activity
20172021
most citeddiBELLA: Distributed Long Read to Long Read Alignment

17 citations · 40 across the 7 of their papers we have counts for

collaborators

14 papers

quant-ph20214 cited

QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis

Ed Younis, Koushik Sen, Katherine Yelick +1

We present a quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encod…

cs.DC202014 cited

10 Years Later: Cloud Computing is Closing the Performance Gap

Giulia Guidi, Marquita Ellis, Aydin Buluc +2

Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answer…

q-bio.BM20205 cited

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluc +2

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While…

cs.DC2020

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

Giulia Guidi, Oguz Selvitopi, Marquita Ellis +3

One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous…

cs.CY2020

Opportunities and Challenges for Next Generation Computing

Gregory D. Hager, Mark D. Hill, Katherine Yelick

Computing has dramatically changed nearly every aspect of our lives, from business and agriculture to communication and entertainment. As a nation, we rely on computing in the desi…

cs.LG2020

Reducing Communication in Graph Neural Network Training

Alok Tripathy, Katherine Yelick, Aydin Buluc

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as spars…