This paper presents a strategy for efficiently rendering time-varying volume data on a distributed-memory parallel computer. Time-varying volume data take large storage space and visualizing them requires reading large files continuously or periodically throughout the course of the visualization process. Instead of using all the processors to collectively render one volume at a time, a pipelined rendering process is proposed by partitioning processors into groups to render multiple volumes concurrently. In this way, the overall rendering time may be greatly minimized because the pipelined rendering tasks are overlapped with the I/O required to load each volume into a group of processors; moreover, parallelization overhead may be reduced as a result of partitioning the processors. We modify an existing parallel volume renderer to exploit various levels of rendering parallelism and to study how the partitioning of processors may lead to optimum rendering performance. We find that two factors affecting the overall execution time are resource utilization efficiency and pipeline startup latency. The optimal partitioning configuration is the one that balances these two factors. Tests on Intel Paragon computers show that in general the optimal partitionings do exist for a given rendering task and result in 40-50% saving in overall rendering time.