NDLI: Evaluation of GPU Architectures Using Spiking Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Pallipuram, V.K. Bhuiyan, M.A. Smith, M.C.
Copyright Year	2011
Abstract	During recent years General-Purpose Graphical Processing Units (GP-GPUs) have entered the field of High-Performance Computing (HPC) as one of the primary architectural focuses for many research groups working with complex scientific applications. Nvidia's Tesla C2050, codenamed Fermi, and AMD's Radeon 5870 are two devices positioned to meet the computationally demanding needs of supercomputing research groups across the globe. Though Nvidia GPUs powered by CUDA have been the frequent choices of the performance centric research groups, the introduction and growth of OpenCL has promoted AMD GP-GPUs as potential accelerator candidates that can challenge Nvidia's stronghold. These architectures not only offer a plethora of features for application developers to explore, but their radically different architectures calls for a detailed study that weighs their merits and evaluates their potential to accelerate complex scientific applications. In this paper, we present our performance analysis research comparing Nvidia's Fermi and AMD's Radeon 5870 using OpenCL as the common programming model. We have chosen four different neuron models for Spiking Neural Networks (SNNs), each with different communication and computation requirements, namely the Izhikevich, Wilson, Morris Lecar (ML), and the Hodgkin Huxley (HH) models. We compare the runtime performance of the Fermi and Radeon GPUs with an implementation that exhausts all optimization techniques available with OpenCL. Several equivalent architectural parameters of the two GPUs are studied and correlated with the application performance. In addition to the comparative study effort, our implementations were able to achieve a speed-up of 857.3x and 658.51x on the Fermi and Radeon architectures respectively for the most compute intensive HH model with a dense network containing 9.72 million neurons. The final outcome of this research is a detailed architectural comparison of the two GPU architectures with a common programming platform.
Starting Page	93
Ending Page	102
File Size	317120
Page Count	10
File Format	PDF
ISBN	9781457706356
DOI	10.1109/SAAHPC.2011.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Firing GPU Architecture Comparison Computational modeling Neurons Fermi SNNs Profiler Counters Optimization Graphics processing unit Speed-up Computer architecture AMD OpenCL Mathematical model
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
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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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