An Integrated Framework for Performance Engineering and Resource-Aware Compilation

NSF EIA 99-75019

About the project:

Next generation parallel and distributed computing systems have the potential to provide greatly increased capabilities and performance. This potential has enabled the development of increasingly complex scientific and commercial applications, which have stringent performance requirements, must be extensible, and must scale well as problem size and/or desired degree of parallelism increases. The significant development costs, maintenance costs, and performance requirements of these applications call for a comprehensive and integrated approach to application composition and development, system and application modeling and evaluation, performance characterization, compiler optimization, and low-overhead runtime support. Achieving these capabilities will require fundamental advances in 1) methods for hierarchical, multi-language modeling, simulation, and evaluation, and 2) techniques for adaptive, resource-aware compilation and runtime support. In this project, we will take a systematic and synergetic approach to making these advances, and incorporate them in an integrated performance engineering framework and resource-aware compilation and runtime system. In addition, we will demonstrate the use of the integrated framework/system via application to several important parallel and distributed multimedia, video database, and computer vision applications.

With respect to the first project objective, we will develop a performance engineering framework for complete distributed and parallel computing systems, accounting for system components including the application software itself, the operating system, and the underlying computing and communication hardware. The framework will be usable in a stand-alone fashion, or in conjunction with the compilation and runtime support environment after an application and hardware have been developed, to guide the compiler and runtime support system in making decisions concerning specific optimizations and resource allocations. It will provide a means by which multiple, heterogeneous models can be composed together, each representing a different module (software or hardware), component, or view of the system. The composition techniques developed will permit the models to interact with one another by sharing state, events, or results, and will be scalable, in the sense that the solution of the entire model will be possible at a cost lower than for an equivalent unstructured model. Multiple modeling languages will be developed, as well as methods to combine models at different levels of resolution. Finally, we will develop model solution methods, including both simulation and analysis, that are efficient, and permit the solution of complete models of complex computing and communication systems, and the applications executing on such systems.

The second project objective is the design of resource-aware algorithms for program transformations, code optimization, and scheduling for multithreaded and ILP architectures; the development of a multilingual resource-aware optimizing compiler; and the development of compiler-generated, application-specific runtime support for high-performance parallel and distributed computer systems. Our approach enables low-overhead, adaptive runtime support to be generated by the compiler and embedded in user code. By using information about application internals (derived by the compiler through analysis and/or user assertions), and knowledge of the target architecture(s) of hierarchical distributed systems, we will produce executable code that scales dynamically and is “responsive” to reconfigurations in the type and number of system resources. Resource-awareness is introduced at each level of the compilation and runtime support phases, and is achieved via resource modeling languages that serve both the compilation and the performance engineering framework.

The modeling, compilation, and runtime techniques summarized above will be synergistically combined to obtain an integrated compiler/runtime/performance engineering framework. To demonstrate the utility of our approach, we will apply the developed performance engineering framework, resource-aware compiler, and runtime environment to multiple distributed applications, including video-filtering, video-indexing, and face-tracking applications that are of significant and increasing importance to the scientific and commercial worlds. These applications rely on computationally demanding algorithms, which are prototypical of many integer and floating-point applications, and can both validate the usefulness of the developed framework and benefit from its use.

Participants in the project:

This project was supported by the Next Generation Software (NGS) program of the National Science Foundation. The project was a combined effort of three research groups at the University of Illinois at Urbana-Champaign.

The researchers involved were:

Papers generated by the project

This material is based upon work supported by the National Science Foundation under Grant No. 9975019. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Fabian Breg and Constantine D. Polychronopoulos, “Improving Java Server Performance with Interruptlets,” Proceedings of the 2001 International Conference on Computational Science, San Francisco, CA, May 28-30, 2001.

Fabian Breg and Constantine D. Polychronopoulos, “Java Virtual Machine Support for Object Serialization,” Proceedings of the Joint ACM JavaGrande – ISCOPE 2001 Conference, Stanford, CA, June 2-4, 2001.

Bridget Carragher, Nebojsa Jojic, et al., “Automated Acquisition of Cryo Electron Micrographs Using Leginon,” Proc. Microscopy and Microanalysis ’99, Portland, Oregon, p. 376.

Steven Carroll, Walden Ko, Mark Yankelevsky, and Constantine Polychronopoulos, “Optimizing Compiler Design for Extensibility and Modularity,” Proc. of the 14th International Workshop on Languages and Compilers for Parallel Computing (LCPC 01), Cumberland Falls, Kentucky, August 2001, LNCS vol. 2624, p. 1.

Steven Carroll and Constantine D. Polychronopoulos, “Design of Resource Aware Retargetable Supercompilers,” International Journal on Parallel Processing, 2004.

Steven Carroll and Constantine Polychronopoulos, “A Framework for Incremental Extensible Compiler Construction,” Proceedings of the 17th Annual International Conference on Supercomputing, San Francisco, CA, 2003, p. 53.

L. S. Chen and T. S. Huang, “Emotional Expressions in Audiovisual Human Computer Interaction,” Proc. International Conference on Multimedia and Expo (ICME2000), New York, July 30-Aug 3, 2000, vol. 1, p. 423.Terrence Chen, Munehiro Nakazato, and Thomas S. Huang, “Speeding up the Similarity Search in Multimedia Database,” Proceedings of IEEE ICME2002, Lausanne, Switzerland, vol. 2, 2002, p. 509.

