87 citations · 215 across the 14 of their papers we have counts for
15 papers
A Proposed Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (FAMLIES)
Devin A. Matthews, Tze Meng Low, Margaret E. Myers +2
We leverage highly successful prior projects sponsored by multiple NSF grants and gifts from industry: the BLAS-like Library Instantiation Software (BLIS) and the libflame efforts…
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Keita Teranishi, Harshitha Menon, William F. Godoy +25
We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular…
Enabling Pivoting in the Formal Derivation of LU factorization
Robert van de Geijn, Margaret E. Myers, Devangi N. Parikh +2
The FLAME methodology for deriving linear algebra algorithms from specification, first introduced around 2000, has been successfully applied to a broad cross section of operations.…
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…
Exploration of Fine-Grained Parallelism for Load Balancing Eager K-truss on GPU and CPU
Mark Blanco, Tze Meng Low, Kyungjoo Kim
In this work we present a performance exploration on Eager K-truss, a linear-algebraic formulation of the K-truss graph algorithm. We address performance issues related to load imb…
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…
A Flexible Framework for Parallel Multi-Dimensional DFTs
Doru Thom Popovici, Martin D. Schatz, Franz Franchetti +1
Multi-dimensional discrete Fourier transforms (DFT) are typically decomposed into multiple 1D transforms. Hence, parallel implementations of any multi-dimensional DFT focus on para…
CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors
Sanghamitra Dutta, Ziqian Bai, Tze Meng Low +1
This work proposes the first strategy to make distributed training of neural networks resilient to computing errors, a problem that has remained unsolved despite being first posed…
A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication
Sanghamitra Dutta, Ziqian Bai, Haewon Jeong +2
This paper has two contributions. First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matri…
High Performance Zero-Memory Overhead Direct Convolutions
Jiyuan Zhang, Franz Franchetti, Tze Meng Low
The computation of convolution layers in deep neural networks typically rely on high performance routines that trade space for time by using additional memory (either for packing p…
Coded FFT and Its Communication Overhead
Haewon Jeong, Tze Meng Low, Pulkit Grover
We propose a coded computing strategy and examine communication costs of coded computing algorithms to make distributed Fast Fourier Transform (FFT) resilient to errors during the…
Automating the Last-Mile for High Performance Dense Linear Algebra
Richard Michael Veras, Tze Meng Low, Tyler Michael Smith +2
High performance dense linear algebra (DLA) libraries often rely on a general matrix multiply (Gemm) kernel that is implemented using assembly or with vector intrinsics. In particu…
Exploiting Symmetry in Tensors for High Performance: Multiplication with Symmetric Tensors
Martin D. Schatz, Tze Meng Low, Robert A. van de Geijn +1
Symmetric tensor operations arise in a wide variety of computations. However, the benefits of exploiting symmetry in order to reduce storage and computation is in conflict with a d…