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Main Title: GPGPU workload characteristics and performance analysis
Author(s): Lal, Sohan
Lucas, Jan
Andersch, Michael
Álvarez-Mesa, Mauricio
Elhossini, Ahmed
Juurlink, Ben
Type: Conference Object
Language Code: en
Abstract: GPUs are much more power-efficient devices compared to CPUs, but due to several performance bottlenecks, the performance per watt of GPUs is often much lower than what could be achieved theoretically. To sustain and continue high performance computing growth, new architectural and application techniques are required to create power-efficient computing systems. To find such techniques, however, it is necessary to study the power consumption at a detailed level and understand the bottlenecks which cause low performance. Therefore, in this paper, we study GPU power consumption at component level and investigate the bottlenecks that cause low performance and low energy efficiency. We divide the low performance kernels into low occupancy and full occupancy categories. For the low occupancy category, we study if increasing the occupancy helps in increasing performance and energy efficiency. For the full occupancy category, we investigate if these kernels are limited by memory bandwidth, coalescing efficiency, or SIMD utilization.
Issue Date: 2014
Date Available: 9-Mar-2017
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): benchmark testing
graphics processing units
instruction sets
power demand
Sponsor/Funder: EC/FP7/288653/EU/Low-Power Parallel Computing on GPUs/LPGPU
Proceedings Title: 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation : SAMOS
Publisher: IEEE
Publisher Place: New York, NY [u.a.]
Publisher DOI: 10.1109/SAMOS.2014.6893202
Page Start: 115
Page End: 124
ISBN: 978-1-4799-3771-4
Appears in Collections:FG Architektur eingebetteter Systeme » Publications

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