Serial query optimization selects the query plan that is the least costly to execute. Since only one process executes the query, choosing the least costly plan yields the fastest response time and requires the least amount of total work from the server.
The goal of executing queries in parallel is to get the fastest response time, even if it involves more total work from the server. During parallel query optimization, the optimizer uses cost-based comparisons similar to those used in serial optimization to select a final query plan.
However, since multiple worker processes execute the query, a parallel query plan requires more total work from Adaptive Server. Multiple worker processes, engines, and partitions that improve the speed of a query require additional costs in overhead, CPU utilization, and disk access. In other words, serial query optimization improves performance by minimizing the use of server resources, but parallel query optimization improves performance for individual queries by fully utilizing available resources to get the fastest response time.