In this paper, we study the scheduling and optimization problems of parallel query processing using interoperation parallelism in a shared-memory environment and propose our solutions for XPRS. we frist study the scheduling problem of a set or a continous sequence of independent tasks that are either from a bushy tree plan of a single query or from the plans of multiple queries, and present a clean and simple scheduling algorithm. our scheduling algorithm achieves maximum resource utilizations by running an IO-bound task and a CPU-bound task in parallel with carefully calculated degrees of parallelism and maintains the maximum resource utilizations by dynamically adjusting the degress of paralelism of running tasks whenever necessary. real per-formance figures are shown to confrim the effectiveness of our swcheduling algorithm. we also revisit the optimization problem of parallel execution plans of a single query and extend our previous results to consider inter-operation parallelism by introducing a new cost estimation method to the query optimizer based on our scheduling algorithm.