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Found 3 papers, 1 papers with code
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Run-by-run variability in parallel programs caused by floating-point non-associativity (FPNA) has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors.
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing
Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (such as BindsNET).
Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer
Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics.
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