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Changes after David's review of CHEF-FP Blog Post
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_posts/2023-04-13-floating-point-error-analysis-with-chef-fp.md

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@@ -95,13 +95,12 @@ CHEF-FP's implementation involves registering an API in Clad for calculating
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the floating-point errors in desired functions using the default error
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estimation models.
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While CHEF-FP provides an API that is useful for building simple expressions,
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it becomes challenging to implement complex models based on just a single
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expression. Therefore, calls to external functions are built as a valid error
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model, as long as the function has a compatible return type for the variable
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being assigned the error. This means that users can define their error models
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as regular C++ functions, enabling the implementation of more computationally
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complex models.
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CHEF_FP not only serves as a proof-of-concept, but it also provides APIs for
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building common expressions. For more complex expressions, custom calls to
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external functions can be built as a valid [custom error model], as long as the
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function has a compatible return type for the variable being assigned the
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error. This means that users can define their error models as regular C++
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functions, enabling the implementation of more computationally complex models.
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### Conclusion
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@@ -110,7 +109,11 @@ Error Analysis, guiding developers in optimizing various precision
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configurations for enhanced performance. By utilizing AD techniques and
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source-level insights, CHEF-FP presents a scalable and efficient solution for
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error estimation in HPC applications, paving the way for better computational
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efficiency.
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efficiency. To explore this research, please view the [CHEF-FP examples repository].
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[Fast And Automatic Floating Point Error Analysis With CHEF-FP]: https://arxiv.org/abs/2304.06441
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[custom error model]: https://github.com/vgvassilev/clad/blob/v1.1/demos/ErrorEstimation/CustomModel/README.md
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[CHEF-FP examples repository]: https://github.com/grimmmyshini/chef-fp-examples

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