In EDS ansehen

A Customized Processor for Energy Efficient Scientific Computing.

Gespeichert in:
Bibliographische Detailangaben
Titel: A Customized Processor for Energy Efficient Scientific Computing.
Autoren: Sethia, Ankit, Dasika, Ganesh, Mudge, Trevor, Mahlke, Scott
Quelle: IEEE Transactions on Computers; Dec2012, Vol. 61 Issue 12, p1711-1723, 13p
Schlagwörter: MICROPROCESSORS, ELECTRIC power consumption, COMPUTATIONAL complexity, GRAPHICS processing units, COMPUTER programming, SUPERCOMPUTERS, BENCHMARK testing (Engineering)
Abstract: The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture—an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Computers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture—an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs. [ABSTRACT FROM AUTHOR]
ISSN:00189340
DOI:10.1109/TC.2012.144