Exploiting parallelism in automatic differentiation
ICS '91 Proceedings of the 5th international conference on Supercomputing
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Parallel programming in OpenMP
Parallel programming in OpenMP
Bringing together automatic differentiation and OpenMP
ICS '01 Proceedings of the 15th international conference on Supercomputing
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
An overview of the BlueGene/L Supercomputer
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Explicit Loop Scheduling in OpenMP for Parallel Automatic Differentiation
HPCS '02 Proceedings of the 16th Annual International Symposium on High Performance Computing Systems and Applications
Exploiting Multiple Levels of Parallelism in OpenMP: A Case Study
ICPP '99 Proceedings of the 1999 International Conference on Parallel Processing
Automatic differentiation of parallel programs
Automatic differentiation of parallel programs
Sourcebook of parallel computing
On-the-fly detection of data races in OpenMP programs
Proceedings of the 2012 Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
Solving a least-squares problem with algorithmic differentiation and OpenMP
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
CUDA-NP: realizing nested thread-level parallelism in GPGPU applications
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
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Today, OpenMP is the de facto standard for portable shared-memory programming supporting multiple levels of parallelism. Unfortunately, most of the current OpenMP implementations are not capable of fully exploiting more than one level of parallelism. With the increasing number of processors available in high-performance computing resources, the number of applications that would benefit from multilevel parallelism is also increasing. Applying automatic differentiation to OpenMP programs is introduced as a new class of OpenMP applications with nested parallelism.