13,975 research outputs found
Extending the Nested Parallel Model to the Nested Dataflow Model with Provably Efficient Schedulers
The nested parallel (a.k.a. fork-join) model is widely used for writing
parallel programs. However, the two composition constructs, i.e. ""
(parallel) and "" (serial), are insufficient in expressing "partial
dependencies" or "partial parallelism" in a program. We propose a new dataflow
composition construct "" to express partial dependencies in
algorithms in a processor- and cache-oblivious way, thus extending the Nested
Parallel (NP) model to the \emph{Nested Dataflow} (ND) model. We redesign
several divide-and-conquer algorithms ranging from dense linear algebra to
dynamic-programming in the ND model and prove that they all have optimal span
while retaining optimal cache complexity. We propose the design of runtime
schedulers that map ND programs to multicore processors with multiple levels of
possibly shared caches (i.e, Parallel Memory Hierarchies) and provide
theoretical guarantees on their ability to preserve locality and load balance.
For this, we adapt space-bounded (SB) schedulers for the ND model. We show that
our algorithms have increased "parallelizability" in the ND model, and that SB
schedulers can use the extra parallelizability to achieve asymptotically
optimal bounds on cache misses and running time on a greater number of
processors than in the NP model. The running time for the algorithms in this
paper is , where is the cache complexity of task ,
is the cost of cache miss at level- cache which is of size ,
is a constant, and is the number of processors in an
-level cache hierarchy
An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments
The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved
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Improving parallel program performance using critical path analysis
A programming tool that performs analysis of critical paths for parallel programs has been developed. This tool determines the critical path for the program as scheduled onto a parallel computer with P processing elements, the critical path for the program expressed as a data flow graph (when maximal parallelism can be expressed), and the minimum number of processing elements (P_opt) needed to obtain maximum program speedup. Experiments were performed using several versions of a Gaussian elimination program to examine how speedup varied with changes in granularity and critical path length. These experiments showed that when the available numer of processing elements P < P_opt, increasing granularity improved program speedup more than reducing (the data flow graph's) critical path length, whereas when P β₯ P_opt, increasing granularity degraded program speedup while reducing critical path length improved program speedup
A Parallel Distributed Strategy for Arraying a Scattered Robot Swarm
We consider the problem of organizing a scattered group of robots in
two-dimensional space, with geometric maximum distance between robots. The
communication graph of the swarm is connected, but there is no central
authority for organizing it. We want to arrange them into a sorted and
equally-spaced array between the robots with lowest and highest label, while
maintaining a connected communication network.
In this paper, we describe a distributed method to accomplish these goals,
without using central control, while also keeping time, travel distance and
communication cost at a minimum. We proceed in a number of stages (leader
election, initial path construction, subtree contraction, geometric
straightening, and distributed sorting), none of which requires a central
authority, but still accomplishes best possible parallelization. The overall
arraying is performed in time, individual messages, and
travel distance. Implementation of the sorting and navigation use communication
messages of fixed size, and are a practical solution for large populations of
low-cost robots
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