MPI tag matching performance on ConnectX and ARM

Marts, W. Pepper; Dosanjh, Matthew G. F.; Schonbein, Whit; Grant, Ryan E.; Bridges, Patrick G.

doi:10.1145/3343211.3343224

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…Based on the above studies and other research, new generations of MPI message matching Network Interface Cards (NICs) were proposed: Bull eXascale Interconnect (BXI) [10], InfiniBand ConnectX-5 [11]. b) Software: Ghazimirsaeed et al [12] showed how using multiple queues can impact the performance of the matching algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

Performance Improvements of Parallel Applications thanks to MPI-4.0 Hints

Moraru

Roussel

Taboada

et al. 2022

2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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HPC systems have experienced significant growth over the past years, with modern machines having hundreds of thousands of nodes. Message Passing Interface (MPI) is the de facto standard for distributed computing on these architectures. On the MPI critical path, the message-matching process is one of the most time-consuming operations. In this process, searching for a specific request in a message queue represents a significant part of the communication latency. So far, no miracle algorithm performs well in all cases. This paper explores potential matching specializations thanks to hints introduced in the latest MPI 4.0 standard. We propose a hash-table-based algorithm that performs constant time message-matching for no wildcard requests. This approach is suitable for intensive point-to-point communication phases in many applications (more than 50% of CORAL benchmarks). We demonstrate that our approach can improve the overall execution time of real HPC applications by up to 25%. Also, we analyze the limitations of our method and propose a strategy for identifying the most suitable algorithm for a given application. Indeed, we apply machine learning techniques for classifying applications depending on their message pattern characteristics.

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Section: Background and Related Workmentioning

confidence: 99%