Technologies and Tools for High-Performance Distributed Computing

Technologies and Tools for High-Performance Distributed Computing

Scientific Discovery through Advanced Computing (SciDAC) The Pennsylvania State University 28 April 2003 David E. Keyes Center for Computational Science Old Dominion University & Institute for Scientific Computing Research Lawrence Livermore National Laboratory Happy Gdels Birthday! A consistency proof for any system can be carried out only by modes of inference that are not formalized in the system itself.. Kurt Gdel Born: 28 April 1906, Brnn, Austria-Hungary

Published Incompleteness Theorem, 1931 Fellow, Royal Society, 1968 National Medal of Science, 1974 Died: 14 January 1978, Princeton, NJ Gave a formal demonstration of the inadequacy of formal demonstrations- anon. PennState Colloquium Remarks This talk is: a personal perspective, not an official statement of the U.S. Department of Energy a project panorama more than a technical presentation For related technical presentations:

Tuesday 2:30pm, 116 McAllister Building personal homepage on the web (www.math.odu.edu/ ~keyes) SciDAC project homepage on the web (www.tops-scidac.org) PennState Colloquium Computational Science & Engineering A multidiscipline on the verge of full bloom Envisioned by Von Neumann and others in the 1940s Undergirded by theory (numerical analysis) for the past fifty years Empowered by spectacular advances in computer architecture over the last twenty years

Enabled by powerful programming paradigms in the last decade Adopted in industrial and government applications Boeing 777s computational design a renowned milestone DOE NNSAs ASCI (motivated by CTBT) DOE SCs SciDAC (motivated by Kyoto, etc.) PennState Colloquium Niche for computational science Has theoretical aspects (modeling) Has experimental aspects (simulation) Unifies theory and experiment by providing

common immersive environment for interacting with multiple data sets of different sources Provides universal tools, both hardware and software Telescopes are for astronomers, microarray analyzers are for biologists, spectrometers are for chemists, and accelerators are for physicists, but computers are for everyone! Costs going down, capabilities going up every year PennState Colloquium Terascale simulation has been sold Applied Physics radiation transport supernovae Environment global climate contaminant transport Biology drug design genomics Engineering crash testing

aerodynamics Scientific Lasers & Energy combustion ICF Simulation In these, and many other areas, simulation is an important complement to experiment. PennState Colloquium Terascale simulation has been sold Applied Physics radiation transport supernovae Experiments controversial Environment global climate contaminant transport Biology drug design

genomics Engineering crash testing aerodynamics Scientific Lasers & Energy combustion ICF Simulation In these, and many other areas, simulation is an important complement to experiment. PennState Colloquium Terascale simulation has been sold Experiments dangerous Experiments controversial Applied Physics radiation transport supernovae

Environment global climate contaminant transport Biology drug design genomics Engineering crash testing aerodynamics Scientific Lasers & Energy combustion ICF Simulation In these, and many other areas, simulation is an important complement to experiment. PennState Colloquium Terascale simulation has been sold Experiments prohibited or impossible Experiments

dangerous Experiments controversial Applied Physics radiation transport supernovae Environment global climate contaminant transport Biology drug design genomics Engineering crash testing aerodynamics Scientific Lasers & Energy combustion ICF Simulation

In these, and many other areas, simulation is an important complement to experiment. PennState Colloquium Terascale simulation has been sold Experiments prohibited or impossible Experiments dangerous Experiments controversial Applied Physics radiation transport supernovae Environment global climate contaminant transport Biology drug design genomics Experiments difficult to instrument

Engineering crash testing aerodynamics Scientific Lasers & Energy combustion ICF Simulation In these, and many other areas, simulation is an important complement to experiment. PennState Colloquium Terascale simulation has been sold Experiments prohibited or impossible Experiments dangerous Experiments controversial Applied Physics radiation transport supernovae

Environment global climate contaminant transport Biology drug design genomics Experiments difficult to instrument Engineering crash testing aerodynamics Experiments expensive Scientific Lasers & Energy combustion ICF Simulation ITER: $20B In these, and many other areas, simulation is an

important complement to experiment. PennState Colloquium Terascale simulation has been sold Experiments prohibited or impossible Experiments dangerous Experiments controversial Applied Physics radiation transport supernovae Environment global climate contaminant transport Biology drug design genomics Experiments difficult to instrument Engineering crash testing

aerodynamics Experiments expensive Scientific Lasers & Energy combustion ICF Simulation However, simulation is far from proven! To meet expectations, we need to handle problems of multiple physical scales. PennState Colloquium Enabling technologies groups to develop reusable software and partner with application groups Since start-up in 2001, 51 projects share $57M per year