Yunqiang Chen and Thomas Huang, “Hierarchical MRF Model for Model-based Multi-object Tracking,” Proc. IEEE Int’l Conf. on Image Processing (ICIP’01), Thessaloniki, Greece, October 2001, vol. 1, p. 385.

Yunqiang Chen, Thomas Huang, and Yong Rui, “Optimal Radial Contour Tracking by Dynamic Programming,” Proc. IEEE Int’l Conf. on Image Processing, Thessaloniki, Greece, October 2001, vol. 1, p. 626.

Yunqiang Chen, Yong Rui, and Thomas S. Huang, “JPDAF Based HMM for Real-Time Contour Tracking,” Proc. IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR’01) (CVPR’01 Outstanding Student Paper), Kauai, Hawaii, 2001, vol. 1, p. I-543.

Yunqiang Chen, Yong Rui, and Thomas Huang, “Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours,” Proc. IEEE Int. Conference on Automatic Face and Gesture Recognition, Washington D.C., 2002, p. 266.

Y. Chen, Y. Rui, and T. S. Huang, “Parametric Contour Tracking Using Unscented Kalman Filter,” Proc. IEEE Int. Conf. on Image Processing, Rochester, September 2002, vol. 3, p. 613.

Yunqiang Chen, Xiang Zhou, and Thomas Huang, “One-class SVM for Learning in Image Retrieval,” Proc. IEEE Int’l Conf. on Image Processing, Thessaloniki, Greece, October 2001, vol. 1, p. 34.

Q. Cheng and T. S. Huang, “Blind Digital Watermarking for Images and Videos and Performance Analysis,” Proc. Intl. Conf. Multimedia and Expo 2000, New York, July 30- Aug. 2, 2000, vol. 1, p. 389.

Q. Cheng and T. S. Huang, “A DCT-Domain Blind Watermarking System Using Optimum Detection on Laplacian Model,” Proc. Intl. Conf. Image Proc. 2000, Vancouver, Canada, Oct. 2000, vol. 1, p. I-454.

A. L. Christensen, Result Specification and Model Connection in the Möbius Modeling Framework. Master’s Thesis, University of Illinois, 2000.

G. Clark, T. Courtney, D. Daly, D. Deavours, S. Derisavi, J. M. Doyle, W. H. Sanders, and P. Webster, “The Möbius Modeling Tool,” Proceedings of the 9th International Workshop on Petri Nets and Performance Models, RWTH Aachen, Germany, September 11-14, 2001, pp. 241-250.

G. Clark and W. H. Sanders, “Implementing a Stochastic Process Algebra within the Möbius Modeling Framework,” in Luca de Alfaro and Stephen Gilmore (Eds.), Process Algebra and Probabilistic Methods: Performance Modelling and Verification: Proceedings of the Joint International Workshop, PAPM-PROBMIV 2001, RWTH Aachen, Germany, September 12-14, 2001, Lecture Notes in Computer Science no. 2165, Berlin: Springer, 2001, pp. 200-215.

I. Cohen, A. Garg, and T. S. Huang, “Vision-Based Overhead View Person Recognition,” Proc. 15th Intl. Conf. on Pattern Recognition, Barcelona, Spain, 2000, vol. 1, p. 1119.

T. Courtney, D. Daly, S. Derisavi, S. Gaonkar, M. Griffith, V. Lam, and W. H. Sanders, “The Möbius Modeling Environment: Recent Developments,” Proceedings of the 1st International Conference on Quantitative Evaluation of Systems (QEST 2004), Enschede, The Netherlands, September 27-30, 2004, pp. 328-329.

T. Courtney, D. Daly, S. Derisavi, V. Lam, and W. H. Sanders, “The Möbius Modeling Environment,” in Tools of the 2003 Illinois International Multiconference on Measurement, Modelling, and Evaluation of Computer-Communication Systems, Universität Dortmund Fachbereich Informatik research report no. 781/2003, 2003, pp. 34-37.

David Craig and Constantine Polychronopoulos, “A Flexible User-level Scheduler,” in the Proceedings of the 13th International Conference on Parallel and Distributed Systems, July 2000, Las Vegas, NV.

D. Daly, Analysis of Connection as a Decomposition Technique. Master’s Thesis, University of Illinois, 2001.

D. M. Daly, Bounded Aggregation Techniques to Solve Large Markov Models. Doctoral Dissertation, University of Illinois, 2005.

D. Daly, D. D. Deavours, J. M. Doyle, P. G. Webster, and W. H. Sanders, “Möbius: An Extensible Tool for Performance and Dependability Modeling,” Computer Performance Evaluation: Modelling Techniques and Tools: Proceedings of the 11th International Conference, TOOLS 2000, Schaumburg, IL, March 27-31, 2000. In B. R. Haverkort, H. C. Bohnenkamp, and C. U. Smith (Eds.), Lecture Notes in Computer Science No. 1786, pp. 332-336. Berlin: Springer, 2000.

D. Daly, G. Kar, and W. H. Sanders, “Modeling of Service-level Agreements for Composed Services,” Management Technologies for E-Commerce and E-Business Applications: Proceedings of the 13th IFIP/IEEE International Workshop on Distributed Systems: Operations & Management, DSOM 2002, Montreal, Canada, October 21-23, 2002, pp. 4-15.

