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Transcript of Pradeep K. Dubey, [email protected] Applications Pradeep K Dubey for Bob Liang Applications...
Pradeep K. Dubey, [email protected]
ApplicationsApplications
Pradeep K Dubey for Bob LiangPradeep K Dubey for Bob Liang
Applications Research LabApplications Research Lab
Microprocessor Technology LabsMicroprocessor Technology Labs
Corporate Technology GroupCorporate Technology Group
December 8, 2005December 8, 2005
2Pradeep K. Dubey, [email protected]
Application Research LabApplication Research Lab
Carole Dulong Pradeep Dubey
Tim Mattson Gary Bradski Horst Haussecker
Jim Hurley
Bob Liang
3Pradeep K. Dubey, [email protected]
What is a killer app?What is a killer app?
““A reasonable man adapts himself to his environment. An A reasonable man adapts himself to his environment. An unreasonable man persists in attempting to adapt his unreasonable man persists in attempting to adapt his environment to suit himself …environment to suit himself …
… … Therefore, all progress depends on the unreasonable Therefore, all progress depends on the unreasonable man.”man.” -- -- George Bernard ShawGeorge Bernard Shaw
Replace “man” with “application”, and you get one definition of a Replace “man” with “application”, and you get one definition of a killer killer appapp, namely , namely that unreasonable application which succeeds in leaving that unreasonable application which succeeds in leaving its mark on the surrounding architectureits mark on the surrounding architecture. .
All architectural progress depends on such unreasonable apps!All architectural progress depends on such unreasonable apps!All architectural progress depends on such unreasonable apps!All architectural progress depends on such unreasonable apps!
4Pradeep K. Dubey, [email protected]
Mining
Indexing
Recognition
Synthesis
Streaming
Graphics
Large dataset miningSemantic Web/Grid MiningStreaming Data Mining
Distributed Data MiningContent-based Retrieval
Collaborative FiltersMultidimensional IndexingDimensionality Reduction
Dynamic OntologiesEfficient access to large, unstructured, sparse datasets
Stream Processing
Multimodal event/object Recognition
Statistical ComputingMachine Learning
Clustering / ClassificationModel-based:
Bayesian network/Markov ModelNeural network / Probability networks
LP/IP/QP/Stochastic Optimization
Photo-real SynthesisReal-world animation
Ray tracingGlobal Illumination
Behavioral SynthesisPhysical simulation
KinematicsEmotion synthesis
Audio synthesisVideo/Image synthesisDocument synthesis
Data-data everywhere, not a bit of sense!Data-data everywhere, not a bit of sense!
5Pradeep K. Dubey, [email protected]
Scene complexity: moderateLocal processing dominated
Media Evolution
Graphics Evolution
Mining Evolution
Scene complexity: largeGlobal processing dominated
Scene complexity: real-worldPhysical simulation dominated
Modality-specific streaming
Modality-aware transformation
Multimodal recognition
Dataset: static/structuredResponse: offline
Dataset: dynamic, multimodalResponse: real-time
Dataset: massive+streamingResponse: interactive
Upcoming Transition
Next Transition
Evolving towards model-based computingEvolving towards model-based computing
Workload convergence: multimodal recognition and synthesis over complex datasetsWorkload convergence: multimodal recognition and synthesis over complex datasets
6Pradeep K. Dubey, [email protected]
What is a tumor? Is there a tumor here? What if the tumor progresses?
It is all about dealing efficiently with complex multimodal datasetsIt is all about dealing efficiently with complex multimodal datasets
Recognition Mining Synthesis
Images courtesy: http://splweb.bwh.harvard.edu:8000/pages/images_movies.html
7Pradeep K. Dubey, [email protected]
Visual InputStreams
Find an existing model instance
What is …? What if …?Is it …?
Recognition Mining Synthesis
Create a newmodel instance
What Killer app – grep, ctrl-c, ctrl-v?What Killer app – grep, ctrl-c, ctrl-v?
