NDLI: GPU Versus FPGA for High Productivity Computing

Content Provider	IEEE Xplore Digital Library
Author	Jones, D.H. Powell, A. Bouganis, C.-S. Cheung, P.Y.K.
Copyright Year	2010
Abstract	Heterogeneous or co-processor architectures are becoming an important component of high productivity computing systems (HPCS). In this work the performance of a GPU based HPCS is compared with the performance of a commercially available FPGA based HPC. Contrary to previous approaches that focussed on specific examples, a broader analysis is performed by considering processes at an architectural level. A set of benchmarks is employed that use different process architectures in order to exploit the benefits of each technology. These include the asynchronous pipelines common to "map" tasks, a partially synchronous tree common to "reduce" tasks and a fully synchronous, fully connected mesh. We show that the GPU is more productive than the FPGA architecture for most of the benchmarks and conclude that FPGA-based HPCS is being marginalised by GPUs.
Starting Page	119
Ending Page	124
File Size	1015888
Page Count	6
File Format	PDF
ISBN	9781424478422
ISSN	19461488
e-ISBN	9781424478439
DOI	10.1109/FPL.2010.32
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing unit Field programmable gate arrays Benchmark testing Random access memory Computer architecture Performance evaluation Productivity FPGA High Productivity Computing GPU
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
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

Accelerating High Performance Computing Applications: Using CPUs, GPUs, Hybrid CPU/GPU, and FPGAs

Spectral Method Characterization on FPGA and GPU Accelerators

A heterogeneous platform with GPU and FPGA for power efficient high performance computing

Image convolution processing: A GPU versus FPGA comparison

BLAS Comparison on FPGA, CPU and GPU

An exploration of heterogeneous systems

Direct virtual memory access from FPGA for high-productivity heterogeneous computing

GPU Approach to FPGA placement based on star+

Just-in-time compilation for FPGA processor cores

GPU Versus FPGA for High Productivity Computing

Similar Documents

Accelerating High Performance Computing Applications: Using CPUs, GPUs, Hybrid CPU/GPU, and FPGAs

Spectral Method Characterization on FPGA and GPU Accelerators

A heterogeneous platform with GPU and FPGA for power efficient high performance computing

Image convolution processing: A GPU versus FPGA comparison

BLAS Comparison on FPGA, CPU and GPU

An exploration of heterogeneous systems

Direct virtual memory access from FPGA for high-productivity heterogeneous computing

GPU Approach to FPGA placement based on star+

Just-in-time compilation for FPGA processor cores

GPU Versus FPGA for High Productivity Computing