D. Daly and W. H. Sanders, “Bounded Decomposition of Stochastic Models,” in Extended Abstracts from PMCCS-6: the Sixth International Workshop on Performability Modeling of Computer and Communication Systems, Monticello, IL, September 5-7, 2003, pp. 1-4.

D. Daly and W. H. Sanders, “A Connection Formalism for the Solution of Large and Stiff Models,” Proceedings of the 34th Annual Simulation Symposium, Seattle, WA, April 22-26, 2001, pp. 258-265.

D. D. Deavours, Formal Specification of the Möbius Modeling Framework. Doctoral Dissertation, University of Illinois, 2001.

D. D. Deavours, G. Clark, T. Courtney, D. Daly, S. Derisavi, J. M. Doyle, W. H. Sanders, and P. G. Webster, “The Möbius Framework and Its Implementation,” IEEE Transactions on Software Engineering, vol. 28, no. 10, October 2002, pp. 956-969.

D. D. Deavours and W. H. Sanders, “An Efficient Well-Specified Check,” Proc. PNPM’99: 8th International Workshop on Petri Nets and Performance Models, Zaragoza, Spain, September 8-10, 1999, p. 124.

D. D. Deavours and W. H. Sanders, “Möbius: Framework and Atomic Models,” Proceedings of the 9th International Workshop on Petri Nets and Performance Models, RWTH Aachen, Germany, September 11-14, 2001, pp. 251-260.

D. D. Deavours and W. H. Sanders, “The Möbius Execution Policy,” Proceedings of the 9th International Workshop on Petri Nets and Performance Models, RWTH Aachen, Germany, September 11-14, 2001, pp. 135-144.

S. Derisavi, The Möbius State-Level Abstract Functional Interface. M.S. Thesis, University of Illinois, May 2003.

S. Derisavi, Solution of Large Markov Models Using Lumping Techniques and Symbolic Data Structures. Doctoral Dissertation, University of Illinois, 2005.

S. Derisavi, H. Hermanns, and W. H. Sanders, “Optimal State-Space Lumping in Markov Chains,” Information Processing Letters, vol. 87, no. 6, September 30, 2003, pp. 309-315.

S. Derisavi, P. Kemper, W. H. Sanders, and T. Courtney, “The Möbius State-level Abstract Functional Interface,” Computer Performance Evaluation: Modelling Techniques and Tools: Proceedings of the 12th International Conference, TOOLS 2002, London, UK, April 14-17, 2002, Lecture Notes in Computer Science vol. 2324 (T. Field, P. G. Harrison, J. Bradley, and U. Harder, eds.), Berlin: Springer, pp. 31-50.

S. Derisavi, P. Kemper, W. H. Sanders, and T. Courtney, “The Möbius State-level Abstract Functional Interface,” Performance Evaluation, vol. 54, no. 2, October 2003, pp. 105-128.

Jay M. Doyle, Abstract Model Specification Using the Möbius Modeling Tool. M.S. thesis, University of Illinois, May 2000.

Brendan Frey and Nebojsa Jojic, “Learning Mixture Models of Images and Inferring Spatial Transformations Using the EM Algorithm,” Proc. Computer Vision and Pattern Recognition (CVPR), Fort Collins, June 23-25, 1999, p. 416.

Brendan Frey and Nebojsa Jojic, “Transformation Invariant Mixture Models,” Proc. Machines that Learn Workshop (Snowbird), March 1999.

Brendan Frey and Nebojsa Jojic, “Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image,” Proc. International Conference on Computer Vision (ICCV) ’99, Korfu, Greece, September 1999, p. 1190.

A. Garg, I. Cohen, and T. S. Huang, “Adaptive Learning Algorithm for SVM Applied to Feature Tracking,” Proceedings of ICIIS’99, Washington DC, November 1-3, 1999, pp. 388-395.

P. Hong and T. S. Huang, “Extracting the Recurring Patterns from Image,” Proc. 4th Asian Conference on Computer Vision, Taipei, Taiwan, Jan. 5-8, 2000.

P. Hong and T. S. Huang, “Learning to Extract Temporal Signal Patterns from Temporal Signal Sequence,” Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, Sept. 3-8, 2000, vol. 2, p. 648.

Pengyu Hong, Qi Tian, and Thomas S. Huang, “Incorporate Support Vector Machines to Content-Based Image Retrieval with Relevance Feedback,” Proc. of the 7th IEEE International Conference on Image Processing (ICIP’2000), Vancouver, Canada, Sept. 10-13, 2000, vol. III, p. 750.

P. Hong, M. Turk, and T. S. Huang, “Constructing Finite State Machines for Fast Gesture Recognition,” Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, Sept. 3-8, 2000, vol. 3, p. 691.

P. Hong, M. Turk, and T. S. Huang, “Gesture Modeling and Recognition Using Finite State Machines,” Proc. 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 28-30, 2000, p. 410.

P. Hong, R. Wang, and T. S. Huang, “Learning Patterns from Images by Combining Soft Decisions and Hard Decisions,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, June 13-15, 2000, pp. 78-83.

T. S. Huang and Milind Naphade, “MARS (Multimedia Analysis and Retrieval System): A Test-bed for Video Indexing, Browsing, Searching, Filtering and Summarization,” Proc. Intl. Workshop on Multimedia Data Storage, Retrieval, Integration and Applications, Hong Kong, January 13-15, 2000.

