NDLI: Multi- and many-core data mining with adaptive sparse grids

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Multi- and many-core data mining with adaptive sparse grids

Content Provider	ACM Digital Library
Author	Heinecke, Alexander Pflüger, Dirk
Abstract	Gaining knowledge out of vast datasets is a main challenge in data-driven applications nowadays. Sparse grids provide a numerical method for both classification and regression in data mining which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. Due to the recursive nature of sparse grid algorithms, they impose a challenge for the parallelization on modern hardware architectures such as accelerators. In this paper, we present the parallelization on several current task- and data-parallel platforms, covering multi-core CPUs with vector units, GPUs, and hybrid systems. Furthermore, we analyze the suitability of parallel programming languages for the implementation. Considering hardware, we restrict ourselves to the x86 platform with SSE and AVX vector extensions and to NVIDIA's Fermi architecture for GPUs. We consider both multi-core CPU and GPU architectures independently, as well as hybrid systems with up to 12 cores and 2 Fermi GPUs. With respect to parallel programming, we examine both the open standard OpenCL and Intel Array Building Blocks, a recently introduced high-level programming approach. As the baseline, we use the best results obtained with classically parallelized sparse grid algorithms and their OpenMP-parallelized intrinsics counterpart (SSE and AVX instructions), reporting both single and double precision measurements. The huge data sets we use are a real-life dataset stemming from astrophysics and an artificial one which exhibits challenging properties. In all settings, we achieve excellent results, obtaining speedups of more than 60 using single precision on a hybrid system.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450306980
DOI	10.1145/2016604.2016640
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Adaptivity Sparse grids Accelerators Many-core Multi-core Simd Hybrid acceleration Gpgpu
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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