@@ -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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+
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+ [ custom error model ] : https://github.com/vgvassilev/clad/blob/v1.1/demos/ErrorEstimation/CustomModel/README.md
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+
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+ [ CHEF-FP examples repository ] : https://github.com/grimmmyshini/chef-fp-examples
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