Parallel Molecular Dynamics Application Oriented Computer Science Research Laxmikant Kale

Outline What is needed for HPC to succeed? Parallelization of Molecular Dynamics –Aggressive Parallel decomposition –Load Balancing and performance –Multiparadigm programming Collaborative Interdisciplinary Research –Comments and lessons

Contributors PI s : –Laxmikant Kale, Klaus Schulten, Robert Skeel NAMD 1: –Robert Brunner, Andrew Dalke, Attila Gursoy, Bill Humphrey, Mark Nelson NAMD2: –M. Bhandarkar, R. Brunner, A. Gursoy, J. Philips, N.Krawetz, A. Shinozaki, K. Varadarajan,

Parallel Computing Research Trends: –application centered CS research –Isolated CS research Drawback of both Needed: – Computer Science centered, yet application oriented research

Middle layers Applications Parallel Machines “Middle Layers”: Languages, Tools, Libraries

Molecular Dynamics Collection of [charged] atoms, with bonds Newtonian mechanics At each time-step –Calculate forces on each atom bonds: non-bonded: electrostatic and van der Waal’s –Calculate velocities and Advance positions 1 femtosecond time-step, millions needed! Thousands of atoms (1, ,000)

Further MD Use of cut-off radius to reduce work – Å –Faraway charges ignored! % work is non-bonded force computations Some simulations need faraway contributions

Scalability The Program should scale up to use a large number of processors. –But what does that mean? An individual simulation isn’t truly scalable Better definition of scalability: –If I double the number of processors, I should be able to retain parallel efficiency by increasing the problem size

Isoefficiency Quantify scalability How much increase in problem size is needed to retain the same efficiency on a larger machine? Efficiency : Seq. Time/ (P · Parallel Time) –parallel time = computation + communication + idle

Traditional Approaches Replicated Data: –All atom coordinates stored on each processor –Non-bonded Forces distributed evenly –Analysis: Assume N atoms, P processors Computation: O(N/P) Communication: O(N log P) Communication/Computation ratio: P log P Fraction of communication increases with number of processors, independent of problem size!

Atom decomposition Partition the Atoms array across processors –Nearby atoms may not be on the same processor –Communication: O(N) per processor –Communication/Computation: O(P)

Force Decomposition Distribute force matrix to processors –Matrix is sparse, non uniform –Each processor has one block –Communication: N/sqrt(P) –Ratio: sqrt(P) Better scalability (can use 100+ processors) –Hwang, Saltz, et al: –6% on 32 Pes 36% on 128 processor

Spatial Decomposition Allocate close-by atoms to the same processor Three variations possible: –Partitioning into P boxes, 1 per processor Good scalability, but hard to implement –Partitioning into fixed size boxes, each a little larger than the cutoff disctance –Partitioning into smaller boxes Communication: O(N/P)

Spatial Decomposition in NAMD NAMD 1 used spatial decomposition Good theoretical isoefficiency, but for a fixed size system, load balancing problems For midsize systems, got good speedups up to 16 processors…. Use the symmetry of Newton’s 3rd law to facilitate load balancing

Spatial Decomposition

FD + SD Now, we have many more objects to load balance: –Each diamond can be assigned to any processor – Number of diamonds (3D): –14·Number of Patches

Bond Forces Multiple types of forces: –Bonds(2), Angles(3), Dihedrals (4),.. –Luckily, each involves atoms in neighboring patches only Straightforward implementation: –Send message to all neighbors, –receive forces from them –26*2 messages per patch!

Bonded Forces: Assume one patch per processor B CA

Implementation Multiple Objects per processor –Different types: patches, pairwise forces, bonded forces, –Each may have its data ready at different times –Need ability to map and remap them –Need prioritized scheduling Charm++ supports all of these

Charm++ Data Driven Objects Object Groups: –global object with a “representative” on each PE Asynchronous method invocation Prioritized scheduling Mature, robust, portable

Data driven execution Scheduler Message Q

Load Balancing Is a major challenge for this application –especially for a large number of processors Unpredictable workloads –Each diamond (force object) and patch encapsulate variable amount of work –Static estimates are inaccurate Measurement based Load Balancing –Very slow variations across timesteps

Bipartite graph balancing Background load: –Patches and angle forces Migratable load: –Non-bonded forces Bipartite communication graph –between migratable and non-migratable objects Challenge: –Balance Load while minimizing communication

Load balancing Collect timing data for several cycles Run heuristic load balancer –Several alternative ones Re-map and migrate objects accordingly –Registration mechanisms facilitate migration Needs a separate talk!

Before and After

Performance: size of system

Performance: various machines

Speedup

Multi-paradigm programming Long-range electrostatic interactions –Some simulations require this –Contributions of faraway atoms can be calculated infrequently –PVM based library, DPMTA developed at Duke by John Board et al Patch life cycle Better expressed as a thread

Converse Supports multi-paradigm programming Provides portability Makes it easy to implement RTS for new paradigms Several languages/libraries: –Charm++, threaded MPI, PVM, Java, md-perl, pc++, Nexus, Path, Cid, CC++, DP, Agents,..

Namd2 with Converse

NAMD2 In production use –Internally for about a year –Several simulations completed/published Fastest MD program? We think so Modifiable/extensible –Steered MD –Free energy calculations

Lessons for CSE Technical lessons –Multiple-domain (patch) decomposition provides necessary flexibility –Data driven objects and threads is a great combo –Measurement based load balancing is better –Multi-paradigm parallel programming works! Integrate independently developed libraries Use appropriate paradigm for each component

Real Application? Drawbacks –Need to spend effort on mundane details not germane to CS research –Production program: complicates structure

Real Application for CS research? Benefits –Subtle and complex research problems uncovered only with real application –Satisfaction of “real” concrete contribution –With careful planning, you can truly enrich the “middle layers” –Bring back a rich variety of relevant CS problems –Apply to other domains: Rockets? Casting?

Collaboration lessons Use conservative methods.. –C++: fashionable vs. conservative –Aggressive methods where they matter Account for differing priorities and objectives