Machine Learning Toolkit for Extreme Scale

Software · Mon Mar 31 00:00:00 EDT 2014

OSTI ID:1231737

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are considered in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets

• Short Name / Acronym: MATEX; 002974WKSTN00
• Version: 00
• Programming Language(s): Medium: X; OS: Linux; Compatibility: Workstation
• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• Contributing Organization: Abhinav Vishnu PNNL Jeyanthi Narasmihan (student intern) Washington State University Khushbu Aqarwal PNNL
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1231737
• Country of Origin: United States