28 citations · 63 across the 5 of their papers we have counts for
5 papers
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Amrita Mathuriya, Deborah Bard, Peter Mendygral +14
Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow:…
Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning
Peter Boyle, Michael Chuvelev, Guido Cossu +3
We discuss practical methods to ensure near wirespeed performance from clusters with either one or two Intel(R) Omni-Path host fabric interfaces (HFI) per node, and Intel(R) Xeon P…
Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System
Taisuke Boku, Ken-Ichi Ishikawa, Yoshinobu Kuramashi +1
Lattice Quantum Chromodynamics (Lattice QCD) is a quantum field theory on a finite discretized space-time box so as to numerically compute the dynamics of quarks and gluons to expl…
A performance evaluation of CCS QCD Benchmark on the COMA (Intel(R) Xeon Phi, KNC) system
Taisuke Boku, Ken-Ichi Ishikawa, Yoshinobu Kuramashi +4
The most computationally demanding part of Lattice QCD simulations is solving quark propagators. Quark propagators are typically obtained with a linear equation solver utilizing HP…