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Fuzzy matching: Hardware accelerated MPI communication middleware

Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019

Dosanjh, Matthew G.; Schonbein, William W.; Grant, Ryan; Bridges, Patrick G.; Gazimirsaeed, S.M.; Afsahi, Ahmad

Contemporary parallel scientific codes often rely on message passing for inter-process communication. However, inefficient coding practices or multithreading (e.g., via MPI-THREAD-MULTIPLE) can severely stress the underlying message processing infrastructure, resulting in potentially un-acceptable impacts on application performance. In this article, we propose and evaluate a novel method for addressing this issue: 'Fuzzy Matching'. This approach has two components. First, it exploits the fact most server-class CPUs include vector operations to parallelize message matching. Second, based on a survey of point-to-point communication patterns in representative scientific applications, the method further increases parallelization by allowing matches based on 'partial truth', i.e., by identifying probable rather than exact matches. We evaluate the impact of this approach on memory usage and performance on Knight's Landing and Skylake processors. At scale (262,144 Intel Xeon Phi cores), the method shows up to 1.13 GiB of memory savings per node in the MPI library, and improvement in matching time of 95.9%; smaller-scale runs show run-time improvements of up to 31.0% for full applications, and up to 6.1% for optimized proxy applications.

More Details
TYPE Conference Poster YEAR 2019
DOIOSTIScopus
Results 23601–23700 of 99,299
Results 23601–23700 of 99,299