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High-Performance Computing, Computational Science, and NeuroInformatics Research. Allen D. Malony Department of Computer and Information Science NeuroInformatics Center (NIC) Computational Science Institute University of Oregon. Outline. High-performance computing research - PowerPoint PPT Presentation
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High-Performance Computing, Computational Science, and NeuroInformatics Research
Allen D. Malony
Department of Computer and Information ScienceNeuroInformatics Center (NIC)Computational Science Institute
University of Oregon
April 29, 2004 PNNL UO Visit
Outline
High-performance computing research Interactions and funding Project areas TAU parallel performance system
Computational science at UO Projects Computational Science Institute
Neuroinformatics research NeuroInformatics Center (NIC)
ICONIC Grid
April 29, 2004 PNNL UO Visit
High-Performance Computing Research
Strong associations with DOE national laboratories Los Alamos National Lab Lawrence Livermore National Lab Sandia National Lab (Livermore) Argonne National Lab National Energy Research Supercomputing Center
DOE funding Office of Science, Advance Scientific Computing ASCI/NNSA
NSF funding Academic Research Infrastructure Major Research Instrumentation
April 29, 2004 PNNL UO Visit
Project Areas
Parallel performance evaluation and tools Parallel language systems Tools for parallel system and software interaction Source code analysis Parallel component software Computational services Grid computing Parallel modeling and simulation Scientific problem solving environments
Allen D. Malony Sameer S. Shende
Department of Computer and Information Science
Computational Science Institute
University of Oregon
TAU Parallel Performance System
April 29, 2004 PNNL UO Visit
Parallel Performance Research
Tools for performance problem solving Empirical-based performance optimization process
characterization
PerformanceTuning
PerformanceDiagnosis
PerformanceExperimentation
PerformanceObservation
hypotheses
properties
• Instrumentation• Measurement• Analysis• Visualization
PerformanceTechnology
April 29, 2004 PNNL UO Visit
Complexity Challenges for Performance Tools
Computing system environment complexity Observation integration and optimization Access, accuracy, and granularity constraints Diverse/specialized observation capabilities/technology Restricted modes limit performance problem solving
Sophisticated software development environments Programming paradigms and performance models Performance data mapping to software abstractions Uniformity of performance abstraction across platforms Rich observation capabilities and flexible configuration Common performance problem solving methods
April 29, 2004 PNNL UO Visit
General Problems
How do we create robust and ubiquitous performance technology for the analysis and tuning of parallel and
distributed software and systems in the presence of (evolving) complexity challenges?
How do we apply performance technology effectively for the variety and diversity of performance problems
that arise in the context of complex parallel and distributed computer systems?
April 29, 2004 PNNL UO Visit
TAU Performance System
Tuning and Analysis Utilities Performance system framework for scalable parallel and
distributed high-performance computing Targets a general complex system computation model
nodes / contexts / threads Multi-level: system / software / parallelism Measurement and analysis abstraction
Integrated toolkit for performance instrumentation, measurement, analysis, and visualization Portable performance profiling and tracing facility Open software approach with technology integration
University of Oregon , Forschungszentrum Jülich, LANL
April 29, 2004 PNNL UO Visit
TAU Performance System Status
Computing platforms IBM SP / Power4, SGI Origin 2K/3K, ASCI Red, Cray
T3E / SV-1 / X-1, HP (Compaq) SC (Tru64), HP Superdome (HP-UX), Sun, Hitachi SR8000, NEX SX-5/6, Linux clusters (IA-32/64, Alpha, PPC, PA-RISC, Power, Opteron), Apple (G4/5, OS X), Windows
Programming languages C, C++, Fortran 77/90/95, HPF, Java, OpenMP, Python
Communication libraries MPI, PVM, Nexus, shmem, LAMPI, MPIJava
Thread libraries pthreads, SGI sproc, Java,Windows, OpenMP
April 29, 2004 PNNL UO Visit
TAU Performance System Status (continued)
Compilers Intel KAI (KCC, KAP/Pro), PGI, GNU, Fujitsu, Sun,
Microsoft, SGI, Cray, IBM (xlc, xlf), Compaq, Hitachi, NEC, Intel
Application libraries (selected) Blitz++, A++/P++, PETSc, SAMRAI, Overture, PAWS
Application frameworks (selected) POOMA, MC++, ECMF, Uintah, VTF, UPS, GrACE
Performance technology integrated with TAU PAPI, PCL, DyninstAPI, mpiP, MUSE/Magnet
TAU full distribution (Version 2.x, web download) TAU performance system toolkit and user’s guide Automatic software installation and examples
April 29, 2004 PNNL UO Visit
