An uncertainty-sampling active learning policy plus a pseudo-streaming workflow trains neutron diffraction structure-finding models with roughly 75% less training data and about 20% shorter training time.
Using Pilot Systems to Execute Many Task Workloads on Supercomputers
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abstract
High performance computing systems have historically been designed to support applications comprised of mostly monolithic, single-job workloads. Pilot systems decouple workload specification, resource selection, and task execution via job placeholders and late-binding. Pilot systems help to satisfy the resource requirements of workloads comprised of multiple tasks. RADICAL-Pilot (RP) is a modular and extensible Python-based pilot system. In this paper we describe RP's design, architecture and implementation, and characterize its performance. RP is capable of spawning more than 100 tasks/second and supports the steady-state execution of up to 16K concurrent tasks. RP can be used stand-alone, as well as integrated with other application-level tools as a runtime system.
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An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
An uncertainty-sampling active learning policy plus a pseudo-streaming workflow trains neutron diffraction structure-finding models with roughly 75% less training data and about 20% shorter training time.