Name
Abstract | ||
|---|---|---|
Tensor decompositions are a powerful technique for enabling comprehensive and complete analysis of real-world data. Data analysis through tensor decompositions involves intensive computations over large-scale irregular sparse data. Optimizing the execution of such data intensive computations is key to reducing the time-to-solution (or response time) in real-world data analysis applications. As high-performance computing (HPC) systems are increasingly used for data analysis applications, it is becoming increasingly important to optimize sparse tensor computations and execute them efficiently on modern and advanced HPC systems. In addition to utilizing the large processing capability of HPC systems, it is crucial to improve memory performance (memory usage, communication, synchronization, memory reuse, and data locality) in HPC systems. In this paper, we present multiple optimizations that are targeted towards faster and memory-efficient execution of large-scale tensor analysis on HPC systems. We demonstrate that our techniques achieve reduction in memory usage and execution time of tensor decomposition methods when they are applied on multiple datasets of varied size and structure from different application domains. We achieve up to 11× reduction in memory usage and up to 7× improvement in performance. More importantly, we enable the application of large tensor decompositions on some important datasets on a multi-core system that would not have been feasible without our optimization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/HPEC.2017.8091026 | 2017 IEEE High Performance Extreme Computing Conference (HPEC) |
Keywords | Field | DocType |
high-performance computing systems,sparse tensor computations,memory performance,memory usage,memory reuse,data locality,multiple optimizations,large-scale tensor analysis,multicore system,large-scale irregular sparse data,data intensive computations,memory-efficient parallel tensor decompositions,data analysis,HPC systems | Data structure,Synchronization,Locality,Tensor,Reuse,Computer science,Matrix decomposition,Parallel computing,Redundancy (engineering),Sparse matrix | Conference |
ISSN | ISBN | Citations |
2377-6943 | 978-1-5386-3473-8 | 8 |
PageRank | References | Authors |
0.60 | 11 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muthu Manikandan Baskaran | 1 | 493 | 33.10 |
| Tom Henretty | 2 | 12 | 1.05 |
| Benoît Pradelle | 3 | 18 | 2.49 |
| M. Harper Langston | 4 | 9 | 2.31 |
| David Bruns-Smith | 5 | 12 | 1.05 |
| James R. Ezick | 6 | 17 | 3.60 |
| Richard Lethin | 7 | 118 | 17.17 |