Distributed memory

This completes the outline of FAMUSAMM. The algorithm has been implemented in the MD simulation program EGO VIII [48] in a sequential and a parallelized version the latter has been implemented and tested on a number of distributed memory parallel computers, e.g., IBM SP2, Cray T3E, Parsytec CC and ethernet-linked workstation clusters running PVM or MPI. 

The complexity analysis shows that the load is evenly balanced among processors and therefore we should expect speedup close to P and efficiency close to 100%. There are however few extra terms in the expression of the time complexity (first order terms in TV), that exist because of the need to compute the next available row in the force matrix. These row allocations can be computed ahead of time and this overhead can be minimized. This is done in the next algorithm. Note that, the communication complexity is the worst case for all interconnection topologies, since simple broadcast and gather on distributed memory parallel systems are assumed. 

D. Brown, J. H. R. Clarke, M. Okuda and T. Yamazaki, A domain decomposition strategy for molecular dynamics simulations on distributed memory machines . Comp. Phys. Comm., Vol 74, 67-80, 1993. 

Y. Hwang, R. Das, F. H. Saltz, M. Hadoscek and B. R. Brooks, Parallelizing molecular dynamics programs for distributed-memory machines , IEEE Computational Science and Engineering, Vol 2, no 2, 18-29, 1995. 

M. Rame and M. Delshad. A compositional reservoir simulator on distributed memory parallel computers. In Proceedings Volume, pages 89-100. 13th SPE Reservoir Simulation Symp (San Antonio, TX, 2/12-2/15), 1995. 

Bernardo DN, Ding YB, Kroghjespersen K, Levy RM (1995) Evaluating polarizable potentials on distributed-memory parallel computers - program-development and applications. J Comput Chem 16(9) 1141-1152... 

Kanerva, R (1990) Sparse distributed memory. MIT Press, Cambridge, MA, pp. 26-27. 

J. Nieplocha, R.J. Harrison and R.J. Littlefield, Global arrays A portable "shared-memory" programming model for distributed memory computers, in Supercomputing 94 (Washington D.C., 1994). 

Beckers JVL, Lowe CP, DeLee SW (1998) An iterative PPPM method for simulating Coulombic systems on distributed memory parallel computers, Mol Simul, 20 369—383... 

Message passing, collective communication, and similar alternatives for programming software libraries for large-scale applications on distributed-memory computer systems. 

The four-index transformation is a good test case for parallel algorithm development of electronic structure calculations, because it has O(N ) operations, a low computation to data transfer ratio and is a compact piece of code. Distributed-memory algorithms were presented for a number of standard QC methods by Whiteside and co-workers Li52 special emphasis on the integral transformation. Details of their implementation on a 32-processor Intel hypercube were provided. 

Rendell et al. compared three previously reported algorithms to the fourth-order triple excitation energy component in MBPT." The authors investigated the implementation of these algorithms on current Intel distributed-memory parallel computers. The algorithms had been developed for shared-... 

Das and Saltz developed a fully distributed data parallel version of CHARMM. 1 8 jhe implementation used Saltz and co-workers PARTI (Parallel Automated Runtime Toolkit at ICASE) primitives, These primitives allow the distribution of arrays across the local memories of multiple nodes and provide global addressing of these arrays even on truly distributed memory machines. Whereas the replicated-data version of Brooks and Hodosceki ... 

Sato, Tanaka, and Yao also reported a parallel implementation of the AMBER MD module.The target machine, the APIOOO distributed-memory parallel computer developed at Fujitsu, consisted of up to 1024 processor elements connected with three different networks. To obtain a higher degree of parallelism and better load balance between processors, a particle division method was developed to randomly allocate particles to processors. Experiments showed that a problem with 41,095 atoms can be processed 226 times faster with a 512-processor APIOOO than by a single processor. 

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