ParallelGlobal with Low Thread Interactions

Global is an optimization algorithm conceived in the ’80s. Since then several papers discussed improvements of the algorithm, but adapting it to a multi-thread execution environment is only a recent branch of development [1]. Our previous work focused on parallel implementation on a single machine but sometimes the use of distributed systems is inevitable. In this paper we introduce a new version of Global which is the first step towards a fully distributed algorithm. While the proposed implementation still works on a single machine, it is easy to see how gossip based information sharing can be built into and be utilized by the algorithm. We show that ParallelGlobal is a feasible way to implement Global on a distributed system. However, further improvements must be made to solve real world problems with the algorithm

Effects of Pooling in ParallelGlobal with Low Thread Interactions

The first step toward a new version of Global is discussed. It is a fully distributed algorithm. While the proposed implementation runs on a single machine, gossip based information sharing can be built into and be utilized by the algorithm. ParallelGlobal shows a feasible way to implement Global on a distributed system. Further improvements must be made to solve big real world problems with the algorithm

A parallel interval arithmetic-based reliable computing method on a GPU

Video cards have now outgrown their purpose of being only a simple tool for graphic display. With their high speed video memories, lots of maths units and parallelism, they can be very powerful accessories for general purpose computing tasks. Our selected platform for testing is the CUDA (Compute Unified Device Architecture), which offers us direct access to the virtual instruction set of the video card, and we are able to run our computations on dedicated computing kernels. The CUDA development kit comes with a useful toolbox and a wide range of GPU-based function libraries. In this parallel environment, we implemented a reliable method based on the Branch-and-Bound algorithm. This algorithm will give us the opportunity to use node level (also called low-level or type 1) parallelization, since we do not modify the searching trajectories; nor do we modify the dimensions of the Branch-and-Bound tree [5]. For testing, we chose the circle covering problem. We then scaled the problem up to three dimensions, and ran tests with sphere covering problems as well