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‎_activities/gsoc.md‎

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@@ -15,10 +15,10 @@ and analyze petabytes of data from high-energy physics experiments, such as thos
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hosted at the CERN laboratory in Geneva, Switzerland.
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Some of the questions that we collectively ask are:
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- what are the fundamental blocks that make up our Universe?
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- what is the nature of dark matter and dark energy?
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- what is the nature of the asymmetry between matter and antimatter?
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- what was early Universe like?
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- What are the fundamental blocks that make up our Universe?
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- What is the nature of dark matter and dark energy?
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- What is the nature of the asymmetry between matter and antimatter?
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- What was early Universe like?
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To answer these questions, particle physicists build software to simulate and analyze what happens in particle physics detectors.
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‎_gsocprojects/2025/project_JuliaHEP.md‎

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---
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project: JuliaHEP
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title: JuliaHEP
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layout: default
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logo: juliahep/juliaheplogo.png
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description: |
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The [JuliaHEP](https://hepsoftwarefoundation.org/activities/juliahep.html) working group brings together a community of developers and users of Julia in Particle Physics, with the aim of improving the sharing of knowledge and expertise, as well as unify effort in developing Julia packages useful for the community.
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---
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{% include gsoc_project.ext %}

‎_gsocproposals/2025/proposal_Clad-ImproveTape.md‎

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---
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title: Implement and improve an efficient, layered tape with prefetching capabilities
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layout: gsoc_proposal
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project: Clad
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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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In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. Automatic differentiation is an alternative technique to Symbolic differentiation and Numerical differentiation (the method of finite differences). Clad is based on Clang which provides the necessary facilities for code transformation. The AD library can differentiate non-trivial functions, to find a partial derivative for trivial cases and has good unit test coverage.
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The most heavily used entity in AD is a stack-like data structure called a tape. For example, the first-in last-out access pattern, which naturally occurs in the storage of intermediate values for reverse mode AD, lends itself towards asynchronous storage. Asynchronous prefetching of values during the reverse pass allows checkpoints deeper in the stack to be stored furthest away in the memory hierarchy. Checkpointing provides a mechanism to parallelize segments of a function that can be executed on independent cores. Inserting checkpoints in these segments using separate tapes enables keeping the memory local and not sharing memory between cores. We will research techniques for local parallelization of the gradient reverse pass, and extend it to achieve better scalability and/or lower constant overheads on CPUs and potentially accelerators. We will evaluate techniques for efficient memory use, such as multi-level checkpointing support. Combining already developed techniques will allow executing gradient segments across different cores or in heterogeneous computing systems. These techniques must be robust and user-friendly, and minimize required application code and build system changes.
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This project aims to improve the efficiency of the clad tape and generalize it into a tool-agnostic facility that could be used outside of clad as well.
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## Expected Results
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* Optimize the current tape by avoiding re-allocating on resize in favor of using connected slabs of array
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* Enhance existing benchmarks demonstrating the efficiency of the new tape
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* Add the tape thread safety
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* Implement multilayer tape being stored in memory and on disk
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* [Stretch goal] Support cpu-gpu transfer of the tape
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* [Stretch goal] Add infrastructure to enable checkpointing offload to the new tape
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* [Stretch goal] Performance benchmarks
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## Requirements
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* Automatic differentiation
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* C++ programming
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* Clang frontend
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## Mentors
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* **[Vassil Vassilev](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_CppInterop-API-ExposeMemory.md‎

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---
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title: Implement CppInterOp API exposing memory, ownership and thread safety information
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layout: gsoc_proposal
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project: Cppyy
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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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Incremental compilation pipelines process code chunk-by-chunk by building an ever-growing translation unit. Code is then lowered into the LLVM IR and subsequently run by the LLVM JIT. Such a pipeline allows creation of efficient interpreters. The interpreter enables interactive exploration and makes the C++ language more user friendly. The incremental compilation mode is used by the interactive C++ interpreter, Cling, initially developed to enable interactive high-energy physics analysis in a C++ environment.
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Clang and LLVM provide access to C++ from other programming languages, but currently only exposes the declared public interfaces of such C++ code even when it has parsed implementation details directly. Both the high-level and the low-level program representation has enough information to capture and expose more of such details to improve language interoperability. Examples include details of memory management, ownership transfer, thread safety, externalized side-effects, etc. For example, if memory is allocated and returned, the caller needs to take ownership; if a function is pure, it can be elided; if a call provides access to a data member, it can be reduced to an address lookup. The goal of this project is to develop API for CppInterOp which are capable of extracting and exposing such information AST or from JIT-ed code and use it in cppyy (Python-C++ language bindings) as an exemplar. If time permits, extend the work to persistify this information across translation units and use it on code compiled with Clang.
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## Project Milestones
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* Collect and categorize possible exposed interop information kinds
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* Write one or more facilities to extract necessary implementation details
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* Design a language-independent interface to expose this information
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* Integrate the work in clang-repl and Cling
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* Implement and demonstrate its use in cppyy as an exemplar
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* Present the work at the relevant meetings and conferences.
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## Requirements
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* C++ programming
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* Python programming
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* Knowledge of Clang and LLVM
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## Mentors
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* **[Vassil Vassilev](mailto:[email protected])**
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* [Aaron Jomy](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/compiler-research/CppInterOp)

