A Bloom filter is a very compact data structure that supports approximate membership queries on a set, allowing false positives.We propose several new variants of Bloom filters and replacements with similar functionality. All of them have a better cache-efficiency and need less hash bits than regular Bloom filters. Some use SIMD functionality, while the others provide an even better space efficiency. As a consequence, we get a more flexible trade-off between false-positive rate, space-efficiency, cache-efficiency, hashefficiency, and computational effort. We analyze the efficiency of Bloom filters and the proposed replacements in detail, in terms of the false-positive rate, the number of expected cache-misses, and the number of required hash bits. We also describe and experimentally evaluate the performance of highly tuned implementations. For many settings, our alternatives perform better than the methods proposed so far.
Computers with multiple processor cores using shared memory are now ubiquitous. In this paper, we present several parallel geometric algorithms that specifically target this environment, with the goal of exploiting the additional computing power. The algorithms we describe are (a) 2-/3-dimensional spatial sorting of points, as is typically used for preprocessing before using incremental algorithms, (b) d-dimensional axis-aligned box intersection computation, and finally (c) 3D bulk insertion of points into Delaunay triangulations, which can be used for mesh generation algorithms, or simply for constructing 3D Delaunay triangulations. For the latter, we introduce as a foundational element the design of a container data structure that both provides concurrent addition and removal operations and is compact in memory. This makes it especially well-suited for storing large dynamic graphs such as Delaunay triangulations.We show experimental results for these algorithms, using our implementations based on the Computational Geometry Algorithms Library (CGAL). This work is a step towards what we hope will become a parallel mode for CGAL, where algorithms automatically use the available parallel resources without requiring significant user intervention.
Abstract. STL dictionaries like map and set are commonly used in C++ programs. We consider parallelizing two of their bulk operations, namely the construction from many elements, and the insertion of many elements at a time. Practical algorithms are proposed for these tasks. The implementation is completely generic and engineered to provide best performance for the variety of possible input characteristics. It features transparent integration into the STL. This can make programs profit in an easy way from multi-core processing power. The performance measurements show the practical usefulness on real-world multi-core machines with up to eight cores.
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