Approximable 1-Turn Routing Problems in All-Optical Mesh Networks

In all-optical networks, several communications can be transmitted through the same fiber link provided that they use different wavelengths. The MINIMUM ALL-OPTICAL ROUTING problem (given a list of pairs of nodes standing for as many point to point communication requests, assign to each request a route along with a wavelength so as to minimize the overall number of assigned wavelengths) has been paid a lot of attention and is known to be N P–hard. Rings, trees and meshes have thus been investigated as specific networks, but leading to just as many N P–hard problems. This paper investigates 1-turn routings in meshes (paths are allowed one turn only). We first show the MINIMUM LOAD 1-TURN ROUTING problem to be N P–hard but 2-APX (more generally, the MINIMUM LOAD k-CHOICES ROUTING problem is N P–hard but k-APX), then that the MINIMUM 1-TURN PATHS COLOURING problem is 4-APX (more generally, any d-segmentable routing of load L in a hypermesh of dimension d can be coloured with 2d(L−1)+1 colours at most). >From there, we prove the MINIMUM ALL-OPTICAL 1-TURN ROUTING problem to be APX

QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection

Scheduling multicasts on unit-capacity trees and meshes

This paper studies the multicast routing and admission control problem on unit-capacity tree and mesh topologies in the throughput-model. The problem is a generalization of the edge-disjoint paths problem and is NP-hard both on trees and meshes. We study both the offline and the online version of the problem: In the offline setting, we give the first constant-factor approximation algorithm for trees, and an O((log log n)^2)-factor approximation algorithm for meshes. In the online setting, we give the first polylogarithmic competitive online algorithm for tree and mesh topologies. No polylogarithmic-competitive algorithm is possible on general network topologies [Bartal,Fiat,Leonardi, 96], and there exists a polylogarithmic lower bound on the competitive ratio of any online algorithm on tree topologies [Awerbuch,Azar,Fiat,Leighton, 96]. We prove the same lower bound for meshes

QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection

Communication aspects of parallel processing

Cover title.Includes bibliographical references.Supported in part by the Air Force Office of Scientific Research. AFOSR-88-0032Cüneyt Özveren

QuickCSG: Arbitrary and Faster Boolean Combinations of N Solids

While studied over several decades, the computation of boolean operations on polyhedra is almost always addressed by focusing on the case of two polyhedra. For multiple input polyhedra and an arbitrary boolean operation to be applied, the operation is decomposed over a binary CSG tree, each node being processed separately in quasilinear time. For large trees, this is both error prone due to intermediate geometry and error accumulation, and inefficient because each node yields a specific overhead. We introduce a fundamentally new approach to polyhedral CSG evaluation, addressing the general N-polyhedron case. We propose a new vertex-centric view of the problem, which both simplifies the algorithm computing resulting geometric contributions, and vastly facilitates its spatial decomposition. We then embed the entire problem in a single KD-tree, specifically geared toward the final result by early pruning of any region of space not contributing to the final surface. This not only improves the robustness of the approach, it also gives it a fundamental speed advantage, with an output complexity depending on the output mesh size instead of the input size as with usual approaches. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to several dozen polyhedra. The algorithm is also shown to outperform recent GPU implementations and approximate discretizations, while producing an exact output without redundant facets.Quoique étudié depuis des décennies, le calcul d'opérations booléennes sur des polyèdres est quasiment toujours fait sur deux opérandes. Pour un plus grand nombre de polyèdres et une opération booléenne arbitraire à effectuer, l'opération est décomposée sur un arbre binaire CSG (géométrie constructive), dans lequel chaque nœud est traité séparément en temps quasi-linéaire. Pour de grands arbres, ceci est à la fois source d'erreurs, à cause des calculs géométriques intermédiaires, et inefficace à cause des traitements superflus au niveau des nœuds. Nous introduisons une approche fondamentalement nouvelle qui traite le cas général de N polyèdres. Nous proposons une vue du problème centrée sur les sommets, ce qui simplifie l'algorithme et facilite sa décomposition spatiale. Nous traitons le problème dans un seul KD-tree, qui est dirigé vers le résultat final, en élaguant les régions de l'espace qui ne contribuent pas à la surface finale. Non seulement ceci améliore la robustesse de l'approche mais ça lui donne un avantage en vitesse, car la complexité dépend plus de la taille de la sortie que celle d'entrée. En la combinant avec une parallélisation basée sur du vol de tâche, l'algorithme a des performances inouïes, d'un ou deux ordres de grandeur plus rapide que les algorithmes de l'état de l'art sur CPU et GPU. De plus il produit un résultat exact, sans aucune primitive géométrique superflue