Add Cppyy open projects

_data/openprojectlist.yml

@@ -1,3 +1,76 @@
+- name: "Enable CUDA compilation on Cppyy-Numba generated IR"
+  description: |
+  Cppyy is an automatic, run-time, Python-C++ bindings generator, for calling
+  C++ from Python and Python from C++. Initial support has been added that
+  allows Cppyy to hook into the high-performance Python compiler,
+  Numba which compiles looped code containing C++ objects/methods/functions
+  defined via Cppyy into fast machine code. Since Numba compiles the code in
+  loops into machine code it crosses the language barrier just once and avoids
+  large slowdowns accumulating from repeated calls between the two languages.
+  Numba uses its own lightweight version of the LLVM compiler toolkit (llvmlite) 
+  that generates an intermediate code representation (LLVM IR) which is also
+  supported by the Clang compiler capable of compiling CUDA C++ code.
+
+  The project aims to demonstrate Cppyy's capability to provide CUDA paradigms to
+  Python users without any compromise in performance. Upon successful completion
+  a possible proof-of-concept can be expected in the below code snippet -
+
+  ```python
+  import cppyy
+  import cppyy.numba_ext
+
+  cppyy.cppdef('''
+  __global__ void MatrixMul(float* A, float* B, float* out) {
+      // kernel logic for matrix multiplication
+  }
+  ''')
+
+  @numba.njit
+  def run_cuda_mul(A, B, out):
+      # Allocate memory for input and output arrays on GPU
+      # Define grid and block dimensions
+      # Launch the kernel
+      MatrixMul[griddim, blockdim](d_A, d_B, d_out)	
+  ```
+  tasks: |
+     * Add support for declaration and parsing of Cppyy-defined CUDA code on
+     the Numba extension.
+     * Design and develop a CUDA compilation and execution mechanism.
+     * Prepare proper tests and documentation.
+
+- name: "Cppyy STL/Eigen - Automatic conversion and plugins for Python based ML-backends"
+  description: |
+  Cppyy is an automatic, run-time, Python-C++ bindings generator, for calling 
+  C++ from Python and Python from C++. Cppyy uses pythonized wrappers of useful
+  classes from libraries like STL and Eigen that allow the user to utilize them
+  on the Python side. Current support follows container types in STL like
+  std::vector, std::map, and std::tuple and the Matrix-based classes in
+  Eigen/Dense. These cppyy objects can be plugged into idiomatic expressions
+  that expect Python builtin-types. This behaviour is achieved by growing
+  pythonistic methods like `__len__` while also retaining its C++ methods
+  like `size`.
+
+  Efficient and automatic conversion between C++ and Python is essential
+  towards high-performance cross-language support. This approach eliminates
+  overheads arising from iterative initialization such as comma insertion in
+  Eigen. This opens up new avenues for the utilization of Cppyy’s bindings in
+  tools that perform numerical operations for transformations, or optimization.
+
+  The on-demand C++ infrastructure wrapped by idiomatic Python enables new
+  techniques in ML tools like JAX/CUTLASS. This project allows the C++
+  infrastructure to be plugged into at service to the users seeking
+  high-performance library primitives that are unavailable in Python.
+
+  tasks: |
+    * Extend STL support for std::vectors of arbitrary dimensions
+    * Improve the initialization approach for Eigen classes
+    * Develop a streamlined interconversion mechanism between Python
+    builtin-types, numpy.ndarray, and STL/Eigen data structures
+    * Implement experimental plugins that perform basic computational  
+    operations in frameworks like JAX
+    * Work on integrating these plugins with toolkits like CUTLASS that
+    utilise the bindings to provide a Python API
+
 - name: "Enable cross-talk between Python and C++ kernels in xeus-clang-REPL by using Cppyy"
   description: |
     xeus-clang-REPL is a C++ kernel for Jupyter notebooks using clang-REPL as