Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Algorithms
Analysis of interprocedural side effects in a parallel programming environment
Journal of Parallel and Distributed Computing - Special Issue on Languages, Compilers and environments for Parallel Programming
A technique for summarizing data access and its use in parallelism enhancing transformations
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
Supercompilers for parallel and vector computers
Supercompilers for parallel and vector computers
Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++
ACM Transactions on Mathematical Software (TOMS)
Automatic Data Structure Selection and Transformation for Sparse Matrix Computations
IEEE Transactions on Parallel and Distributed Systems
ADIC: an extensible automatic differentiation tool for ANSI-C
Software—Practice & Experience
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Loop Transformations for Restructuring Compilers: The Foundations
Loop Transformations for Restructuring Compilers: The Foundations
Exact versus Approximate Array Region Analyses
LCPC '96 Proceedings of the 9th International Workshop on Languages and Compilers for Parallel Computing
An Exact Method for Analysis of Value-based Array Data Dependences
Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing
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A major difficulty in quickly computing Jacobians by Automatic Differentiation is to deal with the nonzero structures of sparse matrices. We propose to detect the sparsity structure of Jacobians by static program analysis. The method consists in traversing the data dependence graph extended with the control-flow of the program and computing relations between array regions. Then, we safely extract informations about the dependences from program inputs to program outputs. The generation of the derived program uses these informations to produce a better result. We eventually, introduce the Automatic Differentiation tool Odyssée and present some benchmark tests.