Thomas S. Huang, Ying Wu, and John Lin, “3D Model-based Visual Hand Tracking,” Proc. IEEE Intl. Conf. on Multimedia and Expo, Lausanne, Switzerland, August 26-29, 2002, vol. I, p. 905.

Thomas S. Huang and Xiang Sean Zhou, “Image Retrieval by Relevance Feedback: From Heuristic Weight Adjustment to Optimal Learning Methods,” Proceedings of the 2001 Intl. Conf. on Image Processing, 2001, vol. 3, p. 2.

Thomas S. Huang, Xiang Sean Zhou, Munehiro Nakazato, Ira Cohen, and Ying Wu, “Learning in Content-based Image Retrieval” (invited talk), Proc. 2nd International Conference on Development and Learning (ICDL’02), MIT, June 12-15, 2002.

Y. Huang and T. S. Huang, “Facial Tracking with Head Pose Estimation in Stereo Vision,” Proc. IEEE Intl. Conference on Image Processing, Rochester, New York, September 22-25, 2002, vol. 3, p. 833.

Y. Huang and T. S. Huang, “Model-based Human Body Tracking,” Proc. IAPR Int. Conference on Pattern Recognition, Quebec City, Canada, August 11-15, 2002, vol. 1, p. 552.

Y. Huang, T. S. Huang, and Heinrich Niemann, “Region-based Method for Model-free Object Tracking,” Proc. IAPR Int. Conf. on Pattern Recognition, Quebec City, Canada, August 11-15, 2002, vol. 1, p. 592.

Y. Huang, T. S. Huang, and Heinrich Niemann, “Segmentation-based Object Tracking Using Image Warping and Kalman Filtering,” Proc. IEEE Int. Conf. on Image Processing, Rochester, September 22-25, 2002, vol. 3, p. 601.

Yu Huang, Thomas S. Huang, and Heinrich Niemann, “Two-Handed Gesture Tracking Incorporating Template Warping With Static Segmentation,” Proc. IEEE Int. Conference of Automatic Face and Gesture Recognition, Washington D.C., 2002, p. 260.

R. K. Iyer, W. H. Sanders, J. H. Patel, and Z. Kalbarczyk, “The Evolution of Dependable Computing at the University of Illinois,” in R. Jacquart, Ed., Building the Information Society: IFIP 18th World Computer Congress Topical Sessions, Toulouse, France, August 22-27, 2004, pp. 135-164. Boston: Kluwer Academic Publishers.

Nebojsa Jojic, Barry Brumitt, Brian Meyers, Steve Harris, and Thomas Huang, “Detection and Estimation of Pointing Gestures in Dense Disparity Maps,” Proc. IEEE International Conference on Automatic Face and Gesture Recognition 2000, Grenoble, France, March 2000, p. 468.

N. Jojic and B. Frey, “Topograhic Transformation as a Latent Variable,” Proc. Neural Information Processing Systems, Denver, CO, Nov. 1999, p. 477.

Nebojsa Jojic, Yong Rui, Yueting Zhuang, and Thomas S. Huang, “A Framework for Garment Shopping over the Internet,” in Handbook of Electronic Commerce (Ed. by Mike Shaw), pp. 249-270, Springer-Verlag, 2000.

N. Jojic, N. Petrovic, B. Frey, and T. S. Huang, “Transformed Hidden Markov Models: Estimating Mixture Models and Inferring Spatial Transformations in Video Sequences,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Hilton Head Island, SC, 2000, pp. II26-33.

Nebojsa Jojic, Matthew Turk, and Thomas S. Huang, “Tracking Articulated Objects in Dense Disparity Maps,” Proc. Intl. Conference on Computer Vision (ICCV), Korfu, Greece, September 1999, p. 123.

Nebojsa Jojic, Matthew Turk, and Thomas S. Huang, “Tracking Articulated Structures in Stereo Sequences,” Proc. IEEE Information Theory Workshop on Detection, Estimation, Classification and Imaging, Santa Fe, NM, February 1999, p. 77.

Walden Ko, Steven Carroll, Fred Koopmans, Mark Yankelevsky, Kazushi Marukawa, Peter Kalogiannis, Dimitrios S. Nikolopoulos, and Constantine D. Polychronopoulos, “Resource-Aware Compilation in PROMIS,” submitted for publication.

Walden Ko, Mark Yankelevsky, Dimitrios S. Nikolopoulos, and Constantine D. Polychronopoulos, “Effective Cross-Platform Multilevel Parallelization via Dynamic Adaptive Execution,” Proc. of the International Parallel and Distributed Processing Symposium, Fort Lauderdale, Florida, April 2002, p. 108.

V. V. Lam, A Path-Based Framework for Analyzing Large Markov Models. Ph.D. Dissertation, University of Illinois at Urbana-Champaign, 2011.

S. E. Levinson, Q. Liu, C. Dodsworth, R. Lin, W. Zhu, and M. Kleffner, “The Role of Sensorimotor Function, Associative Memory and Reinforcement Learning in Automatic Acquisition of Spoken Language by an Autonomous Robot,” Proc. NSF/DARPA Workshop on Development and Learning, East Lansing, MI, April 5-7, 2000, p. 95.

Yang Li, T. Zhang, and S. E. Levinson, “Word Concept Model for Intelligent Dialogue Agents,” Proc. of Third Intl. Workshop on Text, Speech and Dialogue, Brno, Czech Republic, September 13-16, 2000. Lecture Notes in Computer Science, vol. 1902, p. 445.

