no code implementations • 14 Jul 2022 • Yifei He, Artur Podobas, Måns I. Andersson, Stefano Markidis
Discrete Fourier Transform (DFT) libraries are one of the most critical software components for scientific computing.
no code implementations • 27 May 2022 • Ayesha Afzal, Georg Hager, Gerhard Wellein, Stefano Markidis
This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs.
1 code implementation • 14 Jul 2021 • Martin Svedin, Artur Podobas, Steven W. D. Chien, Stefano Markidis
One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning techniques that are inspired by brain models.
no code implementations • 5 Jul 2021 • Xavier Aguilar, Stefano Markidis
We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using Deep-Learning (DL) to calculate the electric field from the electron phase space.
1 code implementation • 9 Jun 2021 • Artur Podobas, Martin Svedin, Steven W. D. Chien, Ivy B. Peng, Naresh Balaji Ravichandran, Pawel Herman, Anders Lansner, Stefano Markidis
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas.
no code implementations • 11 Oct 2020 • Stefano Markidis, Ivy Peng, Artur Podobas, Itthinat Jongsuebchoke, Gabriel Bengtsson, Pawel Herman
Our overall goal is to provide a general workflow for exploring particle trajectory space and automatically classifying particle trajectories from plasma simulations in an unsupervised manner.
no code implementations • 15 Aug 2019 • Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti, Andrey Divin, Gian Luca Delzanno, Sven Anderzen, Pawel Herman, Steven W. D. Chien, Levon Avanov, Andrew P. Dimmock, Stefano Markidis
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI).
no code implementations • 6 Oct 2018 • Steven W. D. Chien, Stefano Markidis, Chaitanya Prasad Sishtla, Luis Santos, Pawel Herman, Sai Narasimhamurthy, Erwin Laure
To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow.
Distributed, Parallel, and Cluster Computing
no code implementations • 11 Mar 2018 • Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter
After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively.
Distributed, Parallel, and Cluster Computing Performance