Computational Science
ComputerScience
Biology
Neuroscience
PsychologyPaleontology
Geoscience
Math
Integration of computer sciencein traditional sciencedisciplines
Third model ofscientificresearch
Application ofhigh-performancecomputation, algorithmsand networking Parallel computing Grid computing
April 29, 2004 PNNL UO Visit
Computational Science Projects at UO
Geological science Model coupling for hydrology
Bioinformatics Zebrafish Information Network (ZFIN) Evolution of gene families Oregon Bioinformatics Tool
Neuroinformatics Electronic notebooks Domain-specific problem solving environments
Dinosaur skeleton and motion modeling Computational Science Institute
April 29, 2004 PNNL UO Visit
Computational Science Cognitive Neuroscience
Computational methods applied to scientific research High-performance simulation of complex phenomena Large-scale data analysis and visualization
Understand functional activity of the human cortex Multiple cognitive, clinical, and medical domains Multiple experimental paradigms and methods
Need for coupled/integrated modeling and analysis Multi-modal (electromagnetic, MR, optical) Physical brain models and theoretical cognitive models
Need for robust tools: computational & informatic
April 29, 2004 PNNL UO Visit
Brain Dynamics Analysis Problem
Identify functional components Different cognitive neuroscience research contexts Clinical and medical applications
Interpret with respect to physical and cognitive models Requirements: spatial (structure), temporal (activity) Imaging techniques for analyzing brain dynamics
Blood flow neuroimaging (PET, fMRI) good spatial resolution functional brain mapping temporal limitations to tracking of dynamic activities
Electromagnetic measures (EEG/ERP, MEG) msec temporal resolution to distinguish components spatial resolution sub-optimal (source localization)
April 29, 2004 PNNL UO Visit
Integrated Electromagnetic Brain Analysis
IndividualBrain Analysis
Structural /FunctionalMRI/PET
DenseArray EEG /
MEG
ConstraintAnalysis
Head Analysis
Source Analysis
Signal Analysis
Response Analysis
Experimentsubject
temporaldynamics
neuralconstraints
CorticalActivity Model
ComponentResponse Model
spatial patternrecognition
temporal patternrecognition
Cortical ActivityKnowledge Base
Component ResponseKnowledge Base
good spatialpoor temporal
poor spatialgood temporal neuroimaging
integration
April 29, 2004 PNNL UO Visit
Experimental Methodology and Tool Integration
source localization constrained to cortical surface
processed EEG
BrainVoyager
BESA
CT / MRI
EEG segmentedtissues
16x256bits permillisec(30MB/m)
mesh generation
EMSEInterpolator 3D
NetStation
April 29, 2004 PNNL UO Visit
NeuroInformatics Center (NIC)
Application of computational science methods to cognitive and clinical neuroscience problems Understand functional activity of the brain Help to diagnosis brain-related disorders Utilize high-performance computing and simulation Support large-scale data analysis and visualization
Advance techniques for integrated neuroimaging Coupled modeling (EEG/ERP and MR analysis) Advanced statistical factor analysis FDM/FEM brain models (EEG, CT, MRI) Source localization
Problem-solving environment for brain analysis
April 29, 2004 PNNL UO Visit
NIC Organization
Director, Allen D. Malony Associate Professor, Computer and Information Science
Associate Director, Don M. Tucker Professor, Psychology; CEO, EGI
Computational Scientist, Kevin Glass Ph.D., Computer Science; B.S., Physics
Computational Physicist, Sergei Turovets Ph.D., Computer Science; B.S., Physics
Computer Scientist, Sameer S. Shende Ph.D., Computer Science; parallel computing specialist
Mathematician, Bob Frank M.S., Mathematics
April 29, 2004 PNNL UO Visit
Funding Support
BBMI federal appropriation DoD Telemedicine Advanced Technology Research
Command (TATRC) Initial budget of approximately $750K Oct. 1, 2002 through March 31, 2004
NSF Major Research Instrumentation ICONIC Grid, awarded
New proposal opportunities NIH Human Brain Project Neuroinformatics NSF ITR
April 29, 2004 PNNL UO Visit
NIC Approaches
Optimize spatial resolution MRI structural information Measurement of skull conductivity Convergence / co-recording with MEG and fMRI
Optimize temporal resolution Use EEG/MEG time course for fMRI signal extraction Decomposition of component analysis (ICA, PCA) Single-trial analysis
Computational brain models Boundary and finite element brain models Brain information databases and atlases
April 29, 2004 PNNL UO Visit
EEG/ERP Methodology
Electroencephalogram (EEG) Event-Related Potential (ERP)
Stimulus-locked measures of brain dynamics Generated from subject- and trial-based analysis
Raw EEG datasets processed and analyzed Segmentation to time series waveforms Blink removal and other cleaning ERP analysis
Averaging for increasing signal to noise Characterization with respect to trial conditions Results visualization
