In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covaria...

Concurrency issues have been a serious problem in the whole software industry. They very often lead to hard-to-reproduce bugs and thus are very difficult to locate and solve. In this paper, we introduce a novel method for detection of one major category of concurrency problems, data race. This method extracts a Variable Dependence Tree from source code and finds data race by analysis on the Var...

The application of Markov random field models to problems involving spatial data on lattice systems requires decisions regarding a number of important aspects of model structure. Existing exploratory techniques appropriate for spatial data do not provide direct guidance to an investigator about these decisions. We introduce an exploratory quantity that is directly tied to the structure of Marko...

Many abstractions of program dependences have already been proposed such as the Dependence Distance the Dependence Di rection Vector the Dependence Level or the Dependence Cone These di erent abstractions have di erent precision The min imal abstraction associated to a transformation is the abstrac tion that contains the minimal amount of information necessary to decide when such a transformati...

A number of program-analysis problems can be tackled by transforming them into certain kinds of graph-reachability problems in labeled directed graphs. The edge labels can be used to filter out paths that are not of interest: A path P from vertex s to vertex t only counts as a “valid connection” between s and t if the word spelled out by P is in a certain language. Often the languages used for ...

One of the most crucial qualities of an optimizing compiler is its ability to detect when di erent data references access the same storage location. Such references are said to be data-dependent and they impose constraints on the amount of program modi cations the compiler can apply for improving the program's performance. For parallelizing compilers the most important program constructs to inv...

This paper introduces a data dependence decision algorithm, called the Power Test; the Power Test is a combination of Banerjee's Generalized GCD dependence algorithm and the Fourier-Motzkin method t o eliminate variables in a system of inequalities. In addition t o having certain advantages over previous dependence algorithms (such as increased precision, the ability t o handle multiple subscri...

Dependence functions are used to construct joint distributions with fixed marginals. They can shed light on relationships among associated random variables. Many dependence functions have been proposed and standardized. However, there has not been an attempt to understand why certain dependence functions are used and what makes certain dependence functions better than others in solving practica...