Oct 17, 2010 · Given a loop and an array accessed therein, the goal of our framework is to distribute the array based on a specified distribution for the loop ...

In this paper, we present techniques to incrementally dis- tribute programs written in language frameworks, such as. UPC [11] and X10 [12], that allow ...

Abstract. In the era of mult-core systems, one of the key requirements of achieving better utilization of multiple available cores.

Oct 17, 2010 · Inferring arbitrary distributions for data and computation for OOPSLA 2010 by Soham Sundar Chakraborty et al.

Inferring Arbitrary Distributions for Data and Computation. Soham S. Chakraborty, V K Nandivada, SPLASH Onward!, ACM, 2010.

As with the normal distribution, the first step is to standardize the data. Then we can use Table D to obtain the area under the curve. μ s/√n.

In this first chapter, we introduce these concepts and methods, many of which will be further explored and expanded throughout the rest of the book.

Bayesian inference is a way of making statistical inferences in which the statistician assigns subjective probabilities to the distributions that could ...

For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-.

Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any ...