A Hybrid Data Prefetching Architecture for Data-Access Efficiency

Colloq: Speaker: 
Yong Chen
Colloq: Speaker Institution: 
Illinois Institute of Technology
Colloq: Date and Time: 
Fri, 2009-02-20 10:00
Colloq: Location: 
ORNL, Bldg. 5700, Room L202
Colloq: Host: 
Jeff Vetter
Colloq: Host Email: 
vetter@ornl.gov
Colloq: Abstract: 
Though computing capability continues increasing rapidly and the multi-core/many-core architecture has emerged as the norm of future high-performance processor, data-access technology still lacks far behind, having a severe impact on overall system performance. In the meantime, a large variety of applications including scientific simulations, visualization applications, information retrieval, etc., have made computing more data centric than computing centric. The preliminary study on the scalability of computing systems and applications has identified that data-access delay, not the processor speed, has become the performance bottleneck of computing and a dominant factor that decides the sustained performance of computing systems. This is especially true for high-end computing (HEC) and high-performance computing (HPC) where performance is keen. The data-access performance bottleneck has been recognized as one of the most critical problems that HEC/HPC community faces.In this talk, a Hybrid Adaptive Prefetching (HAP) architecture will be introduced to bridge the gap between computing speed and data-access speed. The HAP architecture improves data-access performance via two stages, cache-memory stage by leveraging specialized hardware solutions and memory-disk stage by exploiting innovative software solutions. A specialized Data-Access History Cache and feedback-controlled adaptive data prefetching were proposed to address cache-memory stage latency reduction. Cooperative caching and prefetching, online heuristic and pre-execution prefetching were studied to enhance memory-disk stage access efficiency. Extensive experimental testing has been conducted to validate the design and verify the performance gain, and the results have demonstrated significant performance improvement. The Hybrid Adaptive Prefetching architecture can benefit numerous applications, such as scientific simulation, data mining, information retrieval, geographical information system, multimedia and visualization applications, etc. It will have a broad impact on boosting data-access performance for high-end and high-performance computing.
Colloq: Speaker Bio: 
Yong Chen is a Ph.D. student and Research Assistant in the Computer Science Department at Illinois Institute of Technology. He received his B.E. degree in Computer Engineering in 2000 and M.S. degree in Computer Science in 2003, both from University of Science and Technology of China. His research focuses on high-performance computing, parallel and distributed computing and computer architecture in general, and on optimizing data-access performance, parallel I/O, performance modeling and evaluation in particular.