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20172025
most citedScaling Deep Learning on GPU and Knights Landing clusters

71 citations · 129 across the 11 of their papers we have counts for

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19 papers · 1 filter

cs.DC2025

Distributed-Memory Parallel Algorithms for Fixed-Radius Near Neighbor Graph Construction

Gabriel Raulet, Dmitriy Morozov, Aydin Buluc +1

Computing fixed-radius near-neighbor graphs is an important first step for many data analysis algorithms. Near-neighbor graphs connect points that are close under some metric, endo…

cs.DC2022

Distributed-Memory Sparse Kernels for Machine Learning

Vivek Bharadwaj, Aydin Buluç, James Demmel

Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document cl…

cs.DC2021

Combinatorial BLAS 2.0: Scaling combinatorial algorithms on distributed-memory systems

Ariful Azad, Oguz Selvitopi, Md Taufique Hussain +2

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial B…

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…

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.DC2020

Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale

Md Taufique Hussain, Oguz Selvitopi, Aydin Buluç +1

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. In this paper, we consider SpGEMMs perf…