Approximately one-third for applications A third for integrated software infrastructure centers A third for grid infrastructure and collaboratories Plus, two new ~10 Tflop/s IBM SP machines available for SciDAC researchers PennState Colloquium SciDAC project characteristics Affirmation of importance of simulation Recognition that leading-edge simulation is interdisciplinary

no independent support for physicists and chemists to write their own software infrastructure; must collaborate with math & CS experts Commitment to distributed hierarchical memory computers for new scientific discovery, not just for fitting experiments new code must target this architecture type Requirement of lab-university collaborations complementary strengths in simulation 13 laboratories and 50 universities in first round of projects PennState Colloquium Major DOE labs Pacific Northwest Brookhaven Argonne Lawrence Berkeley

Old Dominion University Lawrence Livermore Sandia Livermore Los Alamos Oak Ridge Sandia DOE Science Lab DOE Defense Lab PennState Colloquium Large platforms provided for ASCI ASCI roadmap is to go to 100 Teraflop/s by 2006 Use variety of vendors

Compaq Cray Intel IBM SGI Rely on commodity processor/memory units, with tightly coupled network Massive software project to rewrite physics codes for distributed shared memory PennState Colloquium and now for SciDAC IBM Power3+ SMP 16 procs per node 208 nodes 24 Gflop/s per node 5 Tflop/s (upgraded to 10, Feb 2003)

Berkeley IBM Power4 Regatta 32 procs per node 24 nodes 166 Gflop/s per node 4Tflop/s (10 in 2003) Oak Ridge PennState Colloquium New architecture on horizon: QCDOC System-on-a-chip architecture Designed for Columbia University and Brookhaven National Lab by IBM using Power technology Special purpose machine for Lattice Gauge Theory Quantum Chromodynamics very fast conjugate gradient machine with small local memory 10 Tflop/s total, copies ordered for UK, Japan QCD research groups To be delivered August 2003

PennState Colloquium New architecture on horizon: Blue Gene/L 180 Tflop/s configuration (65536 dual processor chips) Closely related to QCDOC prototype (IBM system-on a chip) Ordered for LLNL institutional computing (not ASCI) To be delivered 2004 PennState Colloquium New architecture just arrived: Cray X1 Massively parallel-vector machine highly desired by global climate simulation community 32-processor prototype ordered for evaluation Scale-up to 100 Tflop/s peak planned, if prototype proves successful Delivered to ORNL 18 March 2003 PennState Colloquium

Boundary conditions from architecture Algorithms must run on physically distributed memory units connected by message-passing network, each serving one or more processors with multiple levels of cache horizontal aspects network latency, BW, diameter vertical aspects memory latency, BW; L/S (cache/reg) BW PennState Colloquium Following the platforms Algorithms must be highly concurrent and straightforward to load balance not communication bound cache friendly (temporal and spatial locality of reference) highly scalable (in the sense of convergence)

Goal for algorithmic scalability: fill up memory of arbitrarily large machines while preserving nearly constant* running times with respect to proportionally smaller problem on one processor *logarithmically growing PennState Colloquium Official SciDAC goals Create a new generation of scientific simulation codes that take full advantage of the extraordinary computing capabilities of terascale computers. Create the mathematical and systems software to enable the scientific simulation codes to effectively and efficiently use terascale computers. Create a collaboratory software environment to enable geographically separated scientists to effectively work together as a team and to facilitate remote access to both facilities and data. PennState Colloquium Four science programs involved

14 projects will advance the science of climate simulation and prediction. These projects involve novel methods and computationally efficient approaches for simulating components of the climate system and work on an integrated climate model. 10 projects will address quantum chemistry and fluid dynamics, for modeling energyrelated chemical transformations such as combustion, catalysis, and photochemical energy conversion. The goal of these projects is efficient computational algorithms to predict complex molecular structures and reaction rates with unprecedented accuracy. PennState Colloquium Four science programs involved 4 projects in high energy and nuclear physics will explore the fundamental processes of nature. The projects include the search for the explosion mechanism of core-collapse supernovae, development of a new generation of accelerator simulation codes, and simulations of quantum chromodynamics. 5 projects are focused on developing and improving the physics models needed for integrated simulations of plasma systems to advance fusion energy science. These projects will focus on such fundamental phenomena as electromagnetic wave-plasma

interactions, plasma turbulence, and macroscopic stability of magnetically confined plasmas. PennState Colloquium SciDAC per year portfolio: $57M SciDAC- Program Offices $ in M $7 $3 MICS $2 BER $8 HENP BES FES $37 for Math, Information and Computer Sciences PennState Colloquium

