The rising pressure for simultaneously improving performance and reducing power is driving more diversity into all aspects of computing devices. An algorithm that is wellmatched to the target hardware can run multiple times faster and more energy efficiently than one that is not. The problem is complicated by the fact that a program's input also affects the appropriate choice of algorithm. As a result, software developers have been faced with the challenge of determining the appropriate algorithm for each potential combination of target device and data. This paper presents DySel, a novel runtime system for automating such determination for kernel-based data parallel programming models such as OpenCL, CUDA, OpenACC, and C++AMP. These programming models cover many applications that demand high performance in mobile, cloud and high-performance computing. DySel systematically deploys candidate kernels on a small portion of the actual data to determine which achieves the best performance for the hardware-data combination. The test-deployment, referred to as micro-profiling, contributes to the final execution result and incurs less than 8% of overhead in the worst observed case when compared to an oracle. We show four major use cases where DySel provides significantly more consistent performance without tedious effort from the developer.