Job Management and Task Bundling
High Performance Computing is often performed on scarce and shared computing resources. To ensure computers are used to their full capacity, administrators often incentivize large workloads that are not possible on smaller systems. Measurements in Lattice QCD frequently do not scale to machine-size...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://doi.org/10.1051/epjconf/201817509007 |
| Summary: | High Performance Computing is often performed on scarce and shared computing resources. To ensure computers are used to their full capacity, administrators often incentivize large workloads that are not possible on smaller systems. Measurements in Lattice QCD frequently do not scale to machine-size workloads. By bundling tasks together we can create large jobs suitable for gigantic partitions. We discuss METAQ and mpi_jm, software developed to dynamically group computational tasks together, that can intelligently backfill to consume idle time without substantial changes to users’ current workflows or executables. |
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| ISSN: | 2100-014X |