Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our compute kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager-worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a 10-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198s using 128 nodes of Piz Daint. This translates to an overall performance of 352TFLOPS. On the node-level, we provide results for two GPUs, Nvidia's Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all compute kernels and devices, demonstrating the performance portability of our approach.

Abstract: This research aims at developing a virtual reality destination experience (VRDE) model as a moderating variable between sustainable tourist behavior (STB) and tourist visit intention (TVI). The portability variable strengthens VRDE, and the content quality variable supports VRDE. The research method used was purposive convenience sampling covering 204 respondents who visited the vicinity of Kebon batik village in Klaten, Central Java, Indonesia. The research findings were H1: STB has a significant effect on VRDE, H2: VDRE has an impact on TRVI, H3: portability has a significant effect on VRDE, and H4: content quality did not have an impact on VRDE. The novelty of this research is the VRDE application applied to Community Sustainable Tourist Behavior in developing countries.