These institutions have access to a grid of interconnected data centers distributed across the globe, which allows researchers to schedule and run the compute workloads for their experiments at a grid site where resources are available.
While most workloads succeed, about 10-15% of them eventually fail, resulting in lost time, misused compute resources, and wasted research funds.
These workloads can fail for any number of reasons—incorrectly entered commands, requested memory, or even the time of day—and each type of failure contains unique information that can help the researcher trying to run it.
For example, if a machine learning (ML) model could predict a workload was likely to fail because of memory (Run-Held-Memory class is predicted), the researcher could adjust the memory requirement and resubmit the workload without wasting the resources an actual failure would.
Successfully predicting which workloads will fail—and shouldn’t be run at all—helps free up resources, reduces wasted CPU cycles, and lets us spend research funds wisely.
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