Taichi Lang: High-performance Parallel Programming in PythonTaichi is a domain-specific language embedded in Python that helps you easily write portable, high-performance parallel programs.
Mitsuba 3 - A Retargetable Forward and Inverse Renderer
Mitsuba 3 is a research-oriented retargetable rendering system, written in portable C++17 on top of the Dr.Jit Just-In-Time compiler. It is developed by the Realistic Graphics Lab at EPFL. It can be compiled into many variants with optional derivative tracking, dynamic compilation via LLVM or CUDA, and various radiance representations (monochrome, RGB, or spectral light, potentially with polarizat
Numba: High-Performance Python with CUDA Acceleration | NVIDIA Technical Blog
Numba is a Python compiler from Anaconda that can compile Python code for execution on CUDA-capable GPUs or multicore CPUs, providing a way to achieve high-performance computing while maintaining Python's flexibility.By using Numba's Just-in-Time (JIT) compilation and function decorators like @vectorize, developers can easily accelerate their Python code on GPUs without needing to rewrite it in an
pycuda 2025.1.2 documentation
Welcome to PyCUDA’s documentation!¶ PyCUDA gives you easy, Pythonic access to Nvidia’s CUDA parallel computation API. Several wrappers of the CUDA API already exist–so why the need for PyCUDA? Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it w
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GitHub - cupy/cupy: NumPy & SciPy for GPU
ソニーがディープラーニング用“コアライブラリ”をオープンソース化
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