Grama

Information about Grama

Published on September 25, 2007

Author: Shariyar

Source: authorstream.com

Content

Petascale Applications in Biology:  Petascale Applications in Biology Critical Issues, Outstanding Challenges, and Potential Impact The Incredibly accomplished and eminently qualified individuals of Grp 1. The Big Picture:  The Big Picture Applications Abstractions Models Algorithms Implementation Validation Some Phrases.:  Some Phrases. 'Forward Science!' 'People are expensive, machines are cheap (software is expensive, hardware is cheap)'. 'Just because we do not know how to solve a problem does not imply that a petaFLOPS computer will solve it!' 'Nothing scales to a petaFLOP, unless otherwise demonstrated or proven.' Applications:  Applications Modeling Biomolecular Complexes (from structure to function). Systems involve upwards of 100M atoms Functions may manifest over very large timescales Models are typically hierarchical – (DFT/Reactive MD/MD/continuum/system/ensembles) Study of anomalies often more important than canonical systems (protein misfolding) Valid case for a petascale application Issues: coarse graining, dealing with time-serialization, solvent models, electrostatics Applications:  Applications Modeling complex biological systems -- From Cells to Organs (neuronal modeling, cardiovascular simulations, radiation transport, pathophysiology) Multiscale/Multiphysics models, Monte Carlo methods, reaction-diffusion in both intracellular and extracellular space, circuit models Typical problem sizes: finite elements -- 1010 higher order finite elements Typical models: Hybrid / stochastic In many applications, geometry is a major consideration Valid case for a petascale application Applications:  Applications Genome-wide association (cell division, genotype-phenotype association) Follows classic data to model to knowledge cycle Algorithms and models are ill-understood Issues of data federation and availability are critical Likely case for petascale application Applications:  Applications Customized patient care Inverse problems over multimodal imaging data (functional, structural) Real-time constraints and resolution may conspire to make problem petascale More expensive covariance models Unknown whether this is really a petascale application. Application:  Application Ecological component of earth system modeling Data integration is critical Case for petascale application is easily made. Application:  Application Evolutionary correlations of networks and functions (phenotypes) Often lead to NP Hard/Complete problems Not clear if this is a petascale application Application:  Application Infectious disease modeling, agent based modeling of populations, applied to ecology as well as epidemiology, spatio-temporal, genotype/phenotype evolution Critical for CDC/DHS Likely case for petascale applications Applications:  Applications Genomics in the large: Metagenomics (gemones of entire microbial communities) Genomics in the small: genotype (SNP)-phenotype modeling (50M SNPs x 1000 individuals x 50 phenotypes) Likely case for petascale applications Models and Abstractions:  Models and Abstractions Commonly used classes of models First Principles Macroscopic Models (Continuum, Coarse Grained) Data Driven Models (Data to Knowledge) For important classes of problems, models do not exist – identify and develop models. For some others, coarse grained models need to be derived for scaling to needed scales – coarse graining techniques. Models and Abstractions:  Models and Abstractions Many applications integrate a range of models (scales and phenomena) Coupling models in efficient and accurate ways Classical examples in DFT/MD/Continuum/System From molecules to organisms to ecosystems From ion transport to cardio-vascular simulation Information flow across models Integrating data into models (model inference) Algorithm and Implementation Issues:  Algorithm and Implementation Issues Many problems pose challenges for hardware/algorithms (PME/FFT) – novel algorithmic approaches/hardware. Several applications do not fit traditional numerical frameworks (FLOPS vs OPS, spatio-temporal locality) – articulate application needs, focus on computing infrastructure. Tremendous issues associated with data handling (federation of data sources – expression, sequence, phenotype, ..) – I/O and file systems, data federation. Dealing with uncertainty in data, missing data, erroneous data – sensitivity analysis. Real time constraints (epidemiology, customized care) – model to the resolution limited by real-time constraints. Algorithm and Implementation Issues:  Algorithm and Implementation Issues Sampling issues Conformational sampling Spatio-temporal sampling Accuracy issues Systems and Architectures:  Systems and Architectures Application specific architectures Memory intensive applications Coprocessors (ASICs, FPGAs, DSPs, GPUs) Bandwidth intensive applications Matching heterogeneity in applications and architectures Data federation, availability Integrating data acquisition systems (sequencers, microarrays, imaging) Analytical Models:  Analytical Models Models for performance analysis and scaling (show that an algorithm scales before implementing it!) Models for quantifying uncertainty (accuracy) Stochastic Modeling Quantifying performance of heuristics

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