Výsledky vyhledávání - "Coletti, Mark"

Report

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Autor: Shanmugavelu, Sanjif, Taillefumier, Mathieu, Culver, Christopher, Hernandez, Oscar, Coletti, Mark, Sedova, Ada

Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumulating errors. Non-reproducibility can critically affect the efficienc

Externí odkaz: http://arxiv.org/abs/2408.05148

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SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing

Autor: Date, Prasanna, Gunaratne, Chathika, Kulkarni, Shruti, Patton, Robert, Coletti, Mark, Potok, Thomas

In many neuromorphic workflows, simulators play a vital role for important tasks such as training spiking neural networks (SNNs), running neuroscience simulations, and designing, implementing and testing neuromorphic algorithms. Currently available s

Externí odkaz: http://arxiv.org/abs/2305.02510

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Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Autor: Gao, Mu, Coletti, Mark, Davidson, Russell B., Prout, Ryan, Abraham, Subil, Hernandez, Benjamin, Sedova, Ada

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic resolution in some cases, and

Externí odkaz: http://arxiv.org/abs/2201.10024

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Dissertation/ Thesis

An analysis of a model-based evolutionary algorithm| Learnable Evolution Model

Autor: Coletti, Mark

An evolutionary algorithm (EA) is a biologically inspired metaheuristic that uses mutation, crossover, reproduction, and selection operators to evolve solutions for a given problem. Learnable Evolution Model (LEM) is an EA that has an evolutionary al

Externí odkaz: http://pqdtopen.proquest.com/#viewpdf?dispub=3625081

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