37 citations · 83 across the 9 of their papers we have counts for
9 papers
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Disa Mhembere, Da Zheng, Carey E. Priebe +2
Graph datasets exceed the in-memory capacity of most standalone machines. Traditionally, graph frameworks have overcome memory limitations through scale-out, distributing computing…
clusterNOR: A NUMA-Optimized Clustering Framework
Disa Mhembere, Da Zheng, Carey E. Priebe +2
Clustering algorithms are iterative and have complex data access patterns that result in many small random memory accesses. The performance of parallel implementations suffer from…
Forest Packing: Fast, Parallel Decision Forests
James Browne, Tyler M. Tomita, Disa Mhembere +2
Machine learning has an emerging critical role in high-performance computing to modulate simulations, extract knowledge from massive data, and replace numerical models with efficie…
knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library
Disa Mhembere, Da Zheng, Carey E. Priebe +2
k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine lea…
FlashR: R-Programmed Parallel and Scalable Machine Learning using SSDs
Da Zheng, Disa Mhembere, Joshua T. Vogelstein +2
R is one of the most popular programming languages for statistics and machine learning, but the R framework is relatively slow and unable to scale to large datasets. The general ap…
Semi-External Memory Sparse Matrix Multiplication for Billion-Node Graphs
Da Zheng, Disa Mhembere, Vince Lyzinski +3
Sparse matrix multiplication is traditionally performed in memory and scales to large matrices using the distributed memory of multiple nodes. In contrast, we scale sparse matrix m…
FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs
Da Zheng, Disa Mhembere, Randal Burns +3
Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines…
MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics
William Gray Roncal, Zachary H. Koterba, Disa Mhembere +10
Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at scale using a combination of diffusion weighted magnetic resonan…
Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs
Disa Mhembere, William Gray Roncal, Daniel Sussman +6
Graphs are quickly emerging as a leading abstraction for the representation of data. One important application domain originates from an emerging discipline called "connectomics".…