SIAM Journal on Scientific and Statistical Computing
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Parallel programming in OpenMP
Parallel programming in OpenMP
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
Journal of Computational Physics
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Advances in Automatic Differentiation
Advances in Automatic Differentiation
Parallel Computing: Architectures, Algorithms and Applications - Volume 15 Advances in Parallel Computing
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We describe a strategy for parallelizing a geothermal simulation package using the shared-memory programming model OpenMP. During the code development OpenMP is employed for the direct problem in such a way that, in a subsequent step, the OpenMP-parallelized code can be transformed via automatic differentiation into an OpenMP-parallelized code capable of computing derivatives for the inverse problem. Performance results on a Sun Fire X4600 using up to 16 threads are reported demonstrating that, for the derivative computation, an approach using nested parallelism is more scalable than a single level of parallelism.