Nearest-neighbor algorithms for load-balancing in parallel computers

With nearest neighbor load balancing algorithms, a processor makes balancing decisions based on localized workload information and manages workload migrations within its neighborhood. This paper compares a couple of fairly well-known nearest neighbor algorithms , the dimension-exchange (DE, for short) and the diiusion (DF, for short) methods and their several variants|the average dimension-exchange (ADE), the optimally-tuned dimension-exchange (ODE), the local average diiusion (ADF) and the optimally-tuned diiusion (ODF). The measures of interest are their eeciency in driving any initial work-load distribution to a uniform distribution and their ability in controlling the growth of the variance among the processors' workloads. The comparison is made with respect to both one-port and all-port communication architectures and in consideration of various implementation strategies including synchronous/asynchronous invocation policies and static/dynamic random workload behaviors. It turns out that the dimension-exchange method outperforms the diiusion method in the one-port communication model. In particular, the ODE algorithm leads itself to best suited for statically synchronous implementations of a load balancing process regardless of its underlying communication models. The strength of the diiusion method is in asynchronous implementations in the all-port communication model; the ODF algorithm performs best in that case. The underlying communication networks considered assume the most popular topologies, the mesh and the torus and their special cases: the hypercube and the k-ary n-cube.