Visual exploration of data with multithread MIC computer architectures

Abstract

Knowledge mining from immense datasets requires fast, reliable and affordable tools for their visual and interactive exploration. Multidimensional scaling (MDS) is a good candidate for embedding of high-dimensional data into visually perceived 2-D and 3-D spaces. We focus here on the way to increase the computational performance of MDS in the context of interactive, hierarchical, visualization of big data. To this end we propose a parallel implementation of MDS on the modern Intel Many Integrated Core Architecture (MIC). We compare the timings obtained for MIC architecture to GPU and standard multi-core CPU implementations of MDS. We conclude that despite 30-40% lower computational performance comparing to GPU/CUDA tuned MDS codes, the MIC solution is still competitive due to dramatically shorter code production and tuning time. The integration of MIC with CPU will make this architecture very competitive with more volatile on technological changes GPU solutions.