Qiong Liu, S. E. Levinson, Ying Wu, and Thomas S. Huang, “Interactive and Incremental Learning via a Mixture of Supervised and Unsupervised Learning Strategies,” Proc. Fifth Joint Conference on Information Systems (JCIS 2000), Atlantic City, NJ, February 27-March 3, 2000, vol. I, p. 555.

Qiong Liu, S. Levinson, Ying Wu, and Thomas S. Huang, “SVM Guided Nearest Neighbor Classification,” Proc. Neural Information Processing Systems (NIPS’99) Workshop on Support Vector Machine, Colorado, Dec. 1999.

Q. Liu, Y. Rui, T. S. Huang, and S. E. Levinson, “Video Sequence Learning and Recognition via Dynamic SOM,” Proc. Sixth International Conference on Image Processing, Kobe, Japan, October 24-28, 1999, vol. 4, p. 93.

B. Moghaddam, Q. Tian, Thomas S. Huang, “Spatial Visualization for Content-Based Image Retrieval,” Proceedings of the 2001 IEEE International Conference on Multimedia and Expo (ICME2001), Tokyo, Japan, August 22-25, 2001, p. 162.

B. Moghaddam, Q. Tian, N. Lesh, C. Shen, and T. S. Huang, “PDH: A Human-Centric Interface for Image Libraries,” Proc. IEEE International Conference on Multimedia and Expo (ICME’02), August 26-29, 2002, Lausanne, Switzerland, vol. I, p. 901.

B. Moghaddam, Q. Tian, N. Lesh, C. Shen, and T. S. Huang, “Visualization & Layout for Personal Photo Libraries,” Proc. International Workshop on Content-Based Multimedia Indexing (CBMI’01), Brescia, Italy, Sept. 19-21, 2001.

B. Moghaddam, Q. Tian, N. Lesh, C. Shen, and T. S. Huang, “Visualization and User-Modeling for Browsing Personal Photo Libraries,” International Journal of Computer Vision, Special Issue on Content-Based Image Retrieval, vol. 56, 2004, p. 109.

Munehiro Nakazato and Thomas S. Huang, “3D MARS: Immersive Virtual Reality for Content-Based Image Retrieval,” Proceedings of 2001 IEEE International Conference on Multimedia and Expo (ICME2001), Tokyo, August 22-25, 2001, p. 44.

Munehiro Nakazato and Thomas S. Huang, “Extending Image Retrieval with Group-Oriented Interface,” Proc. IEEE ICME’02, Lausanne, Switzerland, August 2002, p. 201.

Munehiro Nakazato, Ljubomir Manola and Thomas S. Huang, “Group-based Interface for Content-based Image Retrieval,” Proceedings of Advanced Visual Interfaces 2002 (AVI 2002), Trento, Italy, May 22-24, 2002.

Munehiro Nakazato, Ljubomir Manola and Thomas S. Huang, “ImageGrouper: Search, Annotate and Organize Image by Groups,” Proc. Fifth International Conference on Visual Information Systems (VISual 2002), HsinChu, Taiwan, March 2002.

Milind Naphade, Brendan Frey, Lawrence Chen, and Thomas Huang, “Learning Sparse Multiple Cause Models,” Proc. IAPR International Conference on Pattern Recognition, Barcelona, Spain, September 3-8, 2000, p. 642.

Milind Naphade and Thomas Huang, “Inferring Semantic Concepts for Video Indexing and Retrieval,” Proc. IEEE Intl. Conference on Image Processing, Vancouver, Canada, September 2000, vol. 3, p. III-766.

Milind Naphade and Thomas Huang, “Multimedia Understanding: Challenges in the New Millennium,” Proc. IEEE Intl. Conference on Image Processing, Vancouver, Canada, September 2000, vol. 3, p. 33.

Milind Naphade and Thomas Huang, “A Probabilistic Framework for Semantic Indexing and Retrieval in Video,” Proc. 1st IEEE International Conference on Multimedia and Expo, New York, July 31-August 2, 2000, vol. 1, p. 475.

Milind Naphade and Thomas Huang, “Semantic Video Indexing Using a Probabilistic Framework,” Proc. IAPR International Conference on Pattern Recognition, Barcelona, Spain, September 3-8, 2000, vol. 3, p. 79.

M. Naphade and Thomas Huang, “Stochastic Modeling of Soundtrack for Efficient Segmentation and Indexing of Video,” Proc. SPIE, Storage and Retrieval for Media Databases 2000, San Jose, CA, Jan. 2000, vol. 3972, p. 168.

Milind Naphade, I. Kozintsev, Thomas Huang, and K. Ramchandran, “A Factor Graph Framework for Semantic Indexing and Retrieval in Video,” Proc. Content-Based Access of Image and Video Library 2000, June 12, 2000, p. 35.

M. Naphade, X. Zhou, and T.S. Huang, “Image Classification Using Labeled and Unlabeled Images,” Proceedings of SPIE International Symposium on Voice, Video and Data Communications, Boston, Nov. 6, 2000, vol. 4210, p. 13.

David M. Nicol, William H. Sanders, and Kishor S. Trivedi, “Model-based Evaluation: From Dependability to Security,” IEEE Transactions on Dependability and Security, vol. 1, no. 1, January-March 2004, pp. 48-65.

