228 research outputs found
Quantitative contraction rates for Markov chains on general state spaces
We investigate the problem of quantifying contraction coefficients of Markov
transition kernels in Kantorovich ( Wasserstein) distances. For diffusion
processes, relatively precise quantitative bounds on contraction rates have
recently been derived by combining appropriate couplings with carefully
designed Kantorovich distances. In this paper, we partially carry over this
approach from diffusions to Markov chains. We derive quantitative lower bounds
on contraction rates for Markov chains on general state spaces that are
powerful if the dynamics is dominated by small local moves. For Markov chains
on with isotropic transition kernels, the general bounds can be
used efficiently together with a coupling that combines maximal and reflection
coupling. The results are applied to Euler discretizations of stochastic
differential equations with non-globally contractive drifts, and to the
Metropolis adjusted Langevin algorithm for sampling from a class of probability
measures on high dimensional state spaces that are not globally log-concave.Comment: 39 page
Engineering Parallel String Sorting
We discuss how string sorting algorithms can be parallelized on modern
multi-core shared memory machines. As a synthesis of the best sequential string
sorting algorithms and successful parallel sorting algorithms for atomic
objects, we first propose string sample sort. The algorithm makes effective use
of the memory hierarchy, uses additional word level parallelism, and largely
avoids branch mispredictions. Then we focus on NUMA architectures, and develop
parallel multiway LCP-merge and -mergesort to reduce the number of random
memory accesses to remote nodes. Additionally, we parallelize variants of
multikey quicksort and radix sort that are also useful in certain situations.
Comprehensive experiments on five current multi-core platforms are then
reported and discussed. The experiments show that our implementations scale
very well on real-world inputs and modern machines.Comment: 46 pages, extension of "Parallel String Sample Sort" arXiv:1305.115
Parallel Multiway LCP-Mergesort
In this bachelor thesis, multiway LCP-Merge is introduced, parallelized and applied to create a fully parallel LCP-Mergesort, as well as NUMA optimized pS5. As an advancement of binary LCP-Mergesort, a multiway LCP-aware tournament tree is introduced and parallelized. For dynamic load balancing, one well-known and two new strategies for splitting merge work packages are utilized. Besides the introduction of fully parallel multiway LCP-Mergesort, further focus is put on NUMA architectures. Thus \u27parallel Super Scalar String Sample Sort\u27 (pS5) is adapted to the special properties of these systems by utilising the parallel LCP-Merge. Moreover this yields an efficient and generic approach for parallelizing arbitrary sequential string sorting algorithms and making parallel algorithms NUMA-aware. Several optimizations, important for practical implementations, as well as comprehensive experiments on two current NUMA platforms, are then reported and discussed. The experiments show the good scalability of the introduced algorithms and especially, the great improvements of NUMA-aware pS5 with real-world input sets on modern machines
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