• Publications
  • Influence
The HPC Challenge (HPCC) benchmark suite
This tutorial will introduce attendees to HPCC, provide tools to examine differences in HPC architectures, and give hands-on training that will hopefully lead to better understanding of parallel environments.
Static graph challenge: Subgraph isomorphism
The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems.
Mathematical foundations of the GraphBLAS
This paper provides an introduction to the mathematics of the GraphBLAS, a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments.
Introduction to the HPC Challenge Benchmark Suite
The HPC Challenge benchmark suite is designed to augment the Top500 list, providing benchmarks that bound the performance of many real applications as a function of memory access characteristics e.g., spatial and temporal locality, and providing a framework for including additional tests.
Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis
This work demonstrates launching 32,000 TensorFlow processes in 4 seconds and 262,000 Octave processes in 40 seconds, which allow researchers to rapidly explore novel machine learning architecture and data analysis algorithms.
The BigDAWG polystore system and architecture
Polystore databases, the current BigDAWG architecture and its application on the MIMIC II medical dataset, initial performance results and future development plans are described.
The evolutionary status of the stellar population in the rho Ophiuchi cloud core
This contribution reports the results of an infrared imaging survey aimed at characterizing the stellar populations associated with the three densest star-forming cores in the Ophiuchus molecular
Streaming graph challenge: Stochastic block partition
This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity that employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs.
Sparse Deep Neural Network Graph Challenge
The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems.
HPC Productivity: An Overarching View
  • J. Kepner
  • Computer Science
    Int. J. High Perform. Comput. Appl.
  • 1 November 2004
This paper defines several characteristic HPC workflows that are useful for understanding how users exploit HPC systems, and discusses the role of activity and purpose benchmarks in establishing an empirical basis for HPC productivity.