There has been large amounts of research on image authentication method. Many of the schemes perform well in verification results; however, most of them are time-consuming in traditional serial manners. And improving the efficiency of authentication process has become one of the challenges in image authentication field today. In the future, it’s a trend that authentication system with the properties of high performance, real-time, flexible and ease for development. In this paper, we present a CUDA-based implementation of an image authentication algorithm with NVIDIA’s Tesla C1060 GPU devices. Comparing with the original implementation on CPU, our CUDA-based implementation works 20x-50x faster with single GPU device. And experiment shows that, by using two GPUs, the performance gains can be further improved around 1.2 times in contras to single GPU.

Most recent schema matching systems combine multiple components, each of which employs a particular matching technique with several knobs. The multi-component nature has brought a tuning problem, that is to determine which components to execute and how to adjust the knobs (e.g., thresholds, weights, etc.) of these components for domain users. In this paper, we present an approach to automatically tune schema matching systems using genetic algorithms. We match a given schema S against generated matching scenarios, for which the ground truth matches are known, and find a configuration that effectively improves the performance of matching S against real schemas. To search the huge space of configuration candidates efficiently, we adopt genetic algorithms (GAs) during the tuning process. To promote the performance of our approach, we implement parallel genetic algorithms on graphic processing units (GPUs) based on NVIDIA’s Compute Unified Device Architecture (CUDA). Experiments over four real-world domains with two main matching systems demonstrate that our approach provides more qualified matches over different domains.