504 citations · 2k across the 109 of their papers we have counts for
9 papers · 1 filter
PACSET (Packed Serialized Trees): Reducing Inference Latency for Tree Ensemble Deployment
Meghana Madhyastha, Kunal Lillaney, James Browne +2
We present methods to serialize and deserialize tree ensembles that optimize inference latency when models are not already loaded into memory. This arises whenever models are large…
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…
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…
An SSD-based eigensolver for spectral analysis on billion-node graphs
Da Zheng, Randal Burns, Joshua Vogelstein +2
Many eigensolvers such as ARPACK and Anasazi have been developed to compute eigenvalues of a large sparse matrix. These eigensolvers are limited by the capacity of RAM. They run in…
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…
The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience
Randal Burns, William Gray Roncal, Dean Kleissas +17
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for t…