30,462 research outputs found
Compressed Representations of Permutations, and Applications
We explore various techniques to compress a permutation over n
integers, taking advantage of ordered subsequences in , while supporting
its application (i) and the application of its inverse in
small time. Our compression schemes yield several interesting byproducts, in
many cases matching, improving or extending the best existing results on
applications such as the encoding of a permutation in order to support iterated
applications of it, of integer functions, and of inverted lists and
suffix arrays
An Elegant Algorithm for the Construction of Suffix Arrays
The suffix array is a data structure that finds numerous applications in
string processing problems for both linguistic texts and biological data. It
has been introduced as a memory efficient alternative for suffix trees. The
suffix array consists of the sorted suffixes of a string. There are several
linear time suffix array construction algorithms (SACAs) known in the
literature. However, one of the fastest algorithms in practice has a worst case
run time of . The problem of designing practically and theoretically
efficient techniques remains open. In this paper we present an elegant
algorithm for suffix array construction which takes linear time with high
probability; the probability is on the space of all possible inputs. Our
algorithm is one of the simplest of the known SACAs and it opens up a new
dimension of suffix array construction that has not been explored until now.
Our algorithm is easily parallelizable. We offer parallel implementations on
various parallel models of computing. We prove a lemma on the -mers of a
random string which might find independent applications. We also present
another algorithm that utilizes the above algorithm. This algorithm is called
RadixSA and has a worst case run time of . RadixSA introduces an
idea that may find independent applications as a speedup technique for other
SACAs. An empirical comparison of RadixSA with other algorithms on various
datasets reveals that our algorithm is one of the fastest algorithms to date.
The C++ source code is freely available at
http://www.engr.uconn.edu/~man09004/radixSA.zi
Worst-Case Efficient Sorting with QuickMergesort
The two most prominent solutions for the sorting problem are Quicksort and
Mergesort. While Quicksort is very fast on average, Mergesort additionally
gives worst-case guarantees, but needs extra space for a linear number of
elements. Worst-case efficient in-place sorting, however, remains a challenge:
the standard solution, Heapsort, suffers from a bad cache behavior and is also
not overly fast for in-cache instances.
In this work we present median-of-medians QuickMergesort (MoMQuickMergesort),
a new variant of QuickMergesort, which combines Quicksort with Mergesort
allowing the latter to be implemented in place. Our new variant applies the
median-of-medians algorithm for selecting pivots in order to circumvent the
quadratic worst case. Indeed, we show that it uses at most
comparisons for large enough.
We experimentally confirm the theoretical estimates and show that the new
algorithm outperforms Heapsort by far and is only around 10% slower than
Introsort (std::sort implementation of stdlibc++), which has a rather poor
guarantee for the worst case. We also simulate the worst case, which is only
around 10% slower than the average case. In particular, the new algorithm is a
natural candidate to replace Heapsort as a worst-case stopper in Introsort
QuickXsort: Efficient Sorting with n log n - 1.399n +o(n) Comparisons on Average
In this paper we generalize the idea of QuickHeapsort leading to the notion
of QuickXsort. Given some external sorting algorithm X, QuickXsort yields an
internal sorting algorithm if X satisfies certain natural conditions.
With QuickWeakHeapsort and QuickMergesort we present two examples for the
QuickXsort-construction. Both are efficient algorithms that incur approximately
n log n - 1.26n +o(n) comparisons on the average. A worst case of n log n +
O(n) comparisons can be achieved without significantly affecting the average
case.
Furthermore, we describe an implementation of MergeInsertion for small n.
Taking MergeInsertion as a base case for QuickMergesort, we establish a
worst-case efficient sorting algorithm calling for n log n - 1.3999n + o(n)
comparisons on average. QuickMergesort with constant size base cases shows the
best performance on practical inputs: when sorting integers it is slower by
only 15% to STL-Introsort
A Parallel Monte Carlo Code for Simulating Collisional N-body Systems
We present a new parallel code for computing the dynamical evolution of
collisional N-body systems with up to N~10^7 particles. Our code is based on
the the Henon Monte Carlo method for solving the Fokker-Planck equation, and
makes assumptions of spherical symmetry and dynamical equilibrium. The
principal algorithmic developments involve optimizing data structures, and the
introduction of a parallel random number generation scheme, as well as a
parallel sorting algorithm, required to find nearest neighbors for interactions
and to compute the gravitational potential. The new algorithms we introduce
along with our choice of decomposition scheme minimize communication costs and
ensure optimal distribution of data and workload among the processing units.
The implementation uses the Message Passing Interface (MPI) library for
communication, which makes it portable to many different supercomputing
architectures. We validate the code by calculating the evolution of clusters
with initial Plummer distribution functions up to core collapse with the number
of stars, N, spanning three orders of magnitude, from 10^5 to 10^7. We find
that our results are in good agreement with self-similar core-collapse
solutions, and the core collapse times generally agree with expectations from
the literature. Also, we observe good total energy conservation, within less
than 0.04% throughout all simulations. We analyze the performance of the code,
and demonstrate near-linear scaling of the runtime with the number of
processors up to 64 processors for N=10^5, 128 for N=10^6 and 256 for N=10^7.
The runtime reaches a saturation with the addition of more processors beyond
these limits which is a characteristic of the parallel sorting algorithm. The
resulting maximum speedups we achieve are approximately 60x, 100x, and 220x,
respectively.Comment: 53 pages, 13 figures, accepted for publication in ApJ Supplement
A Bulk-Parallel Priority Queue in External Memory with STXXL
We propose the design and an implementation of a bulk-parallel external
memory priority queue to take advantage of both shared-memory parallelism and
high external memory transfer speeds to parallel disks. To achieve higher
performance by decoupling item insertions and extractions, we offer two
parallelization interfaces: one using "bulk" sequences, the other by defining
"limit" items. In the design, we discuss how to parallelize insertions using
multiple heaps, and how to calculate a dynamic prediction sequence to prefetch
blocks and apply parallel multiway merge for extraction. Our experimental
results show that in the selected benchmarks the priority queue reaches 75% of
the full parallel I/O bandwidth of rotational disks and and 65% of SSDs, or the
speed of sorting in external memory when bounded by computation.Comment: extended version of SEA'15 conference pape
- …