NDLI: Integrating profile-driven parallelism detection and machine-learning-based mapping

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Integrating profile-driven parallelism detection and machine-learning-based mapping

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Integrating profile-driven parallelism detection and machine-learning-based mapping

Content Provider	ACM Digital Library
Author	O'boyle, Michael F. P. Tournavitis, Georgios Wang, Zheng Franke, Björn
Copyright Year	2014
Abstract	Compiler-based auto-parallelization is a much-studied area but has yet to find widespread application. This is largely due to the poor identification and exploitation of application parallelism, resulting in disappointing performance far below that which a skilled expert programmer could achieve. We have identified two weaknesses in traditional parallelizing compilers and propose a novel, integrated approach resulting in significant performance improvements of the generated parallel code. Using profile-driven parallelism detection, we overcome the limitations of static analysis, enabling the identification of more application parallelism, and only rely on the user for final approval. We then replace the traditional target-specific and inflexible mapping heuristics with a machine-learning-based prediction mechanism, resulting in better mapping decisions while automating adaptation to different target architectures. We have evaluated our parallelization strategy on the NAS and SPEC CP benchmarks and two different multicore platforms (dual quad-core Intel Xeon SMP and dual-socket QS20 Cell blade). We demonstrate that our approach not only yields significant improvements when compared with state-of-the-art parallelizing compilers but also comes close to and sometimes exceeds the performance of manually parallelized codes. On average, our methodology achieves 96% of the performance of the hand-tuned OpenMP NAS and SPEC parallel benchmarks on the Intel Xeon platform and gains a significant speedup for the IBM Cell platform, demonstrating the potential of profile-guided and machine-learning- based parallelization for complex multicore platforms.
Starting Page	1
Ending Page	26
Page Count	26
File Format	PDF
ISSN	15443566
e-ISSN	15443973
DOI	10.1145/2579561
Volume Number	11
Issue Number	1
Journal	ACM Transactions on Architecture and Code Optimization (TACO)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Auto-parallelization OpenMP Machine-learning-based parallelism mapping Profile-driven parallelism detection
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Information Systems Software

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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping

Towards a holistic approach to auto-parallelization:integrating profile-driven parallelism detection and machine-learning based mapping

Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping (2009)

Using machine learning to partition streaming programs

Optimizing software runtime systems for speculative parallelization

Generalized Task Parallelism

Parallelization libraries: Characterizing and reducing overheads

Automatic and Portable Mapping of Data Parallel Programs to OpenCL for GPU-Based Heterogeneous Systems

Adaptive Multi-versioning for OpenMP Parallelization via Machine Learning

Integrating profile-driven parallelism detection and machine-learning-based mapping

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