187 citations · 201 across the 4 of their papers we have counts for
6 papers
SMaLL: A Software Framework for portable Machine Learning Libraries
Upasana Sridhar, Nicholai Tukanov, Elliott Binder +3
Interest in deploying Deep Neural Network (DNN) inference on edge devices has resulted in an explosion of the number and types of hardware platforms to use. While the high-level pr…
Delayed Asynchronous Iterative Graph Algorithms
Mark P. Blanco, Scott McMillan, Tze Meng Low
Iterative graph algorithms often compute intermediate values and update them as computation progresses. Updated output values are used as inputs for computations in current or subs…
LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms
Gábor Szárnyas, David A. Bader, Timothy A. Davis +4
Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms that targets algorithm developers. LAGraph bui…
Towards an Objective Metric for the Performance of Exact Triangle Count
Mark P. Blanco, Scott McMillan, Tze Meng Low
The performance of graph algorithms is often measured in terms of the number of traversed edges per second (TEPS). However, this performance metric is inadequate for a graph operat…
Delta-stepping SSSP: from Vertices and Edges to GraphBLAS Implementations
Upasana Sridhar, Mark Blanco, Rahul Mayuranath +3
GraphBLAS is an interface for implementing graph algorithms. Algorithms implemented using the GraphBLAS interface are cast in terms of linear algebra-like operations. However, many…
Mathematical Foundations of the GraphBLAS
Jeremy Kepner, Peter Aaltonen, David Bader +13
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS…