What if your Python code could achieve over 90% of a GPU's theoretical max performance? Learn how NVIDIA is making it possible.
#1about 6 minutes
Understanding the CUDA platform stack for Python developers
The CUDA platform is layered from high-level domain libraries to low-level hardware access, with new tools aiming to combine Python's productivity with GPU performance.
#2about 3 minutes
Improving performance by fusing GPU operations
The nvmath-python library enables kernel fusion using epilogues, which combines multiple operations like matrix multiplication and bias addition into a single GPU kernel launch.
#3about 5 minutes
Calling device-side functions directly from Python kernels
Python kernels can now directly call pre-compiled, high-performance device-side functions from libraries like cuBLAS, enabled by a just-in-time linker called nvJitLink.
#4about 2 minutes
Fine-grained parallelism with cooperative groups in Python
The CUB library is exposed to Python, allowing for cooperative operations and reductions at the block or warp level for fine-grained control over GPU parallelism.
#5about 3 minutes
Accelerating language support with numba-cuda and nupack
The numba-cuda module is separated to accelerate feature delivery, while nupack automatically generates Python bindings for C++ templated code.
#6about 4 minutes
A Pythonic object model for host-side GPU control
A new high-level object model allows Python developers to directly manage GPU resources like devices, contexts, streams, and linker objects without boilerplate code.
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