicate that future systems are likely to contain hundreds to thousands of cores per node, we believe that their networks will be underprovisioned. In this situation, proactive congestion avoidance might become mandatory for performance improvement and portability.

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“…Much work has been done to improve MPI performance on SMP-CMP clusters. Techniques were developed to improve both point-to-point communications [7], [9], [10], [12] and collective communications [14], [15], [17], [19]. These optimizations further differentiate the communication performance in the multi-layer communication infrastructure in SMP-CMP clusters and manifest the impacts of processor affinity.…”

Section: Related Workmentioning

confidence: 99%

Processor affinity and MPI performance on SMP-CMP clusters

Zhang

Yuan

Srinivasan

2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Abstract-Clusters of Symmetric MultiProcessing (SMP) nodes with multi-core Chip-Multiprocessors (CMP), also known as SMP-CMP clusters, are becoming ubiquitous today. For Message Passing interface (MPI) programs, such clusters have a multilayer hierarchical communication structure: the performance of intra-node communication is usually higher than that of internode communication; and the performance of intra-node communication is not uniform with communications between cores within a chip offering higher performance than communications between cores in different chips. As a result, the mapping from Message Passing Interface (MPI) processes to cores within each compute node, that is, processor affinity, may significantly affect the performance of intra-node communication, which in turn may impact the overall performance of MPI applications. In this work, we study the impacts of processor affinity on MPI performance in SMP-CMP clusters through extensive benchmarking and identify the conditions when processor affinity is (or is not) a major factor that affects performance.

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Scaling alltoall collective on multi-core systems

Cited by 30 publications

References 7 publications

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Dodging Non-uniform I/O Access in Hierarchical Collective Operations for Multicore Clusters

Congestion avoidance on manycore high performance computing systems

Processor affinity and MPI performance on SMP-CMP clusters

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