Source localization
April 29, 2004 PNNL UO Visit
EGI Geodesics Sensor Net
Electrical Geodesics Inc. Dense-array sensor technology
64/128/256 channels 256-channel geodesics sensor net
AgCl plastic electrodes Carbon fiber leads
Future optical sensors EGI + LANL
April 29, 2004 PNNL UO Visit
EEG/ERP Experiment Management System
Support EEG-based cognitive neuroscience research Based on experiment model
Experiment type Subjects measured for trial types
Management of experiment data Raw and processed datasets and derived statistics Per experiment/subject/trial database Secure protection and storage with selective access
Analysis tools and workflows Generation of results (across experimental variables) Analysis processes with multi-tool workflows
April 29, 2004 PNNL UO Visit
EEG/ERP Experiment Analysis Environment
… …
rawprocessed datasets/ derived resultsanalysis workflow
storageresources
virtualservices
compute resources
April 29, 2004 PNNL UO Visit
Source Localization
Mapping of scalp potentials to cortical generators Single time sample and time series
Requirements Accurate head model and physics
High-resolution 3D structural geometry Precise tissue identification and segmentation Correct tissue conductivity assessment
Computational head model formulation Finite element model (FEM) Finite difference model (FDM) Forward problem calculation
Dipole search strategy
April 29, 2004 PNNL UO Visit
Advanced Image Segmentation
Native MR gives high gray-to-white matter contrast
Edge detection finds region boundaries
Segments formed by edge merger
Color depicts tissue type Investigate more advance
level set methods and hybrid methods
April 29, 2004 PNNL UO Visit
Building Finite Element Brain Models
MRI segmentation of brain tissues Conductivity model
Measure head tissue conductivity Electrical impedance tomography
small currents are injectedbetween electrode pair
resulting potential measuredat remaining electrodes
Finite element forward solution Source inverse modeling
Explicit and implicit methods Bayesian methodology
scalp
CSF
skull
cortex
April 29, 2004 PNNL UO Visit
Conductivity Modeling
Governing Equations ICS/BCS
Discretization
System of Algebraic Equations
Equation (Matrix) Solver
Approximate Solution
Continuous Solutions
Finite-DifferenceFinite-Element
Boundary-ElementFinite-Volume
Spectral
Discrete Nodal Values
TridiagonalADISOR
Gauss-SeidelGaussian elimination
(x,y,z,t)J (x,y,z,t)B (x,y,z,t)
April 29, 2004 PNNL UO Visit
Source Localization Analysis Environment
… …
raw
storageresources
virtualservices
compute resources
April 29, 2004 PNNL UO Visit
NIC Computational Cluster (“Neuronic Cluster”)
Dell computational cluster 16 dual-processor nodes
2.8 MHz Pentium Xeon 4 Gbyte memory 36 Gbyte disk Dual Gigabit ethernet adaptors 2U form factor
Master node (same specs) 2 Gigabit ethernet switches
Brain modeling Component analysis
April 29, 2004 PNNL UO Visit
NIC Relationships
Psychology
CIS
BDL BEL
CSI
OHSU/ OGI
Utah
UCSD
USCAcademic Labs / Centers
LANL Argonne
NCSAInternet2
EGI
Industry
Intel IBM
NIC
UO Departments
UO Centers/Institutes
BBMI CDSI
CNI
Physics
NSI
Sandia
April 29, 2004 PNNL UO Visit
NSF MRI Proposal
Major Research Instrumentation (MRI) “Acquisition of the Oregon ICONIC Grid for
Integrated COgnitive Neuroscience Informatics and Computation”
PIs Computer Science: Malony, Conery Psychology: Tucker, Posner, Nunnally
Senior personnel Computer Science: Douglas, Cuny Psychology: Neville, Awh, White
Approximately $1.2M over three years
April 29, 2004 PNNL UO Visit
SMPServerIBM p655
GraphicsSMP
SGI MARS
SAN Storage System
Gbit Campus Backbone
NIC CIS CIS
Internet 2
SharedMemory
IBM p690
DistributedMemory
IBM JS20
CNI
DistributedMemory
Dell Pentium Xeon
NIC4x8 16 16 2x8 2x16
graphics workstations interactive, immersive viz other campus clusters
ICONIC Grid
5 Terabytes
April 29, 2004 PNNL UO Visit
Cognitive Neuroscience and ICONIC Grid
Common questions to be explored Identifying brain networks Critical periods during normal development Network involvement in psychopathologies Training interventions in network development
Research areas Development of attentional networks Brain plasticity in normal development and deprived Attention and emotion regulation Spatial working memory and selective attention Attention and psychopathology
April 29, 2004 PNNL UO Visit
Computer Science and ICONIC Grid
Scheduling and resource management Assign hardware resources to computation tasks Scheduling of workloads for
PSEs for computational science Provide scientists an entrée to the computational and
data management power of the infrastructure without requiring specialized knowledge of parallel execution
Marine seismic tomograph, molecular evolution Interactive / immersive three-dimensional visualization
Explore multi-sensory visualization Merge 3D graphics with force-feedback haptics
Parallel performance evaluation