11,756 research outputs found
On Rearrangement of Items Stored in Stacks
There are stacks, each filled with items, and one empty stack.
Every stack has capacity . A robot arm, in one stack operation (step),
may pop one item from the top of a non-empty stack and subsequently push it
onto a stack not at capacity. In a {\em labeled} problem, all items are
distinguishable and are initially randomly scattered in the stacks. The
items must be rearranged using pop-and-pushs so that in the end, the stack holds items , in that order, from the top to
the bottom for all . In an {\em unlabeled} problem, the
items are of types of each. The goal is to rearrange items so that
items of type are located in the stack for all . In carrying out the rearrangement, a natural question is to find the least
number of required pop-and-pushes.
Our main contributions are: (1) an algorithm for restoring the order of
items stored in an table using only column and row
permutations, and its generalization, and (2) an algorithm with a guaranteed
upper bound of steps for solving both versions of the stack
rearrangement problem when for arbitrary fixed
positive number . In terms of the required number of steps, the labeled and
unlabeled version have lower bounds
and , respectively
On the acceleration of wavefront applications using distributed many-core architectures
In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures
Cold Matter Assembled Atom-by-Atom
The realization of large-scale fully controllable quantum systems is an
exciting frontier in modern physical science. We use atom-by-atom assembly to
implement a novel platform for the deterministic preparation of regular arrays
of individually controlled cold atoms. In our approach, a measurement and
feedback procedure eliminates the entropy associated with probabilistic trap
occupation and results in defect-free arrays of over 50 atoms in less than 400
ms. The technique is based on fast, real-time control of 100 optical tweezers,
which we use to arrange atoms in desired geometric patterns and to maintain
these configurations by replacing lost atoms with surplus atoms from a
reservoir. This bottom-up approach enables controlled engineering of scalable
many-body systems for quantum information processing, quantum simulations, and
precision measurements.Comment: 12 pages, 9 figures, 3 movies as ancillary file
A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity
Due to missing IP multicast support on an Internet scale, over-the-top media
streams are delivered with the help of overlays as used by content delivery
networks and their peer-to-peer (P2P) extensions. In this context,
mesh/pull-based swarming plays an important role either as pure streaming
approach or in combination with tree/push mechanisms. However, the impact of
realistic client populations with heterogeneous resources is not yet fully
understood. In this technical report, we contribute to closing this gap by
mathematically analysing the most basic scheduling mechanisms latest deadline
first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain
framework and combining them into a simple, yet powerful, mixed strategy to
leverage inherent differences in client resources. The main contributions are
twofold: (1) a mathematical framework for swarming on random graphs is proposed
with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed
strategy, named SchedMix, is proposed that leverages peer heterogeneity. The
proposed strategy, SchedMix is shown to outperform the other two strategies
using different abstractions: a mean-field theoretic analysis of buffer
probabilities, simulations of a stochastic model on random graphs, and a
full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to
http://ieeexplore.ieee.org/document/7497234
- …