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Next: 12.4.4 Acquiring Locally Essential Up: 12.4 Tree Codes for Previous: 12.4.2 Computing Forces

12.4.3 Parallelism in Tree Codes

Computational science advances both in hardware and algorithms. Occasionally, algorithmic advances are of such tremendous significance that they completely overshadow the striking advances constantly being made by hardware. Tree codes are just such an algorithmic advance. It is literally true that a tree code running on a modest workstation can address larger problems than can the fastest parallel supercomputer running an algorithm. It is well known [Fox:84e], [Fox:88a] that parallel computers can efficiently evaluate the force evaluations required by direct application of Equation 12.5. However, this fact is of limited significance now that a new class of algorithms has changed the underlying complexity of the problem. If parallel computers are to have an impact on the N-body problem, then they must be able to efficiently execute tree codes.

Parallelization of tree codes is a challenging problem. Typical astrophysical simulations are highly inhomogeneous. Spatial densities can vary by a factor of or more through the computational domain. The tree must be adaptive to deal with such a large dynamic range in densities, that is, it must be deep in regions of high particle density, and shallow in regions of low particle density. Furthermore, the structure of the inhomogeneities is often dynamic-for example, galaxies form, move, collide, and merge in cosmological simulations. A fixed tree and/or a fixed decomposition is not suitable for such a system. Despite these problems, it is possible to find parallelism in tree codes and to run them efficiently on large parallel computers [Fox:89t], [Salmon:90a], [Warren:92a;93a].

The technique of ``domain decomposition'' has been applied with excellent results to a number of other problem areas. We have found that a slightly abstracted concept of domain decomposition is also applicable to tree codes. Recall that a domain decomposition usually proceeds by ``assigning'' spatial domains to processors. In designing a parallel program, the precise meaning of ``assign'' is crucial. We adopt the following ``owner-computes'' definition of a domain: A domain is a rectangular region of simulation space. Assignment of a domain to a processor implies that the processor will be responsible for updating the positions and velocities of all particles located within that region of simulation space. We allow that processor domains might change from one time step to the next, based, presumably, on load-balancing considerations.

Processor domains are chosen using orthogonal recursive bisection,  or ORB (see Section 11.1.5). Recall that ORB tries repeatedly to split some measure of the ``load'' in half, and assign the halves to sets of processors. In the present context, that means finding a coordinate so that half of the computational ``load'' is associated with particles above the split, and half is associated with particles below the split. The result of applying orthogonal recursive bisection to a system containing two ``galaxies,'' (well-separated regions with high local particle density) is shown in Figure 12.13.

Figure 12.13: Decomposition Resulting from Orthogonal Recursive Bisection of a System with Two Galaxies

It is a simple matter to record the ``load'' associated with each particle. For example, one can count interactions, or one could simply read the clock before and after the force on the particle is computed. Then, in order to find the splitting coordinate, one simply executes a binary (or more sophisticated) search, seeking a value of the coordinate for which half of the per-particle work is above and half is below.

In fact, seeking the exact median coordinate of the per-particle work does not necessarily guarantee load balance.  It guarantees load balance within the force calculation, but it does not account for load imbalance that may result during construction of the tree, or during the other phases of the computation. It is possible to account for these sources of load imbalance by seeking a coordinate which is not precisely at the median (i.e., percentile), but rather at another percentile. The new target percentile is found by measuring the actual load imbalance, and adjusting the target by a small amount on each time step to reduce the observed load imbalance [Salmon:90a].

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