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6.3.3 Parallel Implementation and Performance

The fairly large three-dimensional lattices (usually ) are partitioned into a ring of M processors with x-dimension which is uniformly distributed among the M processors. The local updates are easily parallelized since the connection is, at most, next-nearest neighbor (for the time-loop update). The needed spin-word arrays from its neighbor are copied into the local storage by the shift routine in the CrOS communication system [Fox:88a] before doing the update. One of the global updates, the time line, can also be done in the same fashion. The communication is very efficient in the sense that a single communication shift, , spins instead of Nt spins in the case where the lattice is partitioned into a two-dimensional grid. The overhead/latency associated with the communication is thus significantly reduced.

The winding-line global update along the x-direction is difficult to do in this fashion, because it involves spins on all the M nodes. In addition, we need to compute the correlation functions which have the same difficulty. However, since these operations are not used very often, we devised a fairly elegant way to parallelize these global operations. A set of gather-scatter routines, based on the cread and cwrite in CrOS, is written. In gather, the subspaces on each node are gathered into complete spaces on each node, preserving the original geometric connection. Parallelism is achieved now since the global operations are done on each node just as in the sequential computer, with each node only doing the part it originally covers. In scatter, the updated (changed) lattice configuration on a particular node (number zero) is scattered (distributed) back to all the nodes in the ring, exactly according to the original partition. Note that this scheme differs from the earlier decomposition scheme [Fox:84a] for the gravitation problem, where memory size constraint is the main concern.

The hypercube nodes were divided into several independent rings, each ring holding an independent simulation, as shown in Figure 6.8. At higher temperatures, a spin system of is enough, so that we can simulate several independent systems at the same time. At low temperatures, one needs larger systems, such as -all the nodes will then be dedicated to a single large system. This simple parallelism makes the simulation very flexible and efficient. In the simulation, we used a parallel version of the Fibonacci additive random numbers  generator [Ding:88d], which has a period larger that .

Figure 6.8: The Configuration of the Hypercube Nodes. In the example, 32 nodes are configured as four independent rings, each consisting of 8 nodes. Each ring does an independent simulation.

We have made a systematic performance analysis  by running the code on different sizes and different numbers of nodes. The timing results for a realistic situation (20 sweeps of update, one measurement) are measured [Ding:90k]. The speedup, /, where () is the time for the same size spins system to run same number operations on one node, is plotted in Figure 6.9. One can see that speedup is quite close to the ideal case, denoted by the dashed line. For the quantum spin system, the 32-node hypercube speeds up the computation by a factor of 26.6, which is a very good result. However, running the same spin system on a 16-node is more efficient, because we can run two independent systems on the 32-node hypercube with a total speedup of (each speedup a factor 14.5). This is better described by efficiency, defined as speedup/nodes, which is plotted in Figure 6.10. Clearly, the efficiency of the implementation is very high, generally over 90%.

Figure 6.9: Speedup of the Parallel Algorithm for Lattice Systems , and . The dashed line is the ideal case.

Figure 6.10: Efficiency of the Parallel Algorithm

Comparison with other supercomputers is interesting. For this program, the one-head CRAY X-MP  speed is approximately that of a 2-node Mark IIIfp. This indicates that our 32-node Mark IIIfp  performs better than the CRAY X-MP by about a factor of % = 14! We note that our code is written in C and the vectorization is limited to the 32-bit inside the words. Rewriting the code in Fortran (Fortran compilers on the CRAY are more efficient) and fully vectorizing the code, one may gain a factor of about three on the CRAY. Nevertheless, this quantum Monte Carlo code is clearly a good example, in that parallel computers easily (i.e., at same programming level) outperform the conventional supercomputers.

next up previous contents index
Next: 6.3.4 Physics Results Up: Magnetism in the Previous: 6.3.2 The Computational Algorithm

Guy Robinson
Wed Mar 1 10:19:35 EST 1995