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This paper presents PPerfGrid, a tool that addresses the challenges involved in the exchange of heterogeneous parallel computing performance data. Parallel computing performance data exists in a wide variety of different schemas and formats, from basic text files to relational databases to XML, and it is stored on geographically dispersed host systems of various platforms. PPerfGrid uses grid services to address these challenges. PPerfGrid exposes application and execution semantic objects as grid services and publishes their location and PPerfGrid clients access this registry, locate the PPerfGrid sites with performance data they are interested in, and bind to a set of grid services that represent this data. This set of application and execution grid services provides a uniform, virtual view of the data available in a particular PPerfGrid session. PPerfGrid addresses scalability by allowing specific questions to be asked about a data store, thereby narrowing the scope of the data returned to a client. In addition, by using a grid services approach, the application and execution grid services involved in a particular query can be dynamically distributed across several hosts, thereby taking advantage of parallelism and improving scalability. We describe our PPerfGrid prototype and include data from preliminary prototype performance tests.