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The scheduling and execution for grid application is an important problem in the grid environment. To get the high reliability and efficiency, we propose a runtime reputation based grid resource selection algorithm. According to the accumulated raw score, the runtime reputation degree for a grid resource is quantified as an evaluating score in the runtime of an application. Instead of being dependent on the historical experiences, it is dynamically adaptive to the runtime availability, load, and performance of the grid resources. The execution framework on the grid is based on Globus Toolkit and Swift system. In a real production grid, Open Science Grid (OSG), a typical grid application with large scale independent jobs was experimented, which was based on BLAST application. The experimental results for the performance of different policies are presented, with a benchmarking workload size of 10,000 jobs. The runtime reputation and behavior statistics for the grid resources are also presented.