NODAL is an Open Distributed Autotuning Library in Julia
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Updated
Nov 14, 2018 - Julia
NODAL is an Open Distributed Autotuning Library in Julia
Meta Programming Tools
Wrappers for arrays to make broadcasted operations multithreaded and multiprocessed for high-performance scientific machine learning (SciML)
Discrete Differential Forms in arbitrary dimensions
Exploring Julia's Parallel Computing capabilities.
Fast and easy parallel mapreduce on HPC clusters
Automatic optimization and parallelization for Scientific Machine Learning (SciML)
Side-channel toolkit in Julia
Complex Step Differentiation in Julia
Codes and notebooks for the application of Markov Chain Monte Carlo in spinfoams. Computation of boundary observables, correlation functions and entanglement entropy.
Measuring memory bandwidth using TheBandwidthBenchmark
Bayesian Information Gap Decision Theory
Automated storage and retrieval of results for functions calls
Readily pin Julia threads to CPU processors
Robust pmap calls for efficient parallelization and high-performance computing
Geostatistical Inversion
Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler
Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
Platform-aware programming in Julia
Inference of microbial interaction networks from large-scale heterogeneous abundance data
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