Data grids and collaboratories National data grids Particle physics grid Earth system grid Plasma physics for magnetic fusion DOE Science Grid Middleware Security and policy for group collaboration Middleware technology for science portals Network research Bandwidth estimation, measurement methodologies and application Optimizing performance of distributed applications Edge-based traffic processing Enabling technology for wide-area data intensive applications PennState Colloquium Computer Science ISICs

Scalable Systems Software Provide software tools for management and utilization of terascale resources. High-end Computer System Performance: Science and Engineering Develop a science of performance prediction based on concepts of program signatures, machine signatures, detailed profiling, and performance simulation and apply to complex DOE applications. Develop tools that assist users to engineer better performance. Scientific Data Management Provide a framework for efficient management and data mining of large, heterogeneous, distributed data sets. Component Technology for Terascale Software Develop software component technology for high-performance parallel scientific codes, promoting reuse and interoperability of complex software, and assist application groups to incorporate component technology into their high-value codes. PennState Colloquium Applied Math ISICs

Terascale Simulation Tools and Technologies Develop framework for use of multiple mesh and discretization strategies within a single PDE simulation. Focus on high-quality hybrid mesh generation for representing complex and evolving domains, high-order discretization techniques, and adaptive strategies for automatically optimizing a mesh to follow moving fronts or to capture important solution features. Algorithmic and Software Framework for Partial Differential Equations Develop framework for PDE simulation based on locally structured grid methods, including adaptive meshes for problems with multiple length scales; embedded boundary and overset grid methods for complex geometries; efficient and accurate methods for particle and hybrid particle/mesh simulations. Terascale Optimal PDE Simulations Develop an integrated toolkit of near optimal complexity solvers for nonlinear PDE simulations. Focus on multilevel methods for nonlinear PDEs, PDE-based eigenanalysis, and optimization of PDE-constrained systems. Packages sharing same distributed data structures include: adaptive time integrators for stiff systems, nonlinear implicit solvers, optimization, linear solvers, and eigenanalysis. PennState Colloquium Applied Math ISICs

Terascale Simulation Tools and Technologies Develop framework for use of multiple mesh and discretization strategies within a single PDE simulation. Focus on high-quality hybrid mesh generation for representing complex and evolving domains, high-order discretization techniques, and adaptive strategies for automatically optimizing a mesh to follow moving fronts or to capture important solution features. Algorithmic and Software Framework for Partial Differential Equations Develop framework for PDE simulation based on locally structured grid methods, including adaptive meshes for problems with multiple length scales; embedded boundary and overset grid methods for complex geometries; efficient and accurate methods for particle and hybrid particle/mesh simulations. Terascale Optimal PDE Simulations Develop an integrated toolkit of near optimal complexity solvers for nonlinear PDE simulations. Focus on multilevel methods for nonlinear PDEs, PDE-based eigenanalysis, and optimization of PDE-constrained systems. Packages sharing same distributed data structures include: adaptive time integrators for stiff systems, nonlinear implicit solvers, optimization, linear solvers, and eigenanalysis. PennState Colloquium Applied Math ISICs

Terascale Simulation Tools and Technologies Develop framework for use of multiple mesh and discretization strategies within a single PDE simulation. Focus on high-quality hybrid mesh generation for representing complex and evolving domains, high-order discretization techniques, and adaptive strategies for automatically optimizing a mesh to follow moving fronts or to capture important solution features. Algorithmic and Software Framework for Partial Differential Equations Develop framework for PDE simulation based on locally structured grid methods, including adaptive meshes for problems with multiple length scales; embedded boundary and overset grid methods for complex geometries; efficient and accurate methods for particle and hybrid particle/mesh simulations. Terascale Optimal PDE Simulations Develop an integrated toolkit of near optimal complexity solvers for nonlinear PDE simulations. Focus on multilevel methods for nonlinear PDEs, PDE-based eigenanalysis, and optimization of PDE-constrained systems. Packages sharing same distributed data structures include: adaptive time integrators for stiff systems, nonlinear implicit solvers, optimization, linear solvers, and eigenanalysis. PennState Colloquium Exciting time for enabling technologies