Dimitri Nicolopoulos, T. S. Papatheodorou, and Constantine D. Polychronopoulos, “Is Data Distribution Necessary in OpenMP?,” Proceedings of the IEEE/ACM Supercomputing’2000, Dallas, TX, Nov. 4-12, 2000.

Dimitrios S. Nikolopoulos, Eduard Ayguade, Jesus Labarta, Theodore S. Paptheodorou, and Constantine D. Polychronopoulos, “The Trade-Off Between Implicit and Explicit Data Distribution in Shared-Memory Programming Paradigms,” Proceedings of the 15th ACM International Conference on Supercomputing (ICS’2001), Sorento, Italy, June 19-23, 2001.

Dimitrios S. Nikolopoulos, Eduard Ayguade, Theodore S. Papatheodorou, Constantine D. Polychronopoulos, and Jesus Labarta, “The Trade-Off between Implicit and Explicit Data Distribution in Shared-Memory Programming Paradigms,” Proc. of the 15th International Conference on Supercomputing (ICS’2001), Sorrento, Italy, 2001, p. 23.

Dimitrios S. Nikolopoulos, Constantine D. Polychronopoulos, Theodore S. Papatheodorou, Jesus Labarta, and Eduard Ayguade, “Scheduler-activated Dynamic Page Migration for Multiprogrammed DSM Multiprocessors,” Journal of Parallel and Distributed Computing, vol. 62, 2002, p. 1069.

Dimitrios S. Nikolopoulos and Constantine D. Polychronopoulos, “Adaptive Scheduling under Memory Pressure on Multiprogrammed Clusters,” Proc. of the Second IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2002), Berlin, Germany, May 2002, p. 14.

Dimitrios S. Nikolopoulos and Constantine D. Polychronopoulos, “Adaptive Scheduling under Memory Pressure on Multiprogrammed SMPs,” Proc. of the 16th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2002), Fort Lauderdale, Florida, April 2002, p. 11.

Dimitrios S. Nikolopoulos and Constantine D. Polychronopoulos, “Scaling Irregular Parallel Codes with Minimal Programming Effort,” Proc. of the 2001 ACM/IEEE Conference on Supercomputing, Denver, Colorado, November 2001, p. 16.

W. D. Obal II and W. H. Sanders, “Measure-Adaptive State-Space Construction,” Proc. 4th IEEE International Computer Performance and Dependability Symposium (IPDS 2K), March 27-29, 2000, Chicago, Illinois, p. 25.

W. D. Obal II and W. H. Sanders, “Measure-Adaptive State-Space Construction,” Performance Evaluation journal, vol. 44, p. 237, 2001.

Hideki Saito, Nick Stavrakos, Steve Carroll, and Constantine Polychronopoulos, “Symbolic Analysis: An Effective Solution to the Phase Ordering Problem,” submitted for publication.

Hideki Saito, Nicholas Stavrakos, and Constantine Polychronopoulos, “The Design of the PROMIS Compiler: Towards Multilevel Parallelization,” International Journal of Parallel Programming, vol. 28, no. 2, April 2000, pp. 195-212.

Hideki Saito, Nicholas Stavrakos, and Constantine Polychronopoulos, “PROMIS as an Application Development Environment,” Proceedings of the 4th International Symposium on High-Performance Computing, October 14-17, 2000, Tokyo, Japan.

W. H. Sanders, “Integrated Frameworks for Multi-Level and Multi-Formalism Modeling,” Proc. PNPM’99: 8th International Workshop on Petri Nets and Performance Models, Zaragoza, Spain, September 8-10, 1999, p. 2.

W. H. Sanders and J. F. Meyer, “Stochastic Activity Networks: Formal Definitions and Concepts,” in E. Brinksma, H. Hermanns, and J. P. Katoen (Eds.), Lectures on Formal Methods and Performance Analysis, First EEEF/Euro Summer School on Trends in Computer Science, Berg en Dal, The Netherlands, July 3-7, 2000, Revised Lectures, Lecture Notes in Computer Science no. 2090, pp. 315-343. Berlin: Springer, 2001.

W. H. Sanders, C. Polychronopoulos, T. Huang, T. Courtney, D. Daly, D. Deavours, and S. Derisavi, “Overview: An Integrated Framework for Performance Engineering and Resource-Aware Compilation,” NSF Next Generation Systems Program Workshop, Fort Lauderdale, FL, April 15, 2002. (Presented but not published in a proceedings.)

W. H. Sanders, C. Polychronopoulos, T. Huang, T. Courtney, D. Daly, D. Deavours, and S. Derisavi, “Overview: An Integrated Framework for Performance Engineering and Resource-Aware Compilation,” Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS ’02), Fort Lauderdale, Florida, April 15-19, 2002, pp. 173-180.

N. Sebe, Q. Tian, Michael Lew, E. Loupias, and Thomas S. Huang, “Color Indexing Using Wavelet-based Salient Points,” Proc. IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL’2000), Hilton Head Island, South Carolina, June 13-16, 2000, p. 15.

N. Sebe, Q. Tian, E. Loupias, M. Lew, and T. S. Huang, “Evaluation of Salient Points Techniques,” Image and Vision Computing, Special Issue on Machine Vision, vol. 21, 2003, p. 1087.

Qi Tian, Pengyu Hong, and Thomas S. Huang, “Update Relevant Image Weights for Content-Based Image Retrieval Using Support Vector Machines,” Proc. IEEE Intl. Conference on Multimedia and Expo (ICME’2000), New York, NY, July 30-Aug. 2, 2000, vol. 2, p. 1199.

