no code implementations • 7 Jul 2019 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns
Emerging frameworks avoid the network bottleneck of distributed data with Semi-External Memory (SEM) that uses a single multicore node and operates on graphs larger than memory.
Distributed, Parallel, and Cluster Computing Databases
1 code implementation • 28 Jun 2016 • Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns
The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets.
Distributed, Parallel, and Cluster Computing
2 code implementations • 21 Apr 2016 • Da Zheng, Disa Mhembere, Joshua T. Vogelstein, Carey E. Priebe, Randal Burns
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.
Distributed, Parallel, and Cluster Computing
2 code implementations • 9 Feb 2016 • Da Zheng, Disa Mhembere, Vince Lyzinski, Joshua Vogelstein, Carey E. Priebe, Randal Burns
In contrast, we scale sparse matrix multiplication beyond memory capacity by implementing sparse matrix dense matrix multiplication (SpMM) in a semi-external memory (SEM) fashion; i. e., we keep the sparse matrix on commodity SSDs and dense matrices in memory.
Distributed, Parallel, and Cluster Computing
