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The Distributed Data Interface in GAMESS

The Distributed Data Interface in GAMESS. Brett M. Bode, Michael W. Schmidt, Graham D. Fletcher, and Mark S. Gordon Ames Laboratory-USDOE, Iowa State University. 10/7/99. What is GAMESS?. G eneral A tomic and M olecular E lectronic S tructure S ystem.

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The Distributed Data Interface in GAMESS

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  1. The Distributed Data Interface in GAMESS Brett M. Bode, Michael W. Schmidt, Graham D. Fletcher, and Mark S. Gordon Ames Laboratory-USDOE, Iowa State University 10/7/99

  2. What is GAMESS? • General Atomic and Molecular Electronic Structure System • First principles - fully quantum mechanical • Created from other programs in ~1980 • Developed by Dr. Mark Gordon’s research group since 1982 with Dr. Michael Schmidt as the principle developer. • Parallelization begin in 1991 • Emphasis on Distributed memory systems • Currently includes methods for treating 1-atom to several hundred atoms

  3. Partial list of capabilities C = Uses disk storage D = Minimal disk usage P = Parallel execution

  4. First Generation ParallelCode • Parallel communications were performed using either: • TCGMSG • Vendor supplied MPI • Parallel version was usually a slightly modified version of the sequential code

  5. IBM-SUR cluster • 22 IBM RS/6000 43P-260: • Dual 200MHz Power3 CPUs • 4 Mb of Level 2 cache • 1 GByte of RAM • 18 GBytes fast local disks • Jumbo Frames Gig Ethernet • Integrated Fast-Ethernet • Fast Ethernet Switch to all • 3x9 port Gigabit Switches

  6. Gigabit Performance on theIBM 43P-260 Cluster

  7. Test Molecule • Ti(C5H5)2 C2H4SiHCl3 • Basis Set • 6-31G(d,p) on C and H. • SBKJC ECP on Si, Ti, and Cl extended with 1 d-type polarization function on Si and Cl. • 345 total basis functions

  8. Parallel SCF • Very good scaling dependant on the size of the molecule. • Large systems show nearly linear scaling through 256 nodes

  9. SCF methods scale very well Most methods run in parallel Good use is made of aggregate CPU and disk resources. MP2 and MCSCF methods scale to only a few (8-32) nodes The aggregate memory is not utilized so jobs are still limited by the memory size of one node. Successes and Limitations

  10. Second Generation Methods • New methods should take advantage of the aggregate memory of a parallel system • Implies a higher communication demands • Many to many messaging profile • Methods should scale to hundreds of nodes (at least) • Demanding local storage needs

  11. The Distributed Data Interface (DDI) DDI provides the core functions needed to treat a portion of the memory on each node as part of a global shared array.

  12. Runs on top of: MPI (MPI-2 preferred) TCP/IP sockets Lightweight - Provides only the functionality needed by GAMESS Is not intended as a general purpose library. Does optimize for mixed SMP and distributed memory systems DDI

  13. New MP2 implementation • Uses DDI to utilize the aggregate memory of the parallel machine at the expense of communications • Trades some symmetry in the MP2 equations for better parallel scalability • Requires more memory than the sequential version • Is slower than the sequential version on 1 CPU

  14. MP2 Scalability

  15. Conclusions • DDI provides a scalable way of taking advantage of the global memory of a parallel system • The new MP2 code demonstrates code written specifically for parallel execution without replacing the sequential version.

  16. Future Work • DDI needs further work to enhance the features and increase robustness, or possibly needs to be replaced with a more general library such as the GA tools from PNNL. • The global shared memory approach is being applied to many other parts of GAMESS to increase scalability.

  17. Thanks! • David Halstead • Guy Helmer For $: • IBM Corp. for an SUR grant (of 15 Workstations) • DOE MICS program (interconnects and 7 workstations) • Air Force OSR (long term dev. Funding) • DOD CHSSI program (improved parallelization)

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