SciDAC application groups have been chartered to build new and improved COMMUNITY CODES. Such codes, such as NWCHEM, consume hundreds of person-years of development, run at hundreds of installations, are given large fractions of community compute resources for decades, and acquire an authority that can enable or limit what is done and accepted as science in their respective communities. Except at the beginning, it is difficult to promote major algorithmic ideas in such codes, since change is expensive and sometimes resisted. ISIC groups have a chance, due to the interdependence built into the SciDAC program structure, to simultaneously influence many of these codes, by delivering software incorporating optimal algorithms that may be reused across many applications. Improvements driven by one application will be available to all. While they are building community codes, this is our chance to build a CODE COMMUNITY! PennState Colloquium SciDAC themes Chance to do community codes right Meant to set new paradigm for other DOE programs new 2003 nano science modeling initiative

possible new 2004 fusion simulation initiative Cultural barriers to interdisciplinary research acknowledged up front Accountabilities constructed in order to force the mixing of scientific cultures (physicists/biologists/chemists/engineers with mathematicians/computer scientists) PennState Colloquium Opportunity: nanoscience modeling Jul 2002 report to DOE Proposes $5M/year theory and modeling initiative to accompany the existing $50M/year experimental initiative in nano science

Report lays out research in numerical algorithms and optimization methods on the critical path to progress in nanotechnology PennState Colloquium Opportunity: integrated fusion modeling Dec 2002 report to DOE Currently DOE supports 52 codes in Fusion Energy Sciences US contribution to ITER will major in simulation Initiative proposes to use

advanced computer science techniques and numerical algorithms to improve the US code base in magnetic fusion energy and allow codes to interoperate PennState Colloquium Whats new in SciDAC library software? Philosophy of library usage large codes interacting as peer applications, with complex calling patterns (e.g., physics code calls implicit solver code calls subroutine automatically generated from original physics code to supply Jacobian of physics code residual) extensibility polyalgorithmic adaptivity Resources for development, long-term

maintenance, and support not just for dissertation scope ideas Experience on terascale computers PennState Colloquium Introducing Terascale Optimal PDE Simulations (TOPS) ISIC Nine institutions, $17M, five years, 24 co-PIs PennState Colloquium 34 apps groups (BER, BES,FES, HENP) adaptive gridding, discretization solvers 7 ISIC groups (4 CS, 3 Math) 10 grid, data collaboratory

groups systems software, component architecture, performance engineering, data management Ax b Ax Bx f ( x , x, t , p ) 0 F ( x, p ) 0 min ( x, u ) u s.t. F ( x, u ) 0 software integration performance optimization PennState Colloquium Who we are

the PETSc and TAO people the Hypre and Sundials people the SuperLU and PARPACK people as well as the builders of other widely used packages PennState Colloquium Plus some university collaborators Demmel et al. Widlund et al. Manteuffel et al. Dongarra et al. Ghattas et al. Keyes et al. Our DOE lab collaborations predate SciDAC by many years. PennState Colloquium You may know the on-line Templates guides www.netlib.org/templates 124 pp. www.netlib.org/etemplates

410 pp. these are good starts, but not adequate for SciDAC scales! PennState Colloquium You may know the on-line Templates guides www.netlib.org/templates www.netlib.org/etemplates 124 pp. 410 pp. SciDAC puts some of the authors (and many others) on-line for physics groups PennState Colloquium Scope for TOPS Design and implementation of solvers Time integrators (w/ sens. anal.) Nonlinear solvers (w/ sens. anal.) Optimizers

f ( x , x, t , p) 0 F ( x, p ) 0 min ( x, u ) s.t. F ( x, u ) 0, u 0 u Linear solvers Eigensolvers Software integration Performance optimization Optimizer Sens. Analyzer Time integrator Nonlinear

solver Eigensolver Ax b Ax Bx Linear solver Indicates dependence PennState Colloquium The power of optimal algorithms Advances in algorithmic efficiency rival advances in hardware architecture Consider Poissons equation on a cube of size N=n3 Year Method Reference

Storage Flops 1947 GE (banded) Von Neumann & Goldstine n5 n7 1950 Optimal SOR Young n3 n4 log n 1971 CG Reid

n3 n3.5 log n 1984 Full MG Brandt n3 n3 64 64 2u=f If n=64, this implies an overall reduction in flops of ~16 million * *On a 16 Mflop/s machine, six-months is reduced to 1 s PennState Colloquium 64