Q. Tian, B. Moghaddam, and Thomas S. Huang, “Display Optimization for Image Browsing,” Proc. 2nd International Workshop on Multimedia Databases and Image Communications (MDIC’01), Sept. 17-18, 2001, Amalfi, Italy.

Q. Tian, B. Moghaddam, and T. S. Huang, “Visualization, Estimation and User Modeling for Interactive Browsing of Image Libraries,” Proc. International Conference on Image and Video Retrieval (CIVR’02), London, UK, July 18-19, 2002.

Q. Tian, N. Sebe, M. Lew, E. Loupias, and Thomas S. Huang, “Content-Based Image Retrieval Using Wavelet-Based Salient Points,” Proc. Storage and Retrieval for Media Databases, SPIE Photonics West, Electronic Imaging, San Jose, CA, Jan. 21-26, 2001, p. 425.

Q. Tian, N. Sebe, M. S. Lew, E. Loupias, and T. S. Huang, “Image Retrieval Using Wavelet-Based Salient Points,” Journal of Electronic Imaging, Special Issue on Storage and Retrieval of Digital Media, vol. 10, p. 835, 2001.

Q. Tian, Y. Wu, and T. S. Huang, “Combine User Defined Region-of-Interest and Spatial Layout for Image Retrieval,” 7th IEEE International Conference on Image Processing (ICIP’2000), Vancouver, BC, Canada, Sep. 10-13, 2000, vol. III, p. 746.

Qi Tian, Ying Wu, and Thomas S. Huang, “Incorporate Discriminant Analysis with EM Algorithm in Image Retrieval,” Proc. IEEE 2000 International Conference on Multimedia and Expo (ICME’2000), New York, NY, July 30-Aug. 2, 2000, vol. 1, p. 299.

Q. Tian, Y. Wu, J. Yu, and T.S. Huang, “Self-Supervised Learning Based on Descriminative Nonlinear Features and Its Applications for Pattern Classification,” Pattern Recognition, Special Issue on Image Understanding for Digital Photographs, 2003.

Q. Tian, J. Yu, Y. Wu, and T.S. Huang, “Learning Based on Kernel Discriminant-EM Algorithm for Image Classification,” Proc. IEEE International Conference On Acoustics, Speech, and Signal Processing (ICASSP04), May 17-24, 2004, Montreal, Quebec, Canada, 2004.

Q. Tian, J. Yu, Y. Wu, and T.S. Huang, “Toward An Improved Parameterized Discriminant Analysis for Image Classification,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington, DC, June 27-July 2, 2004.

Ruoyu Roy Wang, Thomas Huang, and Jialin Zhong, “Generative and Discriminative Face Modelling for Detection,” Proc. IEEE Int. Conference of Automatic Face and Gesture Recognition, Washington D.C., 2002, p. 266.

P. G. Webster, Design of Experiments in the Möbius Modeling Framework, Master’s Thesis, University of Illinois, 2002.

Ying Wu and Thomas S. Huang, “Capturing Articulated Human Hand Motion: A Divide-and-Conquer Approach,” Proc. IEEE Intl. Conf. on Computer Vision (ICCV’99), Greece, Sept. 1999, vol. 1, p. 606.

Ying Wu and Thomas S. Huang, “Color Tracking by Transductive Learning,” Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, SC, 2000, vol. 1, pp. 133-138.

Ying Wu and Thomas S. Huang, “Human Hand Modeling, Analysis and Animation in the Context of HCI,” Proc. IEEE Intl. Conf. on Image Processing (ICIP’99), Japan, Oct. 1999, p. 6.

Ying Wu and Thomas S. Huang, “Non-stationary Color Tracking for Vision-based Human-Computer Interaction,” IEEE Trans. on Neural Networks, vol. 13(4), 2002, p. 948.

Ying Wu and Thomas S. Huang, “Self-Supervised Learning for Visual Tracking and Recognition of Human Hand,” Proc. of AAAI 17th National Conf. on Artificial Intelligence (AAAI’2000), Austin, TX, Aug. 2000, p. 243.

Ying Wu and Thomas S. Huang, “Using Unlabeled Data in Supervised Learning by Discriminant-EM Algorithm,” Proc. Neural Information Processing Systems (NIPS’99) Workshop on Using Unlabeled Data for Supervised Learning, Colorado, Dec. 1999.

Ying Wu and Thomas S. Huang, “View-independent Recognition of Hand Postures,” Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition (CVPR’2000), Hilton Head Island, SC, June 2000, vol. II, p. 88.

Ying Wu and Thomas S. Huang, “Vision-Based Gesture Recognition: A Review,” in Gesture-Based Communication in Human-Computer Interaction: Proc. International Gesture Workshop, GW’99, Gif-sur-Yvette, France, March 1999, Lecture Notes in Artificial Intelligence vol. 1739, 1999, p. 103.

Ying Wu, Qiong Liu, and Thomas S. Huang, “An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-time Human Hand Localization,” Proc. 4th IEEE Asian Conf. on Computer Vision (ACCV’2000), Taipei, Taiwan, Jan. 2000, p. 1106.

Ying Wu, Qiong Liu, and Thomas S. Huang, “Robust Real-Time Human Hand Localization by Self-Organizing Color Segmentation,” Proc. IEEE ICCV’99 Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS’99), Corfu, Greece, Sept. 1999, p. 161.

