GPU computing can significantly improve performance by taking advantage of massive parallelism of GPUs for data parallel applications. Computation in visualization applications is suitable for parallelization on the GPU, which can improve performance and interactivity in these applications. If used effectively, multiple GPUs can lead to a significant speedup over a single GPU. However, the use of multiple GPUs requires memory management, scheduling, and load balancing to ensure that a program takes full advantage of available processors. This work presents methods for data-driven and dynamic multi-GPU load balancing using a pipelined approach and a framework for use with different applications. Data-driven load balancing can improve utilization for applications by taking into account past performance for different combinations of input parameters. The dynamic load balancing method based on buffer fullness can adjust to workload changes at runtime to gain an additional performance improvement. This work provides a framework for load balancing to account for differing characteristics of applications. Implementation of a multi-GPU data structure allows for use of these load balancing methods in the framework. The effectiveness of the framework is demonstrated with performance results from interactive visualization that shows a significant speedup due to load balancing. / Master of Science