Abstract Throughout much of the parallel processing community there is the sense that writing software for distributed‐memory parallel processors Is subject to a ‘no pain—no gain’ rule: that In order to reap the benefits of parallel computation one must first suffer the pain of converting the application to run on a parallel machine. We believe this Is the result of Inadequate programming tools and not a problem Inherent to parallel processing. We will show that one can parallelize real scientific applications and obtain good performance with little effort If the right tools are used. Our vehicle for this demonstration is a 6000‐line DNA and protein sequence comparison application that we have implemented in Mental, an object‐oriented parallel processing system for both parallel and distributed architectures. We briefly describe the application and present performance information for both the Mentat version and a hand‐coded parallel version of the application.

Throughout much of the parallel processing community there is the sense that writing software for distributed memory parallel processors is subject to a 'no pain-no gain' rule: that in order to reap the benefits of parallel computation one must first suffer the pain of converting the application to run on a parallel machine. The authors believe this is the result of inadequate programming tools and not a problem inherent to parallel processing. They show that one can parallelize real scientific applications and obtain good performance with little effort if the right tools are used. Their vehicle for this demonstration is a six-thousand line DNA and protein sequence comparison application that they have implemented in Mentat, an object-oriented parallel processing system for both parallel and distributed architectures. They briefly describe the application and present performance information for both the Mentat version and a hand-coded parallel version of the application.>