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Reverse-mode automatic differentiation and optimization of GPU kernels via Enzyme
Computing derivatives is key to many algorithms in scientific computing and machine
learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a …
learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a …
An introduction to algorithmic differentiation
Algorithmic differentiation (AD), also known as automatic differentiation, is a technology for
accurate and efficient evaluation of derivatives of a function given as a computer model. The …
accurate and efficient evaluation of derivatives of a function given as a computer model. The …
Optimal memory-aware backpropagation of deep join networks
Deep learning training memory needs can prevent the user from considering large models
and large batch sizes. In this work, we propose to use techniques from memory-aware …
and large batch sizes. In this work, we propose to use techniques from memory-aware …
Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
J Herrmann, O Beaumont, L Eyraud-Dubois… - ar** schemes
We consider checkpointing strategies that minimize the number of recomputations needed
when performing discrete adjoint computations using multistage time-step** schemes that …
when performing discrete adjoint computations using multistage time-step** schemes that …
Asynchronous two-level checkpointing scheme for large-scale adjoints in the spectral-element solver Nek5000
Adjoints are an important computational tool for large-scale sensitivity evaluation,
uncertainty quantification, and derivative-based optimization. An essential component of …
uncertainty quantification, and derivative-based optimization. An essential component of …
Bathymetric influences on Antarctic Ice‐Shelf melt rates
Ocean bathymetry exerts a strong control on ice sheet‐ocean interactions within Antarctic
ice‐shelf cavities, where it can limit the access of warm, dense water at depth to the …
ice‐shelf cavities, where it can limit the access of warm, dense water at depth to the …
Reducing memory requirements of quantum optimal control
Quantum optimal control problems are typically solved by gradient-based algorithms such
as GRAPE, which suffer from exponential growth in storage with increasing number of qubits …
as GRAPE, which suffer from exponential growth in storage with increasing number of qubits …
Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory
Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is
usually performed on GPUs with limited memory capacities. This may force data scientists to …
usually performed on GPUs with limited memory capacities. This may force data scientists to …
Reducing memory requirements of unsteady adjoint by synergistically using check‐pointing and compression
The unsteady adjoint method used in gradient‐based optimization in 2D and, particularly,
3D industrial problems modeled by unsteady PDEs may have significant storage …
3D industrial problems modeled by unsteady PDEs may have significant storage …