Search Results for author:
Found 8 papers, 5 papers with code
Distributed Matrix-Based Sampling for Graph Neural Network Training
We provide experimental results on the largest Open Graph Benchmark (OGB) datasets on $128$ GPUs, and show that our pipeline is $2. 5\times$ faster than Quiver (a distributed extension to PyTorch-Geometric) on a $3$-layer GraphSAGE network.
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries.
Graph Representation Learning
Protein Function Prediction
+1
Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly
In this work, we introduce new distributed-memory parallel algorithms for overlap detection and layout simplification steps of de novo genome assembly, and implement them in the diBELLA 2D pipeline.
Distributed, Parallel, and Cluster Computing Genomics
Reducing Communication in Graph Neural Network Training
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data.
QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space
We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice.
Quantum Physics
LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment
To highlight the impact of our work on a real-world application, we couple LOGAN with a many-to-many long-read alignment software called BELLA, and demonstrate that our implementation improves the overall BELLA runtime by up to 10. 6x.
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data.
Accelerating Science: A Computing Research Agenda
The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery.
