New page added for Research Area "Automatic Differentiation"

_pages/automatic_differentiation.md

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+---
+title: "Automatic Differentiation"
+layout: gridlay
+excerpt: "Automatic Differentiation is a general and powerful technique
+of computing partial derivatives (or the complete gradient) of a function inputted as a
+computer program."
+sitemap: true
+permalink: /automatic_differentiation
+---
+
+## Automatic differentiation
+
+Automatic Differentiation (AD) is a general and powerful technique for
+computing partial derivatives (or the complete gradient) of a function
+inputted as a computer program.
+
+It takes advantage of the fact that any computation can be represented as a
+composition of simple operations / functions - this is generally represented
+in a graphical format and referred to as the [computation
+graph](https://colah.github.io/posts/2015-08-Backprop/). AD works by
+repeatedly applying the chain rule over this graph.
+
+### Understanding Differentiation in Computing
+
+Efficient computation of gradients is a crucial requirement in the fields of
+scientific computing and machine learning, where approaches like [Gradient
+Descent](https://en.wikipedia.org/wiki/Gradient_descent) are used to
+iteratively converge over the optimum parameters of a mathematical model.
+
+Within the context of computing, there are various methods for
+differentiation:
+
+- **Manual Differentiation**: This consists of manually applying the rules of
+  differentiation to a given function. While straightforward, it can be
+  tedious and error-prone, especially for complex functions.
+
+- **Numerical Differentiation**: This method approximates the derivatives
+  using finite differences. It is relatively simple to implement but can
+  suffer from numerical instability and inaccuracy in its results. It doesn't
+  scale well with the number of inputs in the function.
+
+- **Symbolic Differentiation**: This approach uses symbolic manipulation to
+  compute derivatives analytically. It provides accurate results but can lead
+  to lengthy expressions for large computations. It requires the computer
+  program to be representable in a closed-form mathematical expression, and
+  thus doesn't work well with control flow scenarios (if conditions and loops)
+  in the program.
+
+- **Automatic Differentiation (AD)**: Automatic Differentiation is a general
+  and an efficient technique that works by repeated application of the chain
+  rule over the computation graph of the program. Given its composable nature,
+  it can easily scale for computing gradients over a very large number of
+  inputs.
+
+### Forward and Reverse mode AD
+Automatic Differentiation works by applying the chain rule and merging the
+derivatives at each node of the computation graph. The direction of this graph
+traversal and derivative accumulation results in two approaches:
+
+  - Forward Mode, Tangent Mode: starts the accumulation from the input
+    parameters towards the output parameters in the graph. This means that we
+    apply the chain rule to the inner functions first. That approach
+    calculates derivatives of output(s) with respect to a single input
+    variable.
+
+   ![Forward Mode](/images/ForwardAccumulationAutomaticDifferentiation.png)
+
+  - Reverse Mode, Adjoint Mode: starts at the output node of the graph and moves backward
+  towards all the input nodes. For every node, it merges all paths that
+  originated at that node. It tracks how every node affects one output. Hence,
+  it calculates the derivative of a single output with respect to all inputs
+  simultaneously - the gradient.
+
+  ![Reverse Mode](/images/ReverseAccumulationAutomaticDifferentiation.png)
+
+### Automatic Differentiation in C++
+
+Automated Differentiation implementations are based on [two major techniques]:
+Operator Overloading and Source Code Transformation. Compiler Research Group's
+focus has been on exploring the [Source Code Transformation] technique, which
+involves constructing the computation graph and producing a derivative at
+compile time. 
+
+[The source code transformation approach] enables optimization by retaining
+all the complex knowledge of the original source code. The compute graph is
+constructed during compilation and then transformed to generate the derivative
+code. The drawback of that approach in many implementations is that, it
