22 citations · 63 across the 13 of their papers we have counts for
16 papers
Nimble GNN Embedding with Tensor-Train Decomposition
Chunxing Yin, Da Zheng, Israt Nisa +3
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario whe…
"Smarter" NICs for faster molecular dynamics: a case study
Sara Karamati, Clayton Hughes, K. Scott Hemmert +6
This work evaluates the benefits of using a "smart" network interface card (SmartNIC) as a compute accelerator for the example of the MiniMD molecular dynamics proxy application. T…
Online Model Swapping in Architectural Simulation
Patrick Lavin, Jeffrey Young, Rich Vuduc +1
As systems and applications grow more complex, detailed simulation takes an ever increasing amount of time. The prospect of increased simulation time resulting in slower design ite…
CUP: Cluster Pruning for Compressing Deep Neural Networks
Rahul Duggal, Cao Xiao, Richard Vuduc +1
We propose Cluster Pruning (CUP) for compressing and accelerating deep neural networks. Our approach prunes similar filters by clustering them based on features derived from both t…
Load-Balanced Sparse MTTKRP on GPUs
Israt Nisa, Jiajia Li, Aravind Sukumaran-Rajam +2
Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most computationally expensive kernels in sparse tensor computations. This work focuses on optimizing the M…
Programming Strategies for Irregular Algorithms on the Emu Chick
Eric Hein, Srinivas Eswar, Abdurrahman Yaşar +7
The Emu Chick prototype implements migratory memory-side processing in a novel hardware system. Rather than transferring large amounts of data across the system interconnect, the E…
A Microbenchmark Characterization of the Emu Chick
Jeffrey S. Young, Eric Hein, Srinivas Eswar +5
The Emu Chick is a prototype system designed around the concept of migratory memory-side processing. Rather than transferring large amounts of data across power-hungry, high-latenc…
A Simple Methodology for Computing Families of Algorithms
Devangi N. Parikh, Margaret E. Myers, Richard Vuduc +1
Discovering "good" algorithms for an operation is often considered an art best left to experts. What if there is a simple methodology, an algorithm, for systematically deriving a f…
Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems
Yang You, James Demmel, Cho-Jui Hsieh +1
We propose two new methods to address the weak scaling problems of KRR: the Balanced KRR (BKRR) and K-means KRR (KKRR). These methods consider alternative ways to partition the inp…
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping
Ioakeim Perros, Evangelos E. Papalexakis, Haesun Park +5
This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the…
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang +4
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling m…
Efficient Communications in Training Large Scale Neural Networks
Linnan Wang, Wei Wu, George Bosilca +2
We consider the problem of how to reduce the cost of communication that is required for the parallel training of a neural network. The state-of-the-art method, Bulk Synchronous Par…
Sparse Hierarchical Tucker Factorization and its Application to Healthcare
Ioakeim Perros, Robert Chen, Richard Vuduc +1
We propose a new tensor factorization method, called the Sparse Hierarchical-Tucker (Sparse H-Tucker), for sparse and high-order data tensors. Sparse H-Tucker is inspired by its na…
Wanted: Floating-Point Add Round-off Error instruction
Marat Dukhan, Richard Vuduc, Jason Riedy
We propose a new instruction (FPADDRE) that computes the round-off error in floating-point addition. We explain how this instruction benefits high-precision arithmetic operations i…
Branch-Avoiding Graph Algorithms
Oded Green, Marat Dukhan, Richard Vuduc
This paper quantifies the impact of branches and branch mispredictions on the single-core performance for two classes of graph problems. Specifically, we consider classical algorit…
Sustainable Software Development for Next-Gen Sequencing (NGS) Bioinformatics on Emerging Platforms
Shel Swenson, Yogesh Simmhan, Viktor Prasanna +4
DNA sequence analysis is fundamental to life science research. The rapid development of next generation sequencing (NGS) technologies, and the richness and diversity of application…