675,533 research outputs found
Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures
Many-core architectures of the future are likely to have distributed memory
organizations and need fine grained concurrency management to be used
effectively. The Self-adaptive Virtual Processor (SVP) is an abstract
concurrent programming model which can provide this, but the model and its
current implementations assume a single address space shared memory. We
investigate and extend SVP to handle distributed environments, and discuss a
prototype SVP implementation which transparently supports execution on
heterogeneous distributed memory clusters over TCP/IP connections, while
retaining the original SVP programming model
Programming the Navier-Stokes computer: An abstract machine model and a visual editor
The Navier-Stokes computer is a parallel computer designed to solve Computational Fluid Dynamics problems. Each processor contains several floating point units which can be configured under program control to implement a vector pipeline with several inputs and outputs. Since the development of an effective compiler for this computer appears to be very difficult, machine level programming seems necessary and support tools for this process have been studied. These support tools are organized into a graphical program editor. A programming process is described by which appropriate computations may be efficiently implemented on the Navier-Stokes computer. The graphical editor would support this programming process, verifying various programmer choices for correctness and deducing values such as pipeline delays and network configurations. Step by step details are provided and demonstrated with two example programs
Using Multiobjective Genetic Programming to Infer Logistic Polynomial Regression Models [and] Experimental Supplement
Abstract. In designing non-linear classifiers, there are important trade-offs to be made between predictive accuracy and model comprehensibility or complexity. We introduce the use of Genetic Programming to generate logistic polynomial models, a relatively comprehensible non-linear parametric model; describe an efficient twostage algorithm consisting of GP structure design and Quasi-Newton coefficient setting; demonstrate that Niched Pareto Multiobjective
Genetic Programming can be used to discover a range of classifiers with different complexity versus âperformanceâ trade-offs; introduce a technique to integrate a new âROC (Receiver Operating Characteristic) dominanceâ concept into the multiobjective setting; and suggest some modifications to the Niched Pareto GA for use in Genetic Programming. The technique successfully generates classifiers with diverse complexity and performance characteristics
Towards a Decoupled Context-Oriented Programming Language for the Internet of Things
Easily programming behaviors is one major issue of a large and reconfigurable
deployment in the Internet of Things. Such kind of devices often requires to
externalize part of their behavior such as the sensing, the data aggregation or
the code offloading. Most existing context-oriented programming languages
integrate in the same class or close layers the whole behavior. We propose to
abstract and separate the context tracking from the decision process, and to
use event-based handlers to interconnect them. We keep a very easy declarative
and non-layered programming model. We illustrate by defining an extension to
Golo-a JVM-based dynamic language
Maximum Principle for Linear-Convex Boundary Control Problems applied to Optimal Investment with Vintage Capital
The paper concerns the study of the Pontryagin Maximum Principle for an
infinite dimensional and infinite horizon boundary control problem for linear
partial differential equations. The optimal control model has already been
studied both in finite and infinite horizon with Dynamic Programming methods in
a series of papers by the same author, or by Faggian and Gozzi. Necessary and
sufficient optimality conditions for open loop controls are established.
Moreover the co-state variable is shown to coincide with the spatial gradient
of the value function evaluated along the trajectory of the system, creating a
parallel between Maximum Principle and Dynamic Programming. The abstract model
applies, as recalled in one of the first sections, to optimal investment with
vintage capital
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