|
| 1 | +- name: "ROOT Superbuilds" |
| 2 | + description: | |
| 3 | + [ROOT](https://root.cern/) is a framework for data processing, |
| 4 | + born at CERN, at the heart of the research on high-energy physics. |
| 5 | + Every day, thousands of physicists use ROOT applications to analyze their |
| 6 | + data or to perform simulations. The ROOT software framework is |
| 7 | + foundational for the HEP ecosystem, providing capabilities such |
| 8 | + as IO, a C++ interpreter, GUI, and math libraries. It uses |
| 9 | + object-oriented concepts and build-time modules to layer between |
| 10 | + components. We believe additional layering formalisms will benefit |
| 11 | + ROOT and its users. |
| 12 | +
|
| 13 | + Currently, ROOT is built as all-in-one package. We are working to create |
| 14 | + a modular version of ROOT that provides a minimal base install of core |
| 15 | + features, then later add functionality via incremental builds. This |
| 16 | + requires introducing new layering mechanisms and extending the functionality |
| 17 | + of the existing ROOT package manager prototype. |
| 18 | + |
| 19 | + tasks: | |
| 20 | + * Enhance the existing CMake build system rules to enable lazy building of packages |
| 21 | + * Bootstrap “RootBase” |
| 22 | + * Demonstrate “layered” lazy builds |
| 23 | +
|
| 24 | +- name: "Improving performance of BioDynaMo using ROOT C++ Modules" |
| 25 | + description: | |
| 26 | + [ROOT](https://root.cern/) is a framework for data processing, |
| 27 | + born at CERN, at the heart of the research on high-energy physics. |
| 28 | + Every day, thousands of physicists use ROOT applications to analyze their |
| 29 | + data or to perform simulations. The ROOT software framework is |
| 30 | + foundational for the HEP ecosystem, providing capabilities such |
| 31 | + as IO, a C++ interpreter, GUI, and math libraries. It uses |
| 32 | + object-oriented concepts and build-time modules to layer between |
| 33 | + components. We believe additional layering formalisms will benefit |
| 34 | + ROOT and its users. |
| 35 | +
|
| 36 | + BioDynaMo is an agent-based simulation platform that enables users |
| 37 | + to perform simulations of previously unachievable scale and complexity, |
| 38 | + making it possible to tackle challenging scientific research questions. |
| 39 | + The project has a wide range of applications in cancer research, |
| 40 | + epidemiology, and social sciences. |
| 41 | +
|
| 42 | + BioDynaMo incorporates ROOT for several crucial functionalities such |
| 43 | + as statistical analysis, random number generation, C++-based Jupyter |
| 44 | + notebooks, and IO. Some features rely on efficient reflection |
| 45 | + information about BioDynaMo’s and user-defined C++ classes. This project |
| 46 | + is about improving the performance of the reflection system by upgrading |
| 47 | + to C++ modules. |
| 48 | +
|
| 49 | + tasks: | |
| 50 | + * Rework the cmake rules to incorporate efficiently ROOT via `FetchContent` |
| 51 | + * Replace invocations of `genreflex` in favor of `rootcling` |
| 52 | + * Enable C++ modules in `rootcling` |
| 53 | + * Produce a comparison report |
| 54 | +
|
| 55 | +- name: "Using ROOT in the field of genome sequencing" |
| 56 | + description: | |
| 57 | + [ROOT](https://root.cern/) is a framework for data processing, |
| 58 | + born at CERN, at the heart of the research on high-energy physics. |
| 59 | + Every day, thousands of physicists use ROOT applications to analyze their |
| 60 | + data or to perform simulations. The ROOT software framework is |
| 61 | + foundational for the HEP ecosystem, providing capabilities such |
| 62 | + as IO, a C++ interpreter, GUI, and math libraries. It uses |
| 63 | + object-oriented concepts and build-time modules to layer between |
| 64 | + components. We believe additional layering formalisms will benefit |
| 65 | + ROOT and its users. |
| 66 | +
|
| 67 | + ROOT has broader scientific uses than the field of high energy |
| 68 | + physics. Several studies have shown promising applications of |
| 69 | + the ROOT I/O system in the field of genome sequencing. This |
| 70 | + project is about extending the developed capability in |
| 71 | + [GeneROOT](https://github.com/GeneROOT) and understanding better |
| 72 | + the requirements of the field. |
| 73 | +
|
| 74 | + tasks: | |
| 75 | + * Reproduce the results from previous comparisons against the ROOT master |
| 76 | + * Investigate changing the compression strategies |
| 77 | + * Investigate different ROOT file splitting techniques |
| 78 | + * Produce a comparison report |
| 79 | + |
| 80 | +- name: "Implement Differentiating of the Kokkos Framework" |
| 81 | + description: | |
| 82 | + In mathematics and computer algebra, automatic differentiation (AD) is |
| 83 | + a set of techniques to numerically evaluate the derivative of a function |
| 84 | + specified by a computer program. Automatic differentiation is an |
| 85 | + alternative technique to Symbolic differentiation and Numerical |
| 86 | + differentiation (the method of finite differences). Clad is based on |
| 87 | + Clang which provides the necessary facilities for code transformation. |
| 88 | + The AD library can differentiate non-trivial functions, to find a partial |
| 89 | + derivative for trivial cases and has good unit test coverage. |
| 90 | +
|
| 91 | + The Kokkos C++ Performance Portability Ecosystem is a production level |
| 92 | + solution for writing modern C++ applications in a hardware agnostic way. |
| 93 | + It is part of the US Department of Energies Exascale Project – the leading |
| 94 | + effort in the US to prepare the HPC community for the next generation of |
| 95 | + super computing platforms. The Ecosystem consists of multiple libraries |
| 96 | + addressing the primary concerns for developing and maintaining applications |
| 97 | + in a portable way. The three main components are the Kokkos Core Programming |
| 98 | + Model, the Kokkos Kernels Math Libraries and the Kokkos Profiling and |
| 99 | + Debugging Tools. |
| 100 | +
|
| 101 | + The Kokkos framework is used in several domains including climate modeling |
| 102 | + where gradients are important part of the simulation process. This project |
| 103 | + aims at teaching Clad to differentiate Kokkos entities in a performance |
| 104 | + portable way |
| 105 | +
|
| 106 | + tasks: | |
| 107 | + * Implement common test cases for Kokkos in Clad |
| 108 | + * Add support for Kokkos functors |
| 109 | + * Add support for Kokkos lambdas |
| 110 | + * Incorporate the changes from the [initial Kokkos PR](https://github.com/vgvassilev/clad/pull/783) |
| 111 | + * Enhance existing benchmarks demonstrating effectiveness of Clad for Kokkos |
| 112 | + * [Stretch goal] Performance benchmarks |
| 113 | +
|
1 | 114 | - name: "Integrate a Large Language Model with the xeus-cpp Jupyter kernel" |
2 | 115 | description: | |
3 | 116 | xeus-cpp is a Jupyter kernel for cpp based on the native implementation |
|
0 commit comments