Ying Wu, Qiong Liu, and Thomas S. Huang, “Tracking, Analyzing and Recognizing Gesture Commands,” Proc. of ARL Symposium (ARL 2000), Maryland, March 2000.

Y. Wu, Q. Tian, and T. S. Huang, “Discriminant-EM Algorithm with Application to Image Retrieval,” in Proc. IEEE Intl. Conf. Computer Vision and Pattern Recognition (CVPR’2000), vol. I, Hilton Head Island, South Carolina, June 13-15, 2000, pp. 222-227.

Ying Wu, Qi Tian, and Thomas S. Huang, “Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback,” Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, Sept. 3-8, 2000, vol. I, p. 21.

Ying Wu, Kentaro Toyama, and Thomas S. Huang, “Wide-Range, Person- and Illumination-Insensitive Head Orientation Estimation,” Proc. of Intl. Conf. on Automatic Face and Gesture Recognition (FG’2000), France, March 2000, p. 183.

Ying Wu, Zhengyou Zhang, Thomas S. Huang, and John Y. Lin, “Multibody Grouping via Orthogonal Subspace Decomposition,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’01), Hawaii, Dec. 2001, vol. 2, p. II-252.

Ziyou Xiong and Thomas S. Huang, “Block-based, Memory-efficient JPEG2000 Images Indexing in Compressed-domain,” Proc. Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, New Mexico, April 7-9, 2002, p. 92.

Ziyou Xiong and Thomas S. Huang, “Nonlinear Independent Component Analysis (ICA) Using Power Series and Application to Blind Source Separation,” Proc. 3rd International Conference on Independent Component Analysis and Blind Signal Separation, San Diego, California, December 9-12, 2001, p. 680.

Ziyou Xiong and Thomas S. Huang, “Subband-based Memory-efficient Compressed-domain JPEG2000 Image Indexing,” Proc. Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, New Mexico, April 7-9, 2002, p. 290.

Ziyou Xiong and Thomas S. Huang, “Wavelet-based Texture Features Can Be Extracted Efficiently from Compressed-domain for JPEG2000 Coded Images,” Proc. Intl. Conf. on Image Processing (ICIP) 2002, Rochester, New York, Sept. 22-25, 2002, vol. I, p. I-481.

Ziyou Xiong, Xiang Zhou, William Pottenger, and Thomas S. Huang, “Speeding up Relevance Feedback in Image Retrieval with Triangle-Inequality Based Algorithms,” Proc. Intl Conf. on Acoustic Speech and Signal Processing (ICASSP), Orlando, Florida, May 13-17, 2002, vol. 4, p. 3383.

Mark N. Yankelevsky and Constantine D. Polychronopoulos, “a-coral: A Multigrain, Multithreaded Processor Architecture,” Proc. of the ACM 15th International Conference on Supercomputing, Sorrento, Italy, 2001, pp. 358-367.

X. Zhou, Y. Rui, and T. S. Huang, Exploration of Visual Data. Kluwer Academic Publishers, 2003.

X. S. Zhou, A. Garg, and T. S. Huang, “Boosting Ranking Functions for Image Retrieval,” IEEE Conference on Computer Vision and Pattern Recognition, 2004.

X. S. Zhou and T. S. Huang, “CBIR: From Low-level Features to High-level Semantics,” SPIE: Proc. Image and Video Comm. and Proc. 2000, San Jose, CA, Jan. 24-28, 2000.

Xiang Sean Zhou and Thomas S. Huang, “Comparing Discriminate Transformations and SVM for Learning During Multimedia Retrieval,” Proc. ACM Multimedia 2001, Sept. 30-Oct. 5, 2001, Ottawa, Ontario, Canada.

Xiang Sean Zhou and Thomas S. Huang, “A Generalized Relevance Feedback Scheme for Image Retrieval,” Proceedings of SPIE: Internet Multimedia Management Systems, Boston, MA, Nov. 6-7, 2000, vol. 4210, p. 348.

X. S. Zhou and T. S. Huang, “Image Representation and Retrieval Using Structural Features,” Proc. Intl. Conf. on Pattern Recognition, Barcelona, Spain, Sept. 3-8, 2000, vol. 1, p. 1039.

X. S. Zhou and T. S. Huang, “Image Retrieval: Feature Primitives, Feature Representation, and Relevance Feedback,” Proc. IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL-2000), in conjunction with IEEE CVPR-2000, Hilton Head Island, NC, June 13-15, 2000, p. 10.

Xiang Sean Zhou and Thomas S. Huang, “Relevance Feedback in Content-based Image Retrieval: Some Recent Advances,” Information Sciences, 2002, vol. 148, p. 129.

Xiang Sean Zhou and Thomas S. Huang, “Relevance Feedback in Content-based Image Retrieval: Some Recent Advances” (invited paper), Proc. 6th Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March 8-14, 2002, p. 15.

Xiang Sean Zhou and Thomas S. Huang, “Unifying Keywords and Contents for Image Retrieval,” Proc. International Workshop on Content-Based Multimedia Indexing, Italy, September 19-21, 2001.

X. S. Zhou, Y. Rui, and T. S. Huang, “Water-filling: A Novel Way for Image Structural Feature Extraction,” Proc. IEEE Intl. Conf. on Image Processing, Kobe, Japan, Oct. 25-29, 1999, vol. 2, p. 570.