Efficient Generating And Processing Of Large-Scale Unstructured Meshes

Unstructured meshes are used in a variety of disciplines to represent simulations and experimental data. Scientists who want to increase accuracy of simulations by increasing resolution must also increase the size of the resulting dataset. However, generating and processing a extremely large unstructured meshes remains a barrier. Researchers have published many parallel Delaunay triangulation (DT) algorithms, often focusing on partitioning the initial mesh domain, so that each rectangular partition can be triangulated in parallel. However, the comproblems for this method is how to merge all triangulated partitions into a single domain-wide mesh or the significant cost for communication the sub-region borders. We devised a novel algorithm --Triangulation of Independent Partitions in Parallel (TIPP) to deal with very large DT problems without requiring inter-processor communication while still guaranteeing the Delaunay criteria. The core of the algorithm is to find a set of independent} partitions such that the circumcircles of triangles in one partition do not enclose any vertex in other partitions. For this reason, this set of independent partitions can be triangulated in parallel without affecting each other. The results of mesh generation is the large unstructured meshes including vertex index and vertex coordinate files which introduce a new challenge \-- locality. Partitioning unstructured meshes to improve locality is a key part of our own approach. Elements that were widely scattered in the original dataset are grouped together, speeding data access. For further improve unstructured mesh partitioning, we also described our new approach. Direct Load which mitigates the challenges of unstructured meshes by maximizing the proportion of useful data retrieved during each read from disk, which in turn reduces the total number of read operations, boosting performance

MFA-DVR: Direct Volume Rendering of MFA Models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community

Progress on H5Part: A Portable High Performance Parallel DataInterface for Electromagnetics Simulations

Significant problems facing all experimental andcomputationalsciences arise from growing data size and complexity. Commonto allthese problems is the need to perform efficient data I/O ondiversecomputer architectures. In our scientific application, thelargestparallel particle simulations generate vast quantitiesofsix-dimensional data. Such a simulation run produces data foranaggregate data size up to several TB per run. Motived by the needtoaddress data I/O and access challenges, we have implemented H5Part,anopen source data I/O API that simplifies the use of the HierarchicalDataFormat v5 library (HDF5). HDF5 is an industry standard forhighperformance, cross-platform data storage and retrieval that runsonall contemporary architectures from large parallel supercomputerstolaptops. H5Part, which is oriented to the needs of the particlephysicsand cosmology communities, provides support for parallelstorage andretrieval of particles, structured and in the future unstructuredmeshes.In this paper, we describe recent work focusing on I/O supportforparticles and structured meshes and provide data showing performance onmodernsupercomputer architectures like the IBM POWER 5