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05/17/2023
A Scalable Method for Readable Tree Layouts
Large tree structures are ubiquitous and real-world relational datasets ...
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03/03/2023
Extreme-scale many-against-many protein similarity search
Similarity search is one of the most fundamental computations that are r...
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08/06/2022
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy
Graph Neural Networks (GNNs) are widely used to perform different machin...
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05/04/2022
Graphical Games for UAV Swarm Control Under Time-Varying Communication Networks
We propose a unified framework for coordinating Unmanned Aerial Vehicle ...
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02/05/2022
MarkovGNN: Graph Neural Networks on Markov Diffusion
Most real-world networks contain well-defined community structures where...
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12/20/2021
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods
Graph representation learning is a fast-growing field where one of the m...
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12/19/2021
Parallel Algorithms for Adding a Collection of Sparse Matrices
We develop a family of parallel algorithms for the SpKAdd operation that...
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06/28/2021
Combinatorial BLAS 2.0: Scaling combinatorial algorithms on distributed-memory systems
Combinatorial algorithms such as those that arise in graph analysis, mod...
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04/07/2021
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Electronic Health Record (EHR) data has been of tremendous utility in Ar...
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12/28/2020
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
Computational prediction of in-hospital mortality in the setting of an i...
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11/07/2020
FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks
We develop a fused matrix multiplication kernel that unifies sampled den...
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10/16/2020
Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in ...
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09/30/2020
Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices
Identifying similar protein sequences is a core step in many computation...
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09/23/2020
SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting
Subgraph counting aims to count occurrences of a template T in a given n...
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09/17/2020
Force2Vec: Parallel force-directed graph embedding
A graph embedding algorithm embeds a graph into a low-dimensional space ...
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04/03/2020
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
We present a multi-filtering Graph Convolution Neural Network (GCN) fram...
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02/26/2020
Bandwidth-Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blocking
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in ...
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02/24/2020
Optimizing High Performance Markov Clustering for Pre-Exascale Architectures
HipMCL is a high-performance distributed memory implementation of the po...
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02/11/2020
BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory
Force-directed algorithms are widely used to generate aesthetically plea...
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01/20/2020
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing con...
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10/14/2019
FastSV: A Distributed-Memory Connected Component Algorithm with Fast Convergence
This paper presents a new distributed-memory algorithm called FastSV for...
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03/11/2019
A GraphBLAS Approach for Subgraph Counting
Subgraph counting aims to count the occurrences of a subgraph template T...
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04/05/2018
High-performance sparse matrix-matrix products on Intel KNL and multicore architectures
Sparse matrix-matrix multiplication (SpGEMM) is a computational primitiv...
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01/30/2018
A distributed-memory approximation algorithm for maximum weight perfect bipartite matching
We design and implement an efficient parallel approximation algorithm fo...
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12/12/2017
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
We propose a new integrated method of exploiting model, batch and domain...
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12/12/2017
Integrated Model and Data Parallelism in Training Neural Networks
We propose a new integrated method of exploiting both model and data par...
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10/30/2017
Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning
Undirected graphical models compactly represent the structure of large, ...
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10/26/2016
