4,866 research outputs found

    Scalable Parallel Numerical Constraint Solver Using Global Load Balancing

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    We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs). Our parallelization scheme consists of homogeneous worker solvers, each of which runs on an available core and communicates with others via the global load balancing (GLB) method. The parallel solver is implemented with X10 that provides an implementation of GLB as a library. In experiments, several NCSPs from the literature were solved and attained up to 516-fold speedup using 600 cores of the TSUBAME2.5 supercomputer.Comment: To be presented at X10'15 Worksho

    Quafu-Qcover: Explore Combinatorial Optimization Problems on Cloud-based Quantum Computers

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    We present Quafu-Qcover, an open-source cloud-based software package designed for combinatorial optimization problems that support both quantum simulators and hardware backends. Quafu-Qcover provides a standardized and complete workflow for solving combinatorial optimization problems using the Quantum Approximate Optimization Algorithm (QAOA). It enables the automatic modeling of the original problem as a quadratic unconstrained binary optimization (QUBO) model and corresponding Ising model, which can be further transformed into a weight graph. The core of Qcover relies on a graph decomposition-based classical algorithm, which enables obtaining the optimal parameters for the shallow QAOA circuit more efficiently. Quafu-Qcover includes a specialized compiler that translates QAOA circuits into physical quantum circuits capable of execution on Quafu cloud quantum computers. Compared to a general-purpose compiler, ours generates shorter circuit depths while also possessing better speed performance. The Qcover compiler can establish a library of qubits coupling substructures in real time based on the updated calibration data of the superconducting quantum devices, ensuring that the task is executed on physical qubits with higher fidelity. The Quafu-Qcover allows us to retrieve quantum computer sampling result information at any time using task ID, enabling asynchronous processing. Besides, it includes modules for result preprocessing and visualization, allowing for an intuitive display of the solution to combinatorial optimization problems. We hope that Quafu-Qcover can serve as a guiding example for how to explore application problems on the Quafu cloud quantum computersComment: Comments are welcome

    Partitioning problems in parallel, pipelined and distributed computing

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    The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest

    Scalable Parallel Numerical CSP Solver

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    We present a parallel solver for numerical constraint satisfaction problems (NCSPs) that can scale on a number of cores. Our proposed method runs worker solvers on the available cores and simultaneously the workers cooperate for the search space distribution and balancing. In the experiments, we attained up to 119-fold speedup using 256 cores of a parallel computer.Comment: The final publication is available at Springe

    Load-Balanced Bottleneck Objectives in Process Mapping

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    We propose a new problem formulation for graph partitioning that is tailored to the needs of time-critical simulations on modern heterogeneous supercomputers

    A Survey of Pipelined Workflow Scheduling: Models and Algorithms

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    International audienceA large class of applications need to execute the same workflow on different data sets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task-, data-, pipelined-, and/or replicated-parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors or optimization goals. This paper surveys the field by summing up and structuring known results and approaches
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