+typically uses a custom parser to build code representation and produce the
+transformed code. It is difficult to implement (especially in C++), but it is
+very efficient, since many computations and optimizations can be done ahead of
+time.
+
+### Advantages of using Automatic Differentiation
+
+- Automatic Differentiation can calculate derivatives without any [additional
+  precision loss]. 
+
+- It is not confined to closed-form expressions. 
+
+- It can take derivatives of algorithms involving conditionals, loops, and
+  recursion. 
+
+- It can be easily scaled for functions with a very large number of inputs.
+
+### Automatic Differentiation Implementation with Clad - a Clang Plugin
+
+Implementing Automatic Differentiation from the ground up can be challenging.
+However, several C++ libraries and tools are available to simplify the
+process. The Compiler Research Group has been working on [Clad], a C++ library
+that enables Automatic Differentiation using the LLVM compiler infrastructure.
+It is implemented as a plugin for the Clang compiler. 
+
+[Clad] operates on Clang AST (Abstract Syntax Tree) and is capable of
+performing C++ Source Code Transformation. When Clad is given the C++ source
+code of a mathematical function, it can algorithmically generate C++ code for
+the computing derivatives of that function. Clad has comprehensive coverage of
+the latest C++ features and a well-rounded fallback and recovery system in
+place.
+
+**Clad's Key Features**:
+
+- Support for both, Forward Mode and Reverse Mode Automatic Differentiation.
+
+- Support for differentiation of the built-in C input arrays, built-in C/C++
+  scalar types, functions with an arbitrary number of inputs, and functions
+  that only return a single value.
+
+- Support for loops and conditionals.
+
+- Support for generation of single derivatives, gradients, Hessians, and
+  Jacobians.
+
+- Integration with CUDA for GPU programming.
+
+- Integration with Cling and ROOT for high-energy physics data analysis.
+
+### Clad Benchmarks (while using Automatic Differentiation)
+
+[Benchmarks] show that Clad is numerically faster than the conventional
+Numerical Differentiation methods, providing Hessians that are 450x (~dim/25
+times faster). [General benchmarks] demonstrate a 3378x improvement in speed
+with Clad (compared to Numerical Differentiation) based on central
+differences. 
+
+For more information on Clad, please view:
+
+- [Clad - Github Repository](https://github.com/vgvassilev/clad)
+
+- [Clad - ReadTheDocs](https://clad.readthedocs.io/en/latest/)
+
+- [Clad - Video Demo](https://www.youtube.com/watch?v=SDKLsMs5i8s)
+
+- [Clad - PDF Demo](https://indico.cern.ch/event/808843/contributions/3368929/attachments/1817666/2971512/clad_demo.pdf)
+
+- [Clad - Automatic Differentiation for C++ Using Clang - Slides](https://indico.cern.ch/event/1005849/contributions/4227031/attachments/2221814/3762784/Clad%20--%20Automatic%20Differentiation%20in%20C%2B%2B%20and%20Clang%20.pdf)
+
+- [Automatic Differentiation in C++ - Slides](https://compiler-research.org/assets/presentations/CladInROOT_15_02_2020.pdf)
+
+
+
+[Clad]: https://compiler-research.org/clad/
+
+[Benchmarks]: https://compiler-research.org/assets/presentations/CladInROOT_15_02_2020.pdf
+
+[General benchmarks]: https://indico.cern.ch/event/1005849/contributions/4227031/attachments/2221814/3762784/Clad%20--%20Automatic%20Differentiation%20in%20C%2B%2B%20and%20Clang%20.pdf
+
+[additional precision loss]: https://compiler-research.org/assets/presentations/CladInROOT_15_02_2020.pdf
+
+[Source Code Transformation]: https://compiler-research.org/assets/presentations/V_Vassilev-SNL_Accelerating_Large_Workflows_Clad.pdf
+
+[two major techniques]: https://compiler-research.org/assets/presentations/G_Singh-MODE3_Fast_Likelyhood_Calculations_RooFit.pdf
+
+[The source code transformation approach]: https://compiler-research.org/assets/presentations/I_Ifrim-EuroAD21_GPU_AD.pdf

_pages/research.md

@@ -90,7 +90,7 @@ only improves performance but also simplifies code development and debugging
 processes, offering a more efficient alternative to static binding methods.
 
 
-[Automatic Differentiation ↗]: https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2072r0.pdf
+[Automatic Differentiation ↗]: https://compiler-research.org/automatic_differentiation
 
 [Interactive C++]: https://blog.llvm.org/posts/2020-12-21-interactive-cpp-for-data-science/