OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

Journal Article · Wed Sep 21 00:00:00 EDT 2016 · Lecture Notes in Computer Science

DOI:https://doi.org/10.1007/978-3-319-45550-1_2· OSTI ID:1378982

Meng, Zhaoyi ^[1]; Koniges, Alice ^[2]; He, Yun Helen ^[2]; Williams, Samuel ^[2]; Kurth, Thorsten ^[2]; Cook, Brandon ^[2]; Deslippe, Jack ^[2]; Bertozzi, Andrea L. ^[3]

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1. Univ. of California, Los Angeles, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
3. Univ. of California, Los Angeles, CA (United States)

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelize the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF); US Air Force Office of Scientific Research (AFOSR)
• Grant/Contract Number: AC02-05CH11231; DMS-1417674; DMS-1045536; FA9550-10-1-0569
• OSTI ID: 1378982
• Journal Information: Lecture Notes in Computer Science, Vol. 9903; ISSN 0302-9743
• Publisher: Springer
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
semi-supervised
unsupervised
data
algorithms
OpenMP
optimization