Abstract- Wepresent a dependence testing algorithm that considers the short width of modern SIMD registers in a typical microprocessor. The test works by solving the dependence system with the generalized GCD algorithm and then simplifying the solution equations for a particular set of dependence distances. We start by simplifying each solution lattice to generate points that satisfy some small constant dependence distance that corresponds to the width of the register being used. We use the Power Test to efficiently perform Fourier-Motzkin Variable Elimination on the simplified systems in order to determine if dependences exist. The improvements described in this paper also extend our SIMD dependence test to loops with symbolic and triangul...
The huge processing power needed by multimedia applications has led to mul-timedia extensions in the...
The random coding capacity of a vector Gaussian arbitrarily varying channel (VGAVC) is determined, a...
We present a new approach to dependence testing in the presence of induction variables. Instead of l...
Data Dependence Analysis is the foundation of any parallelizing compiler. The GCD test and the Baner...
Dependence analysis is an indispensable tool in the automatic vectorization and parallelization of s...
A simple run-time data dependence test is presented which is based on a new formulation of the depen...
Data dependence testing is the basic step in detecting loop level parallelism in numerical programs....
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/19...
International audienceUsing SIMD instructions is essential in modern processor architecture for high...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
A novel dependence graph representation called the multiple-order dependence graph for nested-loop ...
A parallelizing compiler relies on data dependence analysis to detect independent operations in a us...
Most current data dependence tests cannot handle loop bounds or array subscripts that are symbolic, ...
SIGLECNRS 14802 E / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
To effectively translate real programs written in standard, sequential languages into parallel compu...
The huge processing power needed by multimedia applications has led to mul-timedia extensions in the...
The random coding capacity of a vector Gaussian arbitrarily varying channel (VGAVC) is determined, a...
We present a new approach to dependence testing in the presence of induction variables. Instead of l...
Data Dependence Analysis is the foundation of any parallelizing compiler. The GCD test and the Baner...
Dependence analysis is an indispensable tool in the automatic vectorization and parallelization of s...
A simple run-time data dependence test is presented which is based on a new formulation of the depen...
Data dependence testing is the basic step in detecting loop level parallelism in numerical programs....
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/19...
International audienceUsing SIMD instructions is essential in modern processor architecture for high...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
A novel dependence graph representation called the multiple-order dependence graph for nested-loop ...
A parallelizing compiler relies on data dependence analysis to detect independent operations in a us...
Most current data dependence tests cannot handle loop bounds or array subscripts that are symbolic, ...
SIGLECNRS 14802 E / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
To effectively translate real programs written in standard, sequential languages into parallel compu...
The huge processing power needed by multimedia applications has led to mul-timedia extensions in the...
The random coding capacity of a vector Gaussian arbitrarily varying channel (VGAVC) is determined, a...
We present a new approach to dependence testing in the presence of induction variables. Instead of l...