A parallel algorithm for solving TSP(traveling salesman problem) is presented in this paper. Combining 2-opt local search optimization with genetic algorithm is the main ideal of this algorithm. In this paper, MPI+TBB...
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A parallel algorithm for solving TSP(traveling salesman problem) is presented in this paper. Combining 2-opt local search optimization with genetic algorithm is the main ideal of this algorithm. In this paper, MPI+TBB hybrid parallel programming model is employed in implement of our algorithm. Numerical results indicate that it is possible to arrive at high quality solutions in reasonable time. With the increase in the scale of solving problem, the speedup of parallel algorithm is improved. Moreover, with the growth in the number of cores, the speedup of the parallel algorithm presents nearly linear growth.
Task-parallel programming languages offer a variety of high-level mechanisms for synchronization that trade off between flexibility and deadlock safety. Some approaches are deadlock-free by construction but support li...
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Task-parallel programming languages offer a variety of high-level mechanisms for synchronization that trade off between flexibility and deadlock safety. Some approaches are deadlock-free by construction but support limited synchronization patterns, while other approaches are trivial to deadlock. In high-level task-parallel programming, it is imperative that language features offer both flexibility to avoid over-synchronization and also sufficient protection against logical deadlocks. Lack of flexibility leads to code that does not take full advantage of the available parallelism in the computation. Lack of deadlock protection leads to error-prone code in which a single bug can involve arbitrarily many tasks, making it difficult to reason about. We make advances in both flexibility and deadlock protection for existing synchronization mechanisms by carefully designing dynamically verifiable usage policies and language constructs. We first define a deadlock-freedom policy for futures. The rules of the policy follow naturally from the semantics of asynchronous task closures and correspond to a preorder traversal of the task tree. The policy admits an additional class of deadlock-free programs compared to past work. Each blocking wait for a future can be verified by a stateless, lock-free algorithm, resulting in low time and memory overheads at runtime. In order to define and identify deadlocks for promises, we introduce a mechanism for promises to be owned by tasks. Simple annotations make it possible to ensure that each promise is eventually fulfilled by the responsible task or handed off to another task. Ownership semantics allows us to formally define two kinds of promise bugs: omitted sets and deadlock cycles. We present novel detection algorithms for both bugs. We further introduce an approximate deadlock-freedom policy for promises that, instead of precisely detecting cycles, raises an alarm when synchronization dependences occurring between trees of tasks are a
DVM-system is designed for the development of parallel programs of scientific and technical calculations in C-DVMH and Fortran-DVMH languages. These languages use a single parallel programming model (DVMH model) and a...
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ParlayLib is a C++ library for developing efficient parallel algorithms and software on shared-memory multicore machines. It provides additional tools and primitives that go beyond what is available in the C++ standar...
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parallel Computing contributes significantly to most disciplines for solving several scientific problems such as partial differential equations (PDEs), load balancing, and deep learning. The primary characteristic of ...
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ISBN:
(纸本)9783030451820;9783030451837
parallel Computing contributes significantly to most disciplines for solving several scientific problems such as partial differential equations (PDEs), load balancing, and deep learning. The primary characteristic of parallelism is its ability to ameliorate performance on many different sets of computers. Consequently, many researchers are continually expending their efforts to produce efficient parallel solutions for various problems such as heat equation. Heat equation is a natural phenomenon used in many fields like mathematics and physics. Usually, its associated model is defined by a set of partial differential equations (PDEs). This paper is primarily aimed at showing two parallel programs for solving the heat equation which has been discrete-sized using the finite difference method (FDM). These programs have been implemented through different parallel platforms such as SkelGIS and Compute Unified Device Architecture (CUDA).
Today's processors become fatter, not faster. However, the exploitation of these massively parallel compute resources remains a challenge for many traditional HPC applications regarding scalability, portability an...
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Sequence alignment is a problem in bioinformatics that involves arranging sequences of proteins, RNA or DNA so that similar regions between two or more sequences may be determined. The Smith-Waterman algorithm is a ke...
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A CUDA kernel is proposed in this paper for acceleration of the computation of a dynamic hedging model. This is a very useful tool in segregated fund modelling. Current approaches delve on scenario reduction technique...
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There has been a recent trend of teaching agents to solve puzzles and play games using Deep Reinforcement Learning (DRL) which was brought by the success of AlphaGo. While this method has given some truly groundbreaki...
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There has been a recent trend of teaching agents to solve puzzles and play games using Deep Reinforcement Learning (DRL) which was brought by the success of AlphaGo. While this method has given some truly groundbreaking results and it is very computationally intensive. This paper evaluates the feasibility of solving Combinatorial Optimization Problems such as Twisty Puzzles using parallel Q-Learning (PQL). We propose a method using Constant Share-Reinforcement Learning (CS-RL) as a more resource optimized approach and measure the impact of sub-goals built using human knowledge. We attempt to solve three puzzles, the 2x2x2 Pocket Rubik's Cube, the Skewb and the Pyraminx with and without sub-goals based on popular solving methods used by humans and compare their results. Our agents are able to solve these puzzles with a 100% success rate by just a few hours of training, much better than previous DRL based agents that require large computational time. Further, the proposed approach is compared with Deep Learning based solution for 2x2x2 Rubik's Cube and observed higher success rate.
Computer technology, which continues to develop today, often has difficulties in meeting the needs of signal and image processing software. As a result of the developing technology, software needs larger memory and fa...
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