This paper describes a general framework for out-of-core rendering and management of massive terrain surfaces. The two key components of this framework are: view-dependent refinement of the terrain mesh; and a simple scheme for organizing the terrain data to improve coherence and reduce the number of paging events from external storage to main memory. Similar to several previously proposed methods for viewdependent refinement, we recursively subdivide a triangle mesh defined over regularly gridded data using longest-edge bisection. As part of this single, per-frame refinement pass, we perform triangle stripping, view frustum culling, and smooth blending of geometry using geomorphing. Meanwhile, our refinement framework supports a large class of error metrics, is highly competitive in terms of rendering performance, and is surprisingly simple to implement. Independent

In this paper we introduce a new indexing scheme for progressive traversal and visualization of large regular grids. We demonstrate the potential of our approach by providing a tool that displays at interactive rates planar slices of scalar field data with very modest computing resources. We obtain unprecedented results both in terms of absolute performance and, more importantly, in terms of scalability. On a laptop computer we provide real time interaction with a 2048 3 grid (8 Giga-nodes) using only 20MB of memory. On an SGI Onyx we slice interactively an 8192 3 grid ( tera-nodes) using only 60MB of memory. The scheme relies simply on the determination of an appropriate reordering of the rectilinear grid data and a progressive construction of the output slice. The reordering minimizes the amount of I/O performed during the out-of-core computation. The progressive and asynchronous computation of the output provides flexible quality/speed tradeoffs and a timecritical and interruptible user interface. 1.

. In many important computational mechanics applications, the computation adapts dynamically during the simulation. Examples include adaptive mesh refinement, particle simulations and transient dynamics calculations. When running these kinds of simulations on a parallel computer, the work must be assigned to processors in a dynamic fashion to keep the computational load balanced. A number of approaches have been proposed for this dynamic load balancing problem. This paper reviews the major classes of algorithms, and discusses their relative merits on problems from computational mechanics. Shortcomings in the state-of-the-art are identified and suggestions are made for future research directions. Key words. dynamic load balancing, parallel computer, adaptive mesh refinement 1. Introduction. The efficient use of a parallel computer requires two, often competing, objectives to be achieved. First, the processors must be kept busy doing useful work. And second, the amount of interprocess...

Distributed implementations of dynamic adaptive mesh refinement techniques offer the potential for accurate solutions of physically realistic models of complex physical phenomena. However, configuring and managing the execution of these applications presents significant challenges in resource allocation, data-distribution and loadbalancing, communication and coordination, and runtime management. This paper presents the design and evaluation of the ARMaDA framework for adaptive applicationsensitive partitioning of dynamic structured adaptive mesh refinement applications. The ARMaDA framework has three components: application state characterization component, octant-partitioner mapping policy-base, and adaptive meta-partitioner component that dynamically selects and configures partitioning strategies at runtime based on current application state. Experimental results show that adaptive application-sensitive partitioning using the ARMaDA framework can improve application performance as compared to non-adaptive partitioning.

this document reect the shift towards Java as a primary focus of investigation. At least three of the partners are now engaged in development of parallel or optimizing Java compilers

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We perform a comprehensive performance characterization of load balancing algorithms for parallel structured adaptive mesh refinement (SAMR) applications. Using SAMR, computational resources are dynamically concentrated to areas in need of a high accuracy. Because of the dynamic resource allocation, the workload must repeatedly be partitioned and distributed over the processors. For an efficient parallel SAMR implementation, the partitioning algorithm must be dynamically selected at run-time with regard to both the application and computer state. We characterize and compare a common partitioning algorithm and a large number of alternative partitioning algorithms. The results prove the viability of dynamic algorithm selection and show the benefits of using a large number of complementing partitioning algorithms. 1

Parallel implementations of scientific applications involving the simulation of reactive flow on structured grids are challenging, since the underlying phenomena include transport processes with uniform computational loads as well as reactive processes having pointwise varying workloads. As a result, traditional parallelization approaches that assume homogeneous loads are not suitable for these simulations. This paper presents “Dispatch”, a dynamic structured partitioning strategy that has been applied to parallel uniform and adaptive formulations of simulations with computational heterogeneity. Dispatch maintains the computational weights associated with pointwise processes in a distributed manner, computes the local workloads and partitioning thresholds, and performs in-situ localitypreserving load balancing. The experimental evaluation of Dispatch using an illustrative 2-D reactive-diffusion kernel demonstrates improvement in load distribution and overall application performance.

Computer simulations that solve partial differential equations (PDEs) are common in many fields of science and engineering. To decrease the execution time of the simulations, the PDEs can be solved on parallel computers. For efficient parallel implementations, the characteristics of both the hardware and the PDE solver must be taken into account. In this thesis, we explore two ways to increase the efficiency of parallel PDE solvers. First, we use full-system simulation of a parallel computer to get detailed knowledge about cache memory usage for three parallel PDE solvers. The results reveal cases of bad cache memory locality. This insight can be used to improve the performance of the PDE solvers. Second, we study the adaptive mesh refinement (AMR) partitioning problem. Using AMR, computational resources are dynamically concentrated to areas in need of a high accuracy. Because of the dynamic

this paper I introduce a new static indexing scheme that induces a data layout satisfying both requirements (i) and (ii) for the hierarchical traversal of n-dimensional regular grids. In one particular implementation the scheme exploits in a new way the recursive construction of the Z-order space filling curve. The standard indexing that maps the input D data onto a 1D sequence for the Z-order curve is based on a simple bit interleaving operation that merges the input indices into one index n times longer. This helps in grouping the data for geometric proximity but only for a specific level of detail. In this paper I show how this indexing can be transformed into an alternative index that allows to group the data per level of resolution first and then the data within each level per geometric proximity. This yields a data layout that is appropriate for hierarchical out-of-core processing of large grids