Model
Most RMS apps are about enabling interactive (real-time) RMS Loop or iRMSMost RMS apps are about enabling interactive (real-time) RMS Loop or iRMS
SynthesizedVisuals
Learning &Modeling
Graphics Rendering + Physical Simulation
ComputerVision
RealityAugmentation
8Pradeep K. Dubey, [email protected]
iRMS Loop IllustrationiRMS Loop Illustration
Facial Muscle Activations:Compact motion representation,well suited for modeling and synthesis
Video Input Feature TrackingAnalytically Correct, Muscle-
Activated Human Head ModelPhysics-Based Deformable Tissue
(Finite Element Method)
User Interaction:Modified Muscle Activations
Video Output
User Interaction:Modified Physical Model
Source: E. Sifakis, I. Neverov and R. Fedkiw, “Automatic Determination of Facial Muscle Activations from Sparse Motion Capture Marker Data”, ACM SIGGRAPH, 2005 (to appear)
9Pradeep K. Dubey, [email protected]
Virtual Reality, Games, Simulations …Virtual Reality, Games, Simulations …
10Pradeep K. Dubey, [email protected]
Computer Vision – OpenCV 1M downloads!
Computer Vision – OpenCV 1M downloads!
Video Camera Input Desert Road identified
Parallel Body Tracker
Adding vision input asNatural Interface to Physics and Rendering
Visual Programming – rendering, physics, vision inputVisual Programming – rendering, physics, vision input
11Pradeep K. Dubey, [email protected]
Visual ComputingVisual Computing
Closing the loop between computer vision, physical simulation, and photo-realistic rendering, and building an interactive system.
Closing the loop between computer vision, physical simulation, and photo-realistic rendering, and building an interactive system.
TrackingTracking Physics SimulationPhysics SimulationMulti-camera inputMulti-camera input RenderingRendering
Applications:• Games• Virtual Reality• Surgery and health care• Virtual dressing room• Movies and special effects• . . .
Applications:• Games• Virtual Reality• Surgery and health care• Virtual dressing room• Movies and special effects• . . .
12Pradeep K. Dubey, [email protected]
Digital Libraries in the 90’sDigital Libraries in the 90’s Data Base extenders for media data managementData Base extenders for media data management
Server basedServer based
CBIRCBIR IBM QBICIBM QBIC Virage, etc.Virage, etc.
Good for Ad professionalGood for Ad professional Similarity for fade, wipe, etcSimilarity for fade, wipe, etc
Consumers wantConsumers want ““just find it”just find it” Natural user interfaceNatural user interface
13Pradeep K. Dubey, [email protected]
DemosDemos
14Pradeep K. Dubey, [email protected]
New Killer App?: New Killer App?:
There isn’t one There isn’t one -- Same old one: -- Same old one: grep, ctrl-c, ctrl-v
It’s a parallel world!It’s a parallel world!
Shall we look on the other side of theShall we look on the other side of the serial death valley serial death valley??
It’s an analog and non-linear world!It’s an analog and non-linear world!
Computers have digitized and linearized, but … Computers have digitized and linearized, but …
- Real-world problems are still largely non-linear and analog Real-world problems are still largely non-linear and analog
Almost infinite appetite for computational power, if … Almost infinite appetite for computational power, if …
- You reach a certain threshold needed for simulated interactions in real-You reach a certain threshold needed for simulated interactions in real-time.time.
SummarySummary
15Pradeep K. Dubey, [email protected]
Thank You!Thank You!
16Pradeep K. Dubey, [email protected]
Back-upBack-up
17Pradeep K. Dubey, [email protected]
Tomorrow
What is …? What if …?Is it …?
Recognition Mining Synthesis
Create a model instance
RMS: Recognition Mining SynthesisRMS: Recognition Mining Synthesis
Model-basedmultimodalrecognition
Find a modelinstance
Model
Real-time analytics ondynamic, unstructured,
multimodal datasets
Photo-realism andphysics-based
animation
TodayModel-less Real-time streaming and
transactions on static – structured datasets
Very limited realism
18Pradeep K. Dubey, [email protected]
Visual InputStreams
Find an existing model instance
What is …? What if …?Is it …?
