Tootfinder

Accelerating Time-to-Science by Streaming Detector Data Directly into Perlmutter Compute Nodes
Samuel S. Welborn, Bjoern Enders, Chris Harris, Peter Ercius, Deborah J. Bard
https://arxiv.org/abs/2403.14352

Accelerating Time-to-Science by Streaming Detector Data Directly into Perlmutter Compute Nodes
Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement of large data sets from acquisition systems to HPC centers introduces bottlenecks owing to storage I/O at both ends. This manuscript introduces a streaming workflow designed for an high data rate electron detector that streams data directly to compute node me…

Hyperparameter Selection in Continual Learning
Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley, Amos Storkey
https://arxiv.org/abs/2404.06466

Hyperparameter Selection in Continual Learning
In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the whole data stream with different hyperparameter settings. However, this end-of-training HPO is unrealistic as in practice a learner can only s…