Algorithms and Moores Law This advance took place over a span of about 36 years, or 24 doubling times for Moores Law 22416 million the same as the factor from algorithms alone! relative speedup year PennState Colloquium The power of optimal algorithms Since O(N) is already optimal, there is nowhere further upward to go in efficiency, but one must extend optimality outward, to more general problems Hence, for instance, algebraic multigrid (AMG), obtaining O(N) in indefinite, anisotropic, inhomogeneous problems R

n AMG Framework error damped by pointwise algebraically relaxation smooth error Choose coarse grids, transfer operators, etc. to eliminate, based on numerical weights, heuristics PennState Colloquium Gordon Bell Prize performance Year 1988 1989 1990 1992 1993 1994 1995 1996 1997 1998 1999

2000 2001 2002 Type PDE PDE PDE NB MC IE MC PDE NB MD PDE NB NB PDE Application Gflop/s System No. Procs Structures 1.0 Cray Y-MP 8 Seismic 5.6 CM-2 2,048 Seismic 14 CM-2

2,048 Gravitation 5.4 Delta 512 Boltzmann 60 CM-5 1,024 Structures 143 Paragon 1,904 QCD 179 NWT 128 CFD 111 NWT 160 Gravitation 170 ASCI Red 4,096 Magnetism 1,020 T3E-1200 1,536 CFD 627 ASCI BluePac 5,832 Gravitation 1,349 GRAPE-6 96 Gravitation 11,550 GRAPE-6

1,024 Climate 26,500 Earth Sim ~5,000 PennState Colloquium Gordon Bell Prize outpaces Moores Law Gordon Bell CONCURRENCY!!! Gordon Moore <> Four orders of magnitude in 13 years PennState Colloquium SciDAC application: Center for Extended Magnetohydrodynamic Modeling Simulate plasmas in tokomaks, leading to understanding of plasma instability and (ultimately) new energy sources Joint work between ODU, Argonne, LLNL, and PPPL PennState Colloquium Optimal solvers Convergence rate nearly independent

of discretization parameters Multilevel schemes for linear and nonlinear problems Newton-like schemes for quadratic convergence of nonlinear problems 700 600 iters 500 400 ASM-GMRES AMG-FMGRES 300 200 100 0 60 200 Time to Solution

sc un 150 12 27 procs 48 75 time 50 ble ala 40 100 ASM-GMRES

AMG-FMGRES AMG inner 30 50 0 1 3 20 scalable 10 100 1000 Problem Size (increasing with number of processors) AMG shows perfect iteration scaling, above, in contrast to ASM, but still needs performance work to achieve temporal scaling, below, on CEMM fusion code,

M3D, though time is halved (or better) for large runs (all runs: 4K dofs per processor) 10 0 3 12 27 48 75 PennState Colloquium Solver interoperability accomplishments Hypre in PETSc SuperLU_DIST in PETSc

codes with PETSc interface (like CEMMs M3D) can invoke Hypre routines as solvers or preconditioners with commandline switch as above, with SuperLU_DIST Hypre in AMR Chombo code so far, Hypre is level-solver only; its AMG will ultimately be useful as a bottom-solver, since it can be coarsened indefinitely without attention to loss of nested geometric structure; also FAC is being developed for AMR uses, like Chombo PennState Colloquium Background of PETSc Library Developed by at Argonne to support research, prototyping, and production parallel solutions of operator equations in message-passing environments; now joined by four additional staff under SciDAC Distributed data structures as fundamental objects - index sets, vectors/gridfunctions, and matrices/arrays

Iterative linear and nonlinear solvers, combinable modularly and recursively, and extensibly Portable, and callable from C, C++, Fortran Uniform high-level API, with multi-layered entry Aggressively optimized: copies minimized, communication aggregated and overlapped, caches and registers reused, memory chunks preallocated, inspector-executor model for repetitive tasks (e.g., gather/scatter) See http://www.mcs.anl.gov/petsc PennState Colloquium User Code/PETSc Library Interactions Main Routine Timestepping Solvers (TS) Nonlinear Solvers (SNES) Linear Solvers (SLES) PC

PETSc KSP Application Initialization Function Evaluation User code Jacobian Evaluation PostProcessing PETSc code PennState Colloquium User Code/PETSc Library Interactions Main Routine Timestepping Solvers (TS) Nonlinear Solvers (SNES) Linear Solvers (SLES) PC PETSc

KSP Application Initialization Function Evaluation User code Jacobian Evaluation PETSc code PostProcessing To be AD code PennState Colloquium Background of Hypre Library (to be combined with PETSc under SciDAC) Developed by Livermore to support research, prototyping, and production parallel solutions of operator equations in message-passing environments; now joined by seven additional staff under ASCI and SciDAC

Object-oriented design similar to PETSc Concentrates on linear problems only Richer in preconditioners than PETSc, with focus on algebraic multigrid Includes other preconditioners, including sparse approximate inverse (Parasails) and parallel ILU (Euclid) See http://www.llnl.gov/CASC/hypre/ PennState Colloquium Hypres Conceptual Interfaces Linear System Interfaces Linear Solvers GMG, ... FAC, ... Hybrid, ...

AMGe, ... ILU, ... unstruc CSR Data Layout structured composite Slide c/o R. Falgout, LLNL block-struc PennState Colloquium Eigensolvers for Accelerator Design Stanfords Omega3P is using TOPS software to find EM modes of accelerator cavities Methods: Exact Shift-and-Invert Lanczos (ESIL), combining PARPACK with SuperLU when there is

sufficient memory, and Jacobi-Davidson otherwise Current high-water marks: 47-cell chamber, finite element discr. of Maxwells eqs. System dimension 1.3 million 20 million nonzeros in system, 350 million in LU factors halved analysis time on 48 processors, scalable to many hundreds PennState Colloquium Optimizers Unconstrained or boundconstrained optimization PDE-constrained optimization

TAO (powered by PETSc, interfaced in CCTTSS component framework) used in quantum chemistry energy minimization Veltisto (powered by PETSC) used in flow control application, to straighten out wingtip vortex by wing surface blowing and sunction Best technical paper at SC2002 went to TOPS team PETSc-powered inverse wave propagation employed to infer hidden geometry 4000 controls 128 procs 2 million controls 256 procs PennState Colloquium Performance TOPS is tuning sparse kernels

Running on dozens of apps/platform combinations (Jacobian) matrix-vector multiplication sparse factorization multigrid relaxation Power3 (NERSC) and Power4 (ORNL) factors of 2 on structured (CMRS) and unstructured (CEMM) fusion apps Best student paper at ICS2002 went to TOPS team theoretical model and experiments on effects of register blocking for sparse matvec Blocking of 4 rows by 2 columns is 4.07 times faster on Itanium2 than default 11 blocks

PennState Colloquium Lessons to date Working with the same code on the same machine vastly speeds collaboration, as opposed to ftping matrices around the country, etc. Exchanging code templates better than exchanging papers, etc. Version control systems essential to having any last impact or insertion path for solver improvements Doing physics more fun than doing driven cavities PennState Colloquium Abstract Gantt Chart for TOPS Each color module represents an algorithmic research idea on its way to becoming part of a supported community software tool. At any moment (vertical time slice), TOPS has work underway at multiple levels.

While some codes are in applications already, they are being improved in functionality and performance as part of the TOPS research agenda. Dissemination Applications Integration e.g.,PETSc Hardened Codes e.g.,TOPSLib Research Implementations e.g., ASPIN Algorithmic Development time PennState Colloquium Goals/Success Metrics TOPS users Understand range of algorithmic options and their tradeoffs (e.g., memory versus time) Can try all reasonable options easily without recoding or extensive recompilation

Know how their solvers are performing Spend more time in their physics than in their solvers Are intelligently driving solver research, and publishing joint papers with TOPS researchers Can simulate truly new physics, as solver limits are steadily pushed back PennState Colloquium Expectations TOPS has of Users

Be willing to experiment with novel algorithmic choices optimality is rarely achieved beyond model problems without interplay between physics and algorithmics! Adopt flexible, extensible programming styles in which algorithmic and data structures are not hardwired Be willing to let us play with the real code you care about, but be willing, as well to abstract out relevant compact tests Be willing to make concrete requests, to understand that requests must be prioritized, and to work with us in addressing the high priority requests If possible, profile, profile, profile before seeking help PennState Colloquium For more information ... [email protected] http://www.tops-scidac.org PennState Colloquium Related URLs Personal homepage: papers, talks, etc. http://www.math.odu.edu/~keyes SciDAC initiative

http://www.science.doe.gov/scidac TOPS project http://www.math.odu.edu/~keyes/scidac PETSc project http://www.mcs.anl.gov/petsc Hypre project http://www.llnl.gov/CASC/hypre ASCI platforms http://www.llnl.gov/asci/platforms ISCR: annual report, etc. http://www.llnl.gov/casc/iscr PennState Colloquium

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