‎_gsocproposals/2025/proposal_JuliaHepMC3.md‎

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---
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title: Julia Interfaces to HepMC3
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layout: gsoc_proposal
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project: JuliaHEP
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year: 2025
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organization:
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- CERN
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difficulty: medium
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duration: 175
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mentor_avail: June-July- August
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---
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## Description
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In high-energy physics experiments at [CERN](https://home.cern/) it is necessary to simulate physics events in order to compare predicted observations with those that the LHC experiments actually observe. A key piece of the software chain used to do that is the [HepMC3](https://arxiv.org/abs/1912.08005) event record library, which encodes the output from physics event generators in a standard way, so that they can be used by downstream detector simulation and analysis codes.
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There is now [increasing interest](https://doi.org/10.1007/s41781-023-00104-x) in using [Julia](https://julialang.org/) as a language for HEP software, as it combines the ease of programming in interactive languages, e.g., Python, with the speed of compiled language, such as C++. As part of building up the ecosystem of supporting packages for Julia in high-energy physics, developing interfaces to read, manipulated and write HepMC3 event records in Julia is the aim of this project.
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## Task ideas
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This project would develop a wrapper library for [HepMC3](https://gitlab.cern.ch/hepmc/HepMC3) allowing the HepMC3 data objects and methods, in C++, to be called from Julia.
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It would utilise the general underlying wrapper interfaces in [CxxWrap](https://github.com/JuliaInterop/CxxWrap.jl) and the automated wrapper code generator [WrapIt!](https://github.com/grasph/wrapit) to allow for as easy and maintainable an interface as possible.
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A key outcome would be a set of unit tests and examples, based on the HepMC3 ones, demonstrating how to use the library and proving that the code is correct.
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## Expected results and milestones
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- Reading of HepMC3 event files
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- Particularly the ASCII format will be targeted first
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- Access to event data structures
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- Access to particle properties
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- Navigation of the event and the vertices between parent and child particles
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- Access to run information
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- Update of HepMC3 data structures
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- Creation of new HepMC3 events
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- Re-serialisation of these events to file
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- Initially ASCII
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- Documentation and examples on how to use the Julia interfaces
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- HepMC3.jl package registered in the Julia general registry
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- Extension of serialisation to ROOT format (stretch goal)
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## Requirements
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- Programming experience in C++
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- Prior experience in Julia (very advantageous)
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- A background understanding of high-energy physics (advantageous)
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## Evaluation Exercise
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TBD
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## Mentors
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- **[Graeme Stewart](mailto:[email protected])**
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- [Mateusz Fila](mailto:[email protected])
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## Links
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- [Julia Programming Language](https://julialang.org/)
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- [JuliaHEP HSF Group](https://hepsoftwarefoundation.org/workinggroups/juliahep.html)
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- [HepMC3 Repository](https://gitlab.cern.ch/hepmc/HepMC3)
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- [CxxWrap](https://github.com/JuliaInterop/CxxWrap.jl)
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- [WrapIt!](https://github.com/grasph/wrapit)

‎gsoc/2025/mentors.md‎

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* Martin Barisits [[email protected]](mailto:[email protected]) CERN
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* Andy Buckley [[email protected]](mailto:[email protected]) UofGlasgow
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* Vipul Cariappa [[email protected]](mailto:[email protected]) CompRes
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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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* Giacomo Parolini [[email protected]](mailto:[email protected]) CERN
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* Alexander Penev [[email protected]](mailto:[email protected]) CompRes/University of Plovdiv, BG
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* Mayank Sharma [[email protected]](mailto:[email protected]) UMich
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* Simon Spannagel [[email protected]](mailto:[email protected]) DESY
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* Graeme Stewart [[email protected]](mailto:[email protected]) CERN
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* Martin Vasilev [[email protected]](mailto:[email protected]) University of Plovdiv, BG
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* Vassil Vassilev [[email protected]](mailto:[email protected]) CompRes
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* Valentin Volkl [[email protected]](mailto:[email protected]) CERN

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