22 citations · 41 across the 12 of their papers we have counts for
13 papers
Dr. Top-k: Delegate-Centric Top-k on GPUs
Anil Gaihre, Da Zheng, Scott Weitze +5
Recent top- computation efforts explore the possibility of revising various sorting algorithms to answer top- queries on GPUs. These endeavors, unfortunately, perform signifi…
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…
Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers
Hang Liu, Yufei Ding, Da Zheng +2
Shrinking transistors, which powered the advancement of computing in the past half century, has stalled due to power wall; now extreme heterogeneity is promised to be the next driv…
Supervised Dimensionality Reduction for Big Data
Joshua T. Vogelstein, Eric Bridgeford, Minh Tang +4
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will b…
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…
Optimize Unsynchronized Garbage Collection in an SSD Array
Da Zheng, Randal Burns, Alexander S. Szalay
Solid state disks (SSDs) have advanced to outperform traditional hard drives significantly in both random reads and writes. However, heavy random writes trigger fre- quent garbage…
Active Community Detection in Massive Graphs
Heng Wang, Da Zheng, Randal Burns +1
A canonical problem in graph mining is the detection of dense communities. This problem is exacerbated for a graph with a large order and size -- the number of vertices and edges -…
Hadoop in Low-Power Processors
Da Zheng, Alexander Szalay, Andreas Terzis
In our previous work we introduced a so-called Amdahl blade microserver that combines a low-power Atom processor, with a GPU and an SSD to provide a balanced and energy-efficient s…
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…