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MPIPOV: a Parallel Implementation of POVRay Based on MPI
 in: Proc. Euro PVM/MPIâ€™99, Lecture Notes in Computer Science
, 1999
"... . The work presents an MPI parallel implementation of PovRay, a powerful public domain ray tracing engine. The major problem in ray tracing is the large amount of CPU time needed for the elaboration of the image. With this parallel version it is possible to reduce the computation time or to rend ..."
Abstract

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. The work presents an MPI parallel implementation of PovRay, a powerful public domain ray tracing engine. The major problem in ray tracing is the large amount of CPU time needed for the elaboration of the image. With this parallel version it is possible to reduce the computation time or to render, with the same elaboration time, more complex or detailed images. The program was tested successfully on ParMa2, a lowcost cluster of personal computers running Linux operating system. The results are compared with those obtained with a commercial multiprocessor machine, a Silicon Graphics Onyx2 parallel processing system based on an Origin CCNUMA architecture. 1 Introduction The purpose of this work is the implementation of a distributed version of the original code of PovRay [1], that is a well known public domain program for ray tracing. The parallelization of this algorithm involves many problems that are typical of the parallel computation. The ray tracing process is very c...
Tuning of Algorithms for Independent Task Placement in the . . .
 EUROGRAPHICS SYMPOSIUM ON PARALLEL GRAPHICS AND VISUALIZATION (2004)
, 2004
"... This paper investigates assignment strategies (load balancing algorithms) for process farms which solve the problem of online placement of a constant number of independent tasks with given, but unknown, time complexities onto a homogeneous network of processors with a given latency. Results for the ..."
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This paper investigates assignment strategies (load balancing algorithms) for process farms which solve the problem of online placement of a constant number of independent tasks with given, but unknown, time complexities onto a homogeneous network of processors with a given latency. Results for the chunking and factoring assignment strategies are summarised for a probabilistic model which models tasks' time complexities as realisations of a random variable with known mean and variance. Then a deterministic model is presented which requires the knowledge of the minimal and maximal tasks' complexities. While the goal in the probabilistic model is the minimisation of the expected makespan, the goal in the deterministic model is the minimisation of the worstcase makespan. We give a novel analysis of chunking and factoring for the deterministic model. In the context of demanddriven parallel ray tracing, tasks' time complexities are unfortunately unknown until the actual computation finishes. Therefore we propose automatic selftuning procedures which estimate the missing information in runtime. We experimentally demonstrate for an "everyday ray tracing setting" that chunking does not perform much worse than factoring on up to 128 processors, if the parameters of these strategies are properly tuned. This may seem surprising. However, the experimentally measured efficiencies agree with our theoretical predictions.
RadioWave Propagation Prediction using RayTracing Techniques on a Network of Workstations (NOW) âˆ—
, 2004
"... Raytracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensi ..."
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Raytracing based radio wave propagation prediction models play an important role in the design of contemporary wireless networks as they may now take into account diverse physical phenomena including reflections, diffractions, and diffuse scattering. However, such models are computationally expensive even for moderately complex geographic environments. In this paper, we propose a computational framework that functions on a network of workstations (NOW) and helps speed up the lengthy prediction process. In raytracing based radio propagation prediction models, orders of diffractions are usually processed in a stagebystage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different raypaths require different processing times. To address these widely varying needs, we propose a combination of the phaseparallel and manager/workers paradigms as the underpinning framework. The phaseparallel component is used to coordinate different computation stages, while the manager/workers paradigm is used to balance workloads among nodes within each stage. The original computation is partitioned into multiple small tasks based on either raypathlevel or sourcepointlevel granularity. Dynamic loadbalancing scheduling schemes are employed to allocate the resulting tasks to the workers.