Load Balancing in Parallel Computers: Theory and Practice  is about the essential software technique of load balancing in  distributed memory message-passing parallel computers, also called  multicomputers. Each processor has its own address space and has to  communicate with other processors by message passing. In general, a  direct, point-to-point interconnection network is used for the  communications. Many commercial parallel computers are of this class,  including the Intel Paragon, the Thinking Machine CM-5, and the IBM  SP2.   Load Balancing in Parallel Computers: Theory and Practice  presents a comprehensive treatment of the subject using rigorous  mathematical analyses and practical implementations. The focus is on  nearest-neighbor load balancing methods in which every processor at  every step is restricted to balancing its workload with its direct  neighbours only. Nearest-neighbor methods are iterative in nature  because a global balanced state can be reached through processors'  successive local operations. Since nearest-neighbor methods have a  relatively relaxed requirement for the spread of local load  information across the system, they are flexible in terms of allowing  one to control the balancing quality, effective for preserving  communication locality, and can be easily scaled in parallel computers  with a direct communication network.   Load Balancing in Parallel Computers: Theory and Practice serves  as an excellent reference source and may be used as a text for  advanced courses on the subject.

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