Recognition Mining Synthesis
Create a newmodel instance
Emerging Workload Focus: iRMSEmerging Workload Focus: iRMS
Model
Most RMS apps are about enabling interactive (real-time) RMS Loop or iRMSMost RMS apps are about enabling interactive (real-time) RMS Loop or iRMS
SynthesizedVisuals
Learning &Modeling
Graphics Rendering + Physical Simulation
ComputerVision
RealityAugmentation
19Pradeep K. Dubey, [email protected]
“Cognitive SQL”? “Cognitive SQL”?
SQL Today:Table
A B C D . . .Name Age Grade Salary
1 Dave 37 7 $82,000 . . .2 . . .3
. . .
USER:
“Give me all employees who make between $80K and $87K”SQL
API Dave, 37, 7, $82,000, …
. . .
USER:
“Find the variable that most describes compensation”
To
tal
Co
mp
ens
ati
on
Tenure with Company
IPO
Cognitive SQL :A B C D . . .
Name Age Grade Salary1 Dave 37 7 $82,000 . . .2 . . .3
. . .
SpectralClustering
Probabilistic ModelsInference
Clustering,Feature Selection Histograms
DiscriminativeClassifiers/Trees
ML
API
SQL
API
20Pradeep K. Dubey, [email protected]
SummarySummary Design parallel algorithms with parallel computing mindset Design parallel algorithms with parallel computing mindset
from the beginning, not parellelizing serial algorithms. Even from the beginning, not parellelizing serial algorithms. Even “inherently” parallel applications such as Ray tracing and “inherently” parallel applications such as Ray tracing and computer vision requires workcomputer vision requires work
Potential killer app - To satisfy consumer’s requirement of Potential killer app - To satisfy consumer’s requirement of “Just Find it” with natural user interface “Just Find it” with natural user interface
Examples of (iRMS) Interactive Recognition-Mining-Examples of (iRMS) Interactive Recognition-Mining-Synthesis – the essence is the timely delivery of the Synthesis – the essence is the timely delivery of the knowledgeknowledge
Machine learning techniques will play an important role in Machine learning techniques will play an important role in help us extract useful knowledge from the massive amount help us extract useful knowledge from the massive amount of digital datasetof digital dataset
Explore the parallel programming patterns for each domain. Explore the parallel programming patterns for each domain. before we have a book “Parallel computing for dummies” . before we have a book “Parallel computing for dummies” .
21Pradeep K. Dubey, [email protected]
the algorithms can be re-formulated as a sum over the data points and the sum can be broken up over one to many threads
AlgorithmAlgorithm Have summation form?Have summation form?
1.1. Linear regressionLinear regression YesYes
2. 2. Locally weighted linear regression Locally weighted linear regression YesYes
3. 3. Logistic regressionLogistic regression YesYes
4. 4. Gaussian discriminant analysisGaussian discriminant analysis YesYes
5. 5. Naïve BayesNaïve Bayes YesYes
6.6. SVM (without kernel)SVM (without kernel) YesYes
7.7. K-means clusteringK-means clustering YesYes
8.8. EM for mixture distributionsEM for mixture distributions YesYes
9.9. Neural networks Neural networks YesYes
10.10. PCA (Principal components analysis)PCA (Principal components analysis) YesYes
11.11. ICA (Independent components analysis)ICA (Independent components analysis) YesYes
12.12. Policy search (PEGASUS) Policy search (PEGASUS) YesYes
13.13. Boosting Boosting UnknownUnknown
14. 14. SVM (with kernel)SVM (with kernel) UnknownUnknown
15.15. Gaussian process regressionGaussian process regression UnknownUnknown
Machine Learning on Multi-CoreMachine Learning on Multi-Core
22Pradeep K. Dubey, [email protected]
More powerful computer to help us discover new knowledge?More powerful computer to help us discover new knowledge?
Computer has been used to help Researchers discover new knowledge
“Pure Mathematics” - 4 color problem
We have computational geometry, computational chemistry, etc.
Will we have computational history?
23Pradeep K. Dubey, [email protected]
ApplicationsApplications
“New” Innovative Applications?
“There is nothing new under the sun”
This talk: multi-core processing power and “new” techniques to make
some “old” applications work
Innovative Applications -> Afternoon Panel