Activity recognition from videos with parallel hypergraph matching on GPUs

In this paper, we propose a method for activity recognition from videos based on sparse local features and hypergraph matching. We benefit from special properties of the temporal domain in the data to derive a sequential and fast graph matching algorithm for GPUs. Traditionally, graphs and hypergraphs are frequently used to recognize complex and often non-rigid patterns in computer vision, either through graph matching or point-set matching with graphs. Most formulations resort to the minimization of a difficult discrete energy function mixing geometric or structural terms with data attached terms involving appearance features. Traditional methods solve this minimization problem approximately, for instance with spectral techniques. In this work, instead of solving the problem approximatively, the exact solution for the optimal assignment is calculated in parallel on GPUs. The graphical structure is simplified and regularized, which allows to derive an efficient recursive minimization algorithm. The algorithm distributes subproblems over the calculation units of a GPU, which solves them in parallel, allowing the system to run faster than real-time on medium-end GPUs

Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding

Can the success of reinforcement learning methods for simple combinatorial optimization problems be extended to multi-robot sequential assignment planning? In addition to the challenge of achieving near-optimal performance in large problems, transferability to an unseen number of robots and tasks is another key challenge for real-world applications. In this paper, we suggest a method that achieves the first success in both challenges for robot/machine scheduling problems. Our method comprises of three components. First, we show a robot scheduling problem can be expressed as a random probabilistic graphical model (PGM). We develop a mean-field inference method for random PGM and use it for Q-function inference. Second, we show that transferability can be achieved by carefully designing two-step sequential encoding of problem state. Third, we resolve the computational scalability issue of fitted Q-iteration by suggesting a heuristic auction-based Q-iteration fitting method enabled by transferability we achieved. We apply our method to discrete-time, discrete space problems (Multi-Robot Reward Collection (MRRC)) and scalably achieve 97% optimality with transferability. This optimality is maintained under stochastic contexts. By extending our method to continuous time, continuous space formulation, we claim to be the first learning-based method with scalable performance among multi-machine scheduling problems; our method scalability achieves comparable performance to popular metaheuristics in Identical parallel machine scheduling (IPMS) problems

Mapping Finite Element Graphs on Hypercubes

In parallel computing, it is important to map a parallel program onto a parallel computer such that the total execution time of a parallel program is minimized. In general, a parallel program and a parallel computer can be represented by a task graph (TG) and a processor graph (PG), respectively. For a TG, nodes represent tasks of a parallel program and edges denote the data communication needed between tasks. The weights associated with nodes and edges represent the computational load and communication cost, respectively. For a PG, nodes and edges denote processors and communication channels, respectively. By using the graph model, the mapping problem becomes a task allocation problem. In the task allocation problem, we try to distribute the computational load of a parallel program to the processors of a parallel computer as evenly as possible (the load balance criterion (LBC) and minimize the communication cost of processors (the minimum communication cost criterion (MCCC)). The optimal assignment of tasks to processors in order to minimize the total execution time is known to be NP-complete [GaJo79]. This means that the optimal solution is intractable. Therefore, satisfactory suboptimal solutions are generally sought. In this paper, we will discuss how to map finite element graphs (FEGs) onto hypercubes. Our schemes are general and are applicable to a wide variety of PGs. The finite element method (FEM) is a widely used method for the structural modeling of physical system [LaPi83]. Due to the properties of compute-intensiveness and compute-locality, it is very attractive to implement this method on parallel computers [BeBo87] [Bokh81] [Jord78] [SaEr87]. The number of nodes in a FEG is usually greater than the number of processors in a parallel computer. It is important to partition a FEG into M modules such that the computational load of modules are equal and the communication cost among modules are minimized, where M is the number of processors of a parallel computer

Selection and Assignment of Machines: a Parallel Aproach

In this paper, a two-phase method is presented for selection of machines to be kept on the shop floor and assignment of parts to be manufactured to these machines. In the first phase, dynamic programming or a heuristic procedure identifies a set of feasible solutions to a knapsack problem. In the second phase, implicit enumeration technique or a greedy algorithm solves an assignment problem. The proposed method is written in language C and runs on a parallel virtual machine called PVM-W95. The results obtained from the parallel implementation on several examples which are found in the literature as well as examples generated at random were used to establish a comparison with the sequential algorithm and to perform a speedup analysis

Karesel atama problemleri için tavlama benzetimi paralelleştirme yöntemlerinin karşılaştırılması

Quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems in the NP-hard class. Due to the difficulty of the problem, many researchers have been studying this type of assignment problem. In this work simulated annealing method is parallelized on MATLAB platform and is used to solve 36 problems from QAPLIB which is a well-known QAP library. The performance of different parallelization methods is compared for the problems used. As a result, when compared with the serial simulated annealing method, it is seen that the parallel methods give faster results when the appropriate parameters are used

Computer Program of Line Balancing under the Multiple Workers in Each Station (LBMW)

An assembly line with no paralleling of work elements and work stations is called a serial line. The cycle time of the serial line must be at least equal to the maximum work element time. To lower the cycle time beyond the limit and increase the production rate, one may permit the paralleling of work elements or work stations. So in this paper we propose the parallel assignment method for achieving a higher production rate. In this method, work elements are assigned to work stations under the multiple upper time limits which are the products of the various numbers of workers and the limiting cycle time. Further we develop the computer program of the proposed method and provide an illustrative problem and computational results