DocumentCode :
1116796
Title :
Convergence of Recognition, Mining, and Synthesis Workloads and Its Implications
Author :
Chen, Yen-Kuang ; Chhugani, Jatin ; Dubey, Pradeep ; Hughes, Christopher J. ; Kim, Daehyun ; Kumar, Sanjeev ; Lee, Victor W. ; Nguyen, Anthony D. ; Smelyanskiy, Mikhail
Author_Institution :
Intel Corp., Santa Clara
Volume :
96
Issue :
5
fYear :
2008
fDate :
5/1/2008 12:00:00 AM
Firstpage :
790
Lastpage :
807
Abstract :
This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with an ability to iteratively refine the model in real-time. We find such processing needs at the heart of many emerging applications and services. This processing is further decomposed in terms of an integration of three fundamental compute capabilities-recognition, mining, and synthesis (RMS). The set of RMS workloads is examined next in terms of usage, mathematical models, numerical algorithms, and underlying data structures. Our analysis suggests a workload convergence that is analyzed next for its platform implications. In summary, a diverse set of emerging RMS applications from market segments like graphics, gaming, media-mining, unstructured information management, financial analytics, and interactive virtual communities presents a relatively focused, highly overlapping set of common platform challenges. A general-purpose processing platform designed to address these challenges has the potential for significantly enhancing users´ experience and programmer productivity.
Keywords :
data mining; pattern recognition; data mining; data structures; financial analytics; gaming; general-purpose analytics engine; graphics; interactive virtual communities; mathematical models; media mining; numerical algorithms; pattern recognition; synthesis workloads; unstructured information management; Convergence; Data structures; Graphics; Heart; Information analysis; Information management; Mathematical model; Process design; Productivity; Programming profession; Algorithms; data structures; emerging applications; parallel architectures;
fLanguage :
English
Journal_Title :
Proceedings of the IEEE
Publisher :
ieee
ISSN :
0018-9219
Type :
jour
DOI :
10.1109/JPROC.2008.917729
Filename :
4479863
Link To Document :
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