Progressive Load Balancing in Distributed Memory

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

System performance variability is a significant challenge to scalability of tightly-coupled iterative applications. Asynchronous variants perform better, but an imbalance in progress can result in slower convergence or even failure to converge, as old data is used for updates. In shared memory, this can be countered using progressive load balancing (PLB). We present a distributed memory extension to PLB (DPLB) by running PLB on nodes and adding a balancing layer between nodes. We demonstrate that this method is able to mitigate system performance variation by reducing global progress imbalance 1.08x–4.05x and time to solution variability 1.11x–2.89x. In addition, the method scales without significant overhead to 100 nodes.
Original languageEnglish
Pages (from-to)127-136
Number of pages10
JournalAdvances in Parallel Computing
Publication statusPublished - 1 Apr 2020
Event2019 International Conference on Parallel Computing - Prague, Czech Republic
Duration: 10 Sept 201913 Sept 2019


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