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This paper examines the problem of predicting job runtimes by exploiting the properties of parameter sweeps. A new parameter sweep prediction framework GIPSy (grid information prediction system) is introduced. Predictions are made based on prior runtime information and the parameters used to configure each job. The main objective is providing a tool combining development, simulation and application of prediction models within one framework. The different kinds of available sample selectors and models are discussed in detail. Results are presented for a quantum physics problem. A previously introduced scheduling technique and the implementation called PGS (prediction based grid scheduling) is improved and presented in combination with GIPSy to obtain a real-world grid implementation that optimizes the distribution of parameter sweeps.