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This paper describes new load balancing algorithms for parallel database processing on shared memory multiprocessors. The goal of load balancing is to reduce overhead as well as load imbalance, but there is a tradeoff between them in ordinary algorithms. Unfortunately, optimum performance can hardly be obtained using ordinary algorithms because their performances depend on several factors such as database size, the number of processors and data distribution. The proposed algorithms solve these problems by varying the number of tasks allocated at a time (which was fixed in ordinary algorithms) according to the number of remaining tasks and the maximum and minimum processing times of a task. Performance evaluations show that the proposed algorithms achieve fair load balancing with lower overhead independent of the above factors.