@@ -95,13 +95,12 @@ CHEF-FP's implementation involves registering an API in Clad for calculating
9595the floating-point errors in desired functions using the default error
9696estimation models.
9797
98- While CHEF-FP provides an API that is useful for building simple expressions,
99- it becomes challenging to implement complex models based on just a single
100- expression. Therefore, calls to external functions are built as a valid error
101- model, as long as the function has a compatible return type for the variable
102- being assigned the error. This means that users can define their error models
103- as regular C++ functions, enabling the implementation of more computationally
104- complex models.
98+ CHEF_FP not only serves as a proof-of-concept, but it also provides APIs for
99+ building common expressions. For more complex expressions, custom calls to
100+ external functions can be built as a valid [ custom error model] , as long as the
101+ function has a compatible return type for the variable being assigned the
102+ error. This means that users can define their error models as regular C++
103+ functions, enabling the implementation of more computationally complex models.
105104
106105### Conclusion
107106
@@ -110,7 +109,11 @@ Error Analysis, guiding developers in optimizing various precision
110109configurations for enhanced performance. By utilizing AD techniques and
111110source-level insights, CHEF-FP presents a scalable and efficient solution for
112111error estimation in HPC applications, paving the way for better computational
113- efficiency.
112+ efficiency. To explore this research, please view the [ CHEF-FP examples repository ] .
114113
115114
116115[ Fast And Automatic Floating Point Error Analysis With CHEF-FP ] : https://arxiv.org/abs/2304.06441
116+
117+ [ custom error model ] : https://github.com/vgvassilev/clad/blob/v1.1/demos/ErrorEstimation/CustomModel/README.md
118+
119+ [ CHEF-FP examples repository ] : https://github.com/grimmmyshini/chef-fp-examples
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