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Update clad and idd projects for compiler-research (#1683)
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_gsocproposals/2025/proposal_Clad-PyTorch.md

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## Mentors
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* **[Vassil Vassilev](mailto:[email protected])**
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* [Christina Koutsou](mailto:@[email protected])
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* [David Lange](mailto:[email protected])
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## Links
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* [Repo](https://github.com/vgvassilev/clad)

_gsocproposals/2025/proposal_Clad-ThrustAPI.md

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* Clang frontend
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## Mentors
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* **[Christina Koutsou](mailto:@[email protected])**
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* [Vassil Vassilev](mailto:vvasilev@cern.ch)
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* **[Vassil Vassilev](mailto:[email protected])**
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* [David Lange](mailto:david.lange@cern.ch)
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## Links
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* [Repo](https://github.com/vgvassilev/clad)
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---
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title: Interactive Differential Debugging - Intelligent Auto-Stepping and Tab-Completion
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layout: gsoc_proposal
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project: CppInterOp
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year: 2025
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difficulty: medium
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duration: 350
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mentor_avail: June-October
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organization:
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- CompRes
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---
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## Description
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Differential debugging is a time-consuming task that is not well supported by existing tools. Existing state-of-the-art tools do not consider a baseline(working) version while debugging regressions in complex systems, often leading to manual efforts by developers to achieve an automatable task.
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The differential debugging technique analyzes a regressed system and identifies the cause of unexpected behaviors by comparing it to a previous version of the same system. The idd tool inspects two versions of the executable – a baseline and a regressed version. The interactive debugging session runs both executables side-by-side, allowing the users to inspect and compare various internal states.
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This project aims to implement intelligent stepping (debugging) and tab completions of commands. IDD should be able to execute until a stack frame or variable diverges between the two versions of the system, then drop to the debugger. This may be achieved by introducing new IDD-specific commands. IDD should be able to tab complete the underlying GDB/LLDB commands. The contributor is also expected to set up the necessary CI infrastructure to automate the testing process of IDD.
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## Expected Results
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* Enable stream capture
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* Enable IDD-specific commands to execute until diverging stack or variable value.
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* Enable tab completion of commands.
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* Set up CI infrastructure to automate testing IDD.
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* Present the work at the relevant meetings and conferences.
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## Requirements
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* Python & C/C++ programming
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* Familiarity debugging with GDB/LLDB
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## Mentors
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* **[Vipul Cariappa](mailto:[email protected])**
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* [Martin Vasilev](mailto:[email protected])
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## Links
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* [IDD Repository](https://github.com/compiler-research/idd)

gsoc/2025/mentors.md

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* Mateusz Fila [[email protected]](mailto:[email protected]) CERN
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* Chris Gutschow [[email protected]](mailto:[email protected]) UCLondon
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* Aaron Jomy [[email protected]](mailto:[email protected]) CERN/CompRes
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* Christina Koutsou [[email protected]](mailto:@[email protected]) CompRes
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* Stephan Lachnit [[email protected]](mailto:[email protected]) DESY
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* David Lange [[email protected]](mailto:[email protected]) CompRes
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* Serguei Linev [[email protected]](mailto:[email protected]) GSI

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