Message handling in parallel radiance

Reinhard, Erik; Chalmers, Alan

doi:10.1007/3-540-63697-8_121

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The parallel radiance model in [67] also uses message passing method to communicate among processors and assumes that the entire scene of operation resides in every node's main memory throughout the computation. Additional "parallel" and interactive ray-tracing systems are discussed in [65,45,63,64]. All these systems consider their computational problem as an (nearly) embarrassingly parallel one that can be divided into a number of completely independent and equally intensive components and each such component can be designated to any processor (or machine).…”

Section: Related Workmentioning

confidence: 99%

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Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Chen¹,

Delis

Bertoni³

2004

Journal of Parallel and Distributed Computing

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Ray-tracing 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 ray-tracing based radio propagation prediction models, orders of diffractions are usually processed in a stage-by-stage fashion. In addition, various source points (transmitters, diffraction corners, or diffuse scattering points) and different ray-paths require different processing times. To address these widely varying needs, we propose a combination of the phase-parallel and manager/workers paradigms as the underpinning framework. The phase-parallel 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 raypath-level or source-point-level granularity. Dynamic load-balancing scheduling schemes are employed to allocate the resulting tasks to the workers.We also address issues regarding main memory consumption, intermediate data assembly, and final prediction generation. We implement our proposed computational model on a NOW configuration by using the message passing interface (MPI) standard. Our experiments with real and synthetic building and terrain databases show that, when no constraint is imposed on the main memory consumption, the proposed prediction model performs very well and achieves nearly linear speedups under various workload. When main memory consumption is a concern, our model still delivers very promising performance rates provided that the complexity of the involved computation is high, so that the extra computation and communication overhead introduced by the proposed model do not dominate the original computation. The accuracy of prediction results and the achievable speedup rates can be significantly improved when 3D building and terrain databases are used and/or diffuse scattering effect is taken into account.Indexing Terms: Ray-tracing based radio propagation prediction model, Radio wave prediction model on Network of Workstations (NOW), Data or control domain decomposition, Dynamic workload-balancing scheduling schemes.

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Section: Related Workmentioning

confidence: 99%

“…The latter is a function of past and current status of job execution at nodes as well as the state of the communication substrate. We have considered three loadbalancing schemes functioning at the coordinator site in our adopted manager/workers model: fixed-sizetask, variable-size-task, and hybrid-size-task schemes [26,64,69,41].…”

Section: Load Balancing Schemesmentioning

confidence: 99%

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Chen¹,

Delis

Bertoni³

2004

Journal of Parallel and Distributed Computing

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“…This problem may be overcome by using a distributed object database. We followed the main ideas described for instance in [5], [4], [13], [14]. In the following we talk about our implementation.…”

Section: Distributed Object Databasementioning

confidence: 99%

“…Some parallelizations (hybrid algorithms) [7], [8], [13] extend the data-driven approach of object space subdivision algorithms with additional demand-driven tasks running in processors in order to achieve a better load balance.…”

Section: Introductionmentioning

confidence: 99%

Perfect Load Balancing for Demand- Driven Parallel Ray Tracing

Plachetka

2002

Euro-Par 2002 Parallel Processing

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Abstract.A demand-driven parallelization of the ray tracing algorithm is presented. Correctness and optimality of a perfect load balancing algorithm for image space subdivision are proved and its exact message complexity is given. An integration of antialiasing into the load balancing algorithm is proposed. A distributed object database allows rendering of complex scenes which cannot be stored in the memory of a single processor. Each processor maintains a permanent subset of the object database as well as a cache for a temporary storage of other objects. A use of object bounding boxes and bounding hierarchy in combination with the LRU (Last Recently Used) caching policy reduces the number of requests for missing data to a necessary minimum. The proposed parallelization is simple and robust. It should be easy to implement with any sequential ray tracer and any message-passing system. Our implementation is based on POV-Ray and PVM.

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“…These methods were not tested with a large number of processors, and it is unclear how well they would scale. They also did not employ speedup measures in addition to parallelization [2,4].…”

Section: Introductionmentioning

confidence: 99%

Parallelization of radiance for real time interactive lighting visualization walkthroughs

Robertson

Campbell

Lau

et al. 1999

Proceedings of the 1999 ACM/IEEE Conference on Supercomputing

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Radiance is a software package developed at Lawrence Berkeley National Laboratory for lighting visualization. Lighting visualization predicts how lighting would appear if a modelled scene were to be physically realized. Unlike most lighting systems, Radiance physically models the effects of lighting, providing an image that is closer to physical reality. This is of obvious benefit to architects and lighting designers. Such visualizations are computationally expensive: rendering a single image can take hours on a standard workstation environment. Ideally, an architect would like to be able to interactively navigate through a scene to get a full impression of the true appearance of a particular model. With this goal in mind, we have (1) developed a geometric-based method (point cloud) to reuse pixels from a previous frame and (2) developed a parallel, distributed memory implementation of Radiance and the point cloud using MPI for inter-processor communication.

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Message handling in parallel radiance

Cited by 8 publications

References 8 publications

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Radio-wave propagation prediction using ray-tracing techniques on a network of workstations (NOW)

Perfect Load Balancing for Demand- Driven Parallel Ray Tracing

Parallelization of radiance for real time interactive lighting visualization walkthroughs

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