Degree of parallelism

About: Degree of parallelism is a research topic. Over the lifetime, 1515 publications have been published within this topic receiving 25546 citations.

A parallel execution model of logic programs

Abstract: A parallel-execution model that can concurrently exploit AND and OR parallelism in logic programs is presented. This model employs a combination of techniques in an approach to executing logic problems in parallel, making tradeoffs among number of processes, degree of parallelism, and combination bandwidth. For interpreting a nondeterministic logic program, this model (1) performs frame inheritance for newly created goals, (2) creates data-dependency graphs (DDGs) that represent relationships among the goals, and (3) constructs appropriate process structures based on the DDGs. (1) The use of frame inheritance serves to increase modularity. In contrast to most previous parallel models that have a large single process structure, frame inheritance facilitates the dynamic construction of multiple independent process structures, and thus permits further manipulation of each process structure. (2) The dynamic determination of data dependency serves to reduce computational complexity. In comparison to models that exploit brute-force parallelism and models that have fixed execution sequences, this model can reduce the number of unification and/or merging steps substantially. In comparison to models that exploit only AND parallelism, this model can selectively exploit demand-driven computation, according to the binding of the query and optional annotations. (3) The construction of appropriate process structures serves to reduce communication complexity. Unlike other methods that map DDGs directly onto process structures, this model can significantly reduce the number of data sent to a process and/or the number of communication channels connected to a process. >

Pricing American Options Under High-Dimensional Models with Recursive Adaptive Sparse Expectations

Abstract: We introduce a novel numerical framework for pricing American options in high dimensions. Such settings naturally arise for derivatives with multiple underlying assets, like basket options. They are equally important for single-asset options because high-dimensional models are best capable of capturing observed price dynamics. Yet, higher-dimensional settings come at the cost of a loss of tractability due to the accompanying exponential growth of computational complexity. Our scheme manages to alleviate the problem of dimension scaling through the use of adaptive sparse grids. We approximate the value function with a low number of points and recursively apply fast approximations of the expectation operator from an exercise period to the previous one. The algorithm copes with discretely spaced, possibly nonuniform, time grids. This makes it particularly fast for options with a limited number of exercise periods, like Bermudan options, and options for which the optimal exercise schedule is known ex ante. As compared to Monte Carlo simulations, our scheme processes an entire cross section of options in a single execution. It thereby offers an immediate solution to the estimation of hedging coefficients through finite differences and is ideal when multiple related options need to be analyzed. The algorithm is also capable of dealing with discrete dividends with no performance deterioration, thus improving on the documented inefficiency of exercise policies under continuous dividend yield approximations. We benchmark our algorithm under both the canonical model of Black and Scholes and the stochastic volatility model of Heston in the presence of discrete dividends. We illustrate the massive improvement of complexity scaling over dense grids with a basket option study including up to eight underlying assets. We show how the high degree of parallelism of our scheme makes it suitable for deployment on massively parallel computing units to scale to higher dimensions or further speed up the solution process.

Scheduling parallelizable imprecise computations on multiprocessors

Abstract: In a real-time application that supports imprecise computation, each task is logically composed of a hard task and a soft task. The hard task must be completed before its deadline. The soft task is an optional task which may not be executed to completion, if insufficient computational resources are available. In the presented model, each task may be parallelized and executed on multiple processors with a multiprocessing overhead which is assumed to be a linear function of the degree of parallelism. It is shown that the problem of time allocation in such a real-time application can be formulated and solved as a linear programming problem. An algorithm is given for constructing a multiprocessor schedule from the linear programming solution. This algorithm guarantees the multiprocessing overhead generated in the multiprocessor schedule not to exceed a linear upper bound. >

Parallel gene splicing method based on De Bruijn graph

Abstract: The invention relates to the technical field of gene sequencing, and provides a parallel gene splicing method based on a De Bruijn graph. The parallel gene splicing method based on the De Bruijn graph comprises the following steps that S1, the distributed De Bruijn graph is built in parallel; S2, error paths are removed; S3, the De Bruijn graph is simplified on the base of a depth graph traversal method; S4, contig is combined, and scaffold is generated; S5, the scaffold is output. The parallel gene splicing method is based on a trunking system, the De Bruijn graph is built in parallel, and the problems that when large genomes are spliced, as the data volume of the large genomes is too large, graphs cannot be built and further processing cannot be executed in traditional single-machine serial gene splicing algorithms are solved. Meanwhile, in the simplifying process, parallel simplification based on depth graph traversal is carried out, the graph simplifying process is simple, the degree of parallelism is high, and the splicing speed is high.