Search Results for author:
Found 9 papers, 2 papers with code
FFTc: An MLIR Dialect for Developing HPC Fast Fourier Transform Libraries
Discrete Fourier Transform (DFT) libraries are one of the most critical software components for scientific computing.
Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
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.
Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain
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.
A Deep Learning-Based Particle-in-Cell Method for Plasma Simulations
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.
StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas.
Automatic Particle Trajectory Classification in Plasma Simulations
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.
Automated classification of plasma regions using 3D particle energy distributions
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI).
Characterizing Deep-Learning I/O Workloads in TensorFlow
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
NVIDIA Tensor Core Programmability, Performance & Precision
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
