Numba for CUDA GPUs¶ · Overview of External Memory Management · Effects on Deallocation Strategies · Management of other objects · Implementing an EMM Plugin. CUDA Host API · Writing CUDA Kernels · Debugging CUDA Python with...

Numba for CUDA GPUs · Overview · Terminology · Programming model · Writing CUDA Kernels · Introduction · Kernel declaration · Memory management · Data transfer. Writing CUDA Kernels · Overview · Supported Python features in...

Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA ...

The CUDA JIT is a low-level entry point to the CUDA features in Numba. It translates Python functions into PTX code which execute on the CUDA hardware. The jit ...

What is in this course? · Session 1: An introduction to Numba and CUDA Python · Session 2: Typing · Session 3: Porting strategies, performance, interoperability ...

Numba is a just-in-time Python function compiler that exposes a simple interface for accelerating numerically-focused Python functions.

An out-of-tree CUDA target for Numba. This contains an entire copy of Numba's CUDA target (the numba.cuda module), and a mechanism to ensure the code from ...

CUDA has an execution model unlike the traditional sequential model used for programming CPUs. In CUDA, the code you write will be executed by multiple threads ...

Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution ...

Numba exposes many CUDA features, including shared memory. To demonstrate shared memory, let's reimplement a famous CUDA solution for summing a vector which ...