Paul Graham

Accelerating Python on GPUs

Is your Python code hitting a performance wall? Learn how to leverage the massive parallelism of GPUs with minimal code changes.

Accelerating Python on GPUs
#1about 2 minutes

The rise of general-purpose GPU computing

NVIDIA's evolution from a graphics hardware company to a leader in general-purpose computing was accelerated by the use of GPUs for AI with models like AlexNet.

#2about 4 minutes

Why GPUs outperform CPUs for parallel tasks

As single-threaded CPU performance plateaued, GPUs offered a path forward with their massively parallel architecture designed for simultaneous computation.

#3about 6 minutes

Understanding modern GPU architecture and operation

GPUs work with CPUs by offloading compute-intensive code and use thousands of threads to hide memory latency, leveraging streaming multiprocessors and high-bandwidth memory.

#4about 7 minutes

Introducing the CUDA parallel computing platform

The CUDA platform is a complete ecosystem with compilers, libraries, and frameