1. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC NAMD Development Goals L.V. (Sanjay) Kale Professor Dept. of Computer Science http://www.ks.uiuc.edu/Research/namd/

2. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC NAMD Vision Make NAMD a widely used MD program –For large molecular systems, –Scaling from PCs, clusters, to large parallel machines –For interactive molecular dynamics Goals: –High performance –Ease of use: configuration and run –Ease of modification (for us and advanced users) Maximize reuse of communication and control patterns Push parallel complexity down into Charm++ runtime –Incorporation of features needed by Scientists

3. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC NAMD 3 New Features Software Goal: –Modular architecture to permit reuse extensibility Scientific/Numeric Modules: –Implicit solvent models (e.g, generalized Born) –Replica exchange (e.g., 10 on 16 processors) –Self-consistent polarizability with a (sequential) CPU penalty of less than 100%. –Hybrid quantum/classical mechanics –Fast nonperiodic (and periodic) electrostatics using multiple grid methods. –A Langevin integrator that permits larger time steps (by being exact for constant forces). –An integrator module that computes shadow energy.

4. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Design NAMD 3 will be a major rewrite of NAMD –Incorporate lessons learned in the past years –Use modern features of Charm++ –Refactor software for modularity –Restructure for supporting planned features –Algorithms that scale to even larger machines

5. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Programmability NAMD3 Scientific Modules: –Forces, integration, steering, analysis –Keep code with a common goal together –Add new features without touching old code Parallel Decomposition Framework: –Support common scientific algorithm patterns –Avoid duplicating services for each algorithm –Start with NAMD 2 architecture (but not code)

6. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Core CHARM++ ClustersLemieuxTeragrid Collective communicationLoad balancer FFTFault ToleranceGrid Scheduling Bonds related Force calculation IntegrationPair-wise Forces calculation PME Charm++ modules NAMD Core Replica exchangeQMImplicit SolventsPolarizable Force Field MDAPI … New Science modules

7. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC MDAPI Modular Interface Separate “front end” from modular “engine” Same program or over a network or grid Dynamic discovery of engine capabilities, no limitations imposed by interface Front ends: NAMD 2, NAMD 3, Amber, CHARMM, VMD Engines: NAMD 2, NAMD 3, MINDY

8. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Terascale Biology and Resources PSC LeMieux Riken MDGRAPE NCSA Tungsten TeraGrid ASCI Purple Red Storm Thor’s Hammer CRAY X1

9. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC NAMD on Charm++ Active computer science collaboration (since 1992) Object array - A collection of chares, –with a single global name for the collection, and –each member addressed by an index –Mapping of element objects to processors handled by the system A[0]A[1]A[2]A[3]A[..] A[3]A[0] User’s view System view

10. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC NAMD3 Features Based on Charm++ Adaptive load balancing Optimized communication –Persistent Communication, Optimized concurrent multicast/reduction Flexible, tuned, parallel FFT libraries Automatic Checkpointing Ability to change the number of processors Scheduling on the grid Fault tolerance –Fully automated restart –Survive loss of a node Scaling to large machines –fine-grained parallelism for PME: bonded and nonbonded force evaluations

11. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Efficient Parallelization for IMD Characteristics –Limited parallelism on small systems –Real time response needed Fine grained parallelization –Improve speedups on 4K-30K atom systems –Time/step goal Currently 0.2s/step for BrH on single processor (P4 1.7GHz) Targeting on 0.003s/step on 64 processors of faster machine, that is 20picosecond/minute Flexible use of clusters –Migrating jobs (shrink/expand) –Better utilization when machine is idle

12. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Integration with CHARMM/Amber? Goal: NAMD as parallel simulation engine for CHARMM/Amber Generate input files in CHARMM/Amber –NAMD must read native file formats Run with NAMD on parallel computer –Need to use equivalent algorithms Analyze simulation in CHARMM/Amber –NAMD must generate native file formats

13. NIH Resource for Biomolecular Modeling and Bioinformatics http://www.ks.uiuc.edu/ Beckman Institute, UIUC Proud of Programmers