Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships. However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data. These distortions occur both at the global inter-cluster and the local intra-cluster levels. The former leads to misinterpretation of the distances between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance. The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance. In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design. Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots. We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors. Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D statistics to yield what we call Cluster Appearance Glyphs. We demonstrate our framework with a dataset acquired to analyze program execution times in file systems.

VOXEL DATA MANAGEMENT AND ANALYSIS IN POSTGRESQL/POSTGIS UNDER DIFFERENT DATA LAYOUTS

Three-dimensional (3D) raster data (also named voxel) is important sources for 3D geo-information applications, which have long been used for modelling continuous phenomena such as geological and medical objects. Our world can be represented in voxels by gridding the 3D space and specifying what each grid represents by attaching every voxel to a real-world object. Nature-triggered disasters can also be modelled in volumetric representation. Unlike point cloud, it is still a lack of wide research on how to efficiently store and manage such semantic 3D raster data. In this work, we would like to investigate four different data layouts for voxel management in open-source (spatial) DBMS - PostgreSQL/PostGIS, which is suitable for efficiently retrieving and quick querying. Besides, a benchmark has been developed to compare various voxel data management solutions concerning functionality and performance. The main test dataset is the groups of buildings of UNSW Kensington Campus, with 10cm resolution. The obtained storage and query results suggest that the presented approach can be successfully used to handle voxel management, semantic and range queries on large voxel dataset.

Performance and Energy Assessment of a Lattice Boltzmann Method Based Application on the Skylake Processor

This paper presents the performance analysis for both the computing performance and the energy efficiency of a Lattice Boltzmann Method (LBM) based application, used to simulate three-dimensional multicomponent turbulent systems on massively parallel architectures for high-performance computing. Extending results reported in previous works, the analysis is meant to demonstrate the impact of using optimized data layouts designed for LBM based applications on high-end computer platforms. A particular focus is given to the Intel Skylake processor and to compare the target architecture with other models of the Intel processor family. We introduce the main motivations of the presented work as well as the relevance of its scientific application. We analyse the measured performances of the implemented data layouts on the Skylake processor while scaling the number of threads per socket. We compare the results obtained on several CPU generations of the Intel processor family and we make an analysis of energy efficiency on the Skylake processor compared with the Intel Xeon Phi processor, finally adding our interpretation of the presented results.