On comparing ray shooting algorithms

Havran, Vlastimil; Purgathofer, Werner

doi:10.1016/s0097-8493(03)00103-1

Cited by 105 publications

(201 citation statements)

References 15 publications

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“…A few examples include constraint satisfaction in AI [11], frequent itemset mining in data mining [6], maximal clique enumeration in graph mining [16], k-d tree traversal for ray tracing in graphics [9], and logic programming languages such as Prolog. Backtracking typically constructs optimal solutions from candidate solutions, thus forming a search tree that the backtracking traverses.…”

Section: Introductionmentioning

confidence: 99%

Lessons Learned from Exploring the Backtracking Paradigm on the GPU

Jenkins

Arkatkar

Owens

et al. 2011

Euro-Par 2011 Parallel Processing

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Abstract. We explore the backtracking paradigm with properties seen as sub-optimal for GPU architectures, using as a case study the maximal clique enumeration problem, and find that the presence of these properties limit GPU performance to approximately 1.4-2.25 times a single CPU core. The GPU performance "lessons" we find critical to providing this performance include a coarse-and-fine-grain parallelization of the search space, a low-overhead load-balanced distribution of work, global memory latency hiding through coalescence, saturation, and shared memory utilization, and the use of GPU output buffering as a solution to irregular workloads and a large solution domain. We also find a strong reliance on an efficient global problem structure representation that bounds any efficiencies gained from these lessons, and discuss the meanings of these results to backtracking problems in general.

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Section: Introductionmentioning

confidence: 99%

Lessons Learned from Exploring the Backtracking Paradigm on the GPU

Jenkins

Arkatkar

Owens

et al. 2011

Euro-Par 2011 Parallel Processing

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“…1. The typical construction and traversal procedure, taken from Havran's thesis [17] as well as Pharr and Humphreys' book [19], are reviewed as follows, and how to apply the kd-tree in the SBR is also described.…”

Section: Ray Tracing Using the Kd-tree Acceleration Structurementioning

confidence: 99%

“…As the patch may be referenced in multiple leaf nodes and these leaf nodes are usually neighboring nodes, there is a high probability that a ray may be tested with the same patch multiple times. These redundant tests can be avoided through the mailbox technique [17][18][19]. In this technique, a unique integer index is assigned to each ray, and each patch also keeps the index of the last tested ray.…”

Section: Kd-tree Traversal With the Mailbox Techniquementioning

confidence: 99%

“…It is necessary, therefore, to find an effective acceleration data structure for the uneven spatial distribution of patches. In computer graphics, different acceleration data structures across a variety of scenes (targets) have been widely studied to evaluate their relative performance, and the conclusion is that the kd-tree, which adaptively subdivides the target space into uneven boxes, is the best general-purpose acceleration structure for ray tracing of static scenes [17]. Thus, this paper seeks to make use of the kd-tree to accelerate the ray tracing in the SBR.…”

Section: Introductionmentioning

confidence: 99%

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Kd-Tree Based Fast Ray Tracing for RCS Prediction

Tao¹,

Lin²,

Bao³

2008

PIER

116

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Abstract-Ray tracing is of great use for computational electromagnetics, such as the well-known shooting and bouncing ray (SBR) method. In this paper, the kd-tree data structure, coupled with the mailbox technique, is proposed to accelerate the ray tracing in the SBR. The kd-tree is highly effective in handling the irregularly distribution of patches of the target, while the repeatedly intersection tests between the ray and the patch when using space division acceleration structures can be eliminated through the mailbox technique. Numerical results show excellent agreement with the measured data and the exact solution, and demonstrate that the kd-tree as well as the mailbox technique can greatly reduce the computation time.

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“…As a result, various techniques have been proposed to reduce the computational time of the SBR. The octree and kdtree [4,5] were utilized to accelerate the ray tube tracing of the SBR, and the latter has been well known as the best general-purpose acceleration structure for ray tracing of static scenes in computer graphics [6]. The multiresolution grid algorithm [7] was introduced to reduce the total number of ray tubes.…”

Section: Introductionmentioning

confidence: 99%

Parallel Shooting and Bouncing Ray Method on Gpu Clusters for Analysis of Electromagnetic Scattering

Tao¹,

Lin²

2013

PIER

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Abstract-This paper proposes an efficient parallel shooting and bouncing ray (SBR) method on the graphics processing unit (GPU) cluster for solving the electromagnetic scattering problems. At each incident direction, the parallel SBR method partitions the virtual aperture into sub-apertures, and distributes the computational process of each sub-aperture over GPU nodes. As ray tubes in the virtual aperture do not have the same computational time, the parallel efficiency highly depends on how to partition the virtual aperture. This paper addresses this issue by a dynamic partitioning scheme according to the computational time at the previous angle, which can achieve excellent load balance. Numerical examples are presented to demonstrate the accuracy, high parallel efficiency, good scalability and versatility of the proposed method.

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On comparing ray shooting algorithms

Cited by 105 publications

References 15 publications

Lessons Learned from Exploring the Backtracking Paradigm on the GPU

Lessons Learned from Exploring the Backtracking Paradigm on the GPU

Kd-Tree Based Fast Ray Tracing for RCS Prediction

Parallel Shooting and Bouncing Ray Method on Gpu Clusters for Analysis of Electromagnetic Scattering

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