b'Aydin Buluc'

10 Years Later: Cloud Computing is Closing the Performance Gap

Large scale modeling and simulation problems, from nanoscale materials t...
11/02/2020 ∙ by Giulia Guidi, et al. ∙ 0 ∙ share

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

Understanding protein structure-function relationships is a key challeng...
10/30/2020 ∙ by Nicolas Swenson, et al. ∙ 13 ∙ share

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

One of the most computationally intensive tasks in computational biology...
10/20/2020 ∙ by Giulia Guidi, et al. ∙ 0 ∙ share

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

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in ...
10/16/2020 ∙ by Md Taufique Hussain, et al. ∙ 0 ∙ share

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Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computation...
09/30/2020 ∙ by Oguz Selvitopi, et al. ∙ 0 ∙ share

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Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks t...
05/07/2020 ∙ by Alok Tripathy, et al. ∙ 13 ∙ share

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Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the po...
02/24/2020 ∙ by Oguz Selvitopi, et al. ∙ 0 ∙ share

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LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Pairwise sequence alignment is one of the most computationally intensive...
02/12/2020 ∙ by Alberto Zeni, et al. ∙ 0 ∙ share

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diBELLA: Distributed Long Read to Long Read Alignment

We present a parallel algorithm and scalable implementation for genome a...
01/27/2020 ∙ by Marquita Ellis, et al. ∙ 0 ∙ share

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The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing con...
01/20/2020 ∙ by Katherine Yelick, et al. ∙ 0 ∙ share

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A High-Throughput Solver for Marginalized Graph Kernels on GPU

We present the design of a solver for the efficient and high-throughput ...
10/14/2019 ∙ by Yu-Hang Tang, et al. ∙ 0 ∙ share

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RDMA vs. RPC for Implementing Distributed Data Structures

Distributed data structures are key to implementing scalable application...
10/04/2019 ∙ by Benjamin Brock, et al. ∙ 0 ∙ share

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GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to ...
08/04/2019 ∙ by Carl Yang, et al. ∙ 0 ∙ share

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BCL: A Cross-Platform Distributed Container Library

One-sided communication is a useful paradigm for irregular parallel appl...
10/30/2018 ∙ by Benjamin Brock, et al. ∙ 0 ∙ share

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Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overl...
09/19/2018 ∙ by Evangelos Georganas, et al. ∙ 0 ∙ share

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Implementing Push-Pull Efficiently in GraphBLAS

We factor Beamer's push-pull, also known as direction-optimized breadth-...
04/10/2018 ∙ by Carl Yang, et al. ∙ 0 ∙ share

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High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitiv...
04/05/2018 ∙ by Yusuke Nagasaka, et al. ∙ 0 ∙ share

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Design Principles for Sparse Matrix Multiplication on the GPU

We implement two novel algorithms for sparse-matrix dense-matrix multipl...
03/22/2018 ∙ by Carl Yang, et al. ∙ 0 ∙ share

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A distributed-memory approximation algorithm for maximum weight perfect bipartite matching

We design and implement an efficient parallel approximation algorithm fo...
01/30/2018 ∙ by Ariful Azad, et al. ∙ 0 ∙ share

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Integrated Model, Batch and Domain Parallelism in Training Neural Networks

We propose a new integrated method of exploiting model, batch and domain...
12/12/2017 ∙ by Amir Gholami, et al. ∙ 0 ∙ share

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Integrated Model and Data Parallelism in Training Neural Networks

We propose a new integrated method of exploiting both model and data par...
12/12/2017 ∙ by Amir Gholami, et al. ∙ 0 ∙ share

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Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning

Undirected graphical models compactly represent the structure of large, ...
10/30/2017 ∙ by Penporn Koanantakool, et al. ∙ 0 ∙ share

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The Reverse Cuthill-McKee Algorithm in Distributed-Memory

Ordering vertices of a graph is key to minimize fill-in and data structu...
10/26/2016 ∙ by Ariful Azad, et al. ∙ 0 ∙ share

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Mathematical Foundations of the GraphBLAS

The GraphBLAS standard (GraphBlas.org) is being developed to bring the p...
06/18/2016 ∙ by Jeremy Kepner, et al. ∙ 0 ∙ share

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Aydin Buluc

Featured Co-authors

Kurt Keutzer

Yuxin Chen

Jeremy Kepner

Amir Gholami

John D. Owens

Ariful Azad

Satoshi Matsuoka

Carl Yang

Henning Meyerhenke

Manoj Kumar

Nan Ding

10 Years Later: Cloud Computing is Closing the Performance Gap

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

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

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

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Reducing Communication in Graph Neural Network Training

Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

diBELLA: Distributed Long Read to Long Read Alignment

The Parallelism Motifs of Genomic Data Analysis

A High-Throughput Solver for Marginalized Graph Kernels on GPU

RDMA vs. RPC for Implementing Distributed Data Structures

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

BCL: A Cross-Platform Distributed Container Library

Extreme Scale De Novo Metagenome Assembly

Implementing Push-Pull Efficiently in GraphBLAS

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Design Principles for Sparse Matrix Multiplication on the GPU

A distributed-memory approximation algorithm for maximum weight perfect bipartite matching

Integrated Model, Batch and Domain Parallelism in Training Neural Networks

Integrated Model and Data Parallelism in Training Neural Networks

Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning

The Reverse Cuthill-McKee Algorithm in Distributed-Memory

Mathematical Foundations of the GraphBLAS