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StreamMD Molecular Dynamics

StreamMD Molecular Dynamics. Eric Darve. MD of water molecules. Cutoff is used to truncate electrostatic potential Gridding technique: water molecules are grouped in clusters of size r cutoff . Problem: what data structure do we use to store this array of clusters?.

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StreamMD Molecular Dynamics

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  1. StreamMDMolecular Dynamics Eric Darve

  2. MD of water molecules • Cutoff is used to truncate electrostatic potential • Gridding technique: water molecules are grouped in clusters of size rcutoff. • Problem: what data structure do we use to store this array of clusters?

  3. Option: 3D array of streams. • Needs to be updated at each time step (or every 10 time steps) to account for molecules that enter or leave a cluster. • Q: how do I move data from one stream to another? • Q: how does the compiler take care of the load balancing? • Data locality defined by geometric distance between clusters, i.e.If two clusters are neighbors, they should be physically located in neighbor processors.

  4. Current implementation • After position has been updated: • Loop over all clusters C[i][j][k]: • We push in C[i][j][k] if water molcl is in same cluster. • We push in Ctmp otherwise. • Ctmp is then concatenated in a large stream Mol which contains all the molecules which have exited their cell. • Kernel is exec. to push molecules in Mol to C[][][].

  5. MD of proteins • Three possible types of potentials:Bond stretch: two atoms and 1 bond.Bond angle: three atoms and 2 bonds.Torsion angle: four atoms and 3 bonds. • Typical organization of the computation:loop over bond stretch, bond angle and torsion potentials. • Brook needs some information to map the data in an efficient manner.

  6. Information is static: connectivity between atoms does not change throughout the run. • However the information is not accessible to the compiler: run-time information. • Protein backbone: sequence of amino-acids. Typically 10-20 atoms each. • User can provide following information: for each atom, give residue number.

  7. “Distance” between two data can then be defined as the difference between residue number. • Run-time optimization approach:all atoms with same residue number are on the same processor.2 processors with nearby residue numbers should be physically close to each other.

  8. Load balancing • Typical size of protein: 10 residues for test proteins to hundreds for real life problem. • Size is relatively small but computationally very expensive.Well optimized load balancing and distribution of data is critical. • Example: Duan and Kollman ’98. 36-residue protein + 3000 water molecules.1/2 billion time steps: four months on 256-processor computer. Complete fold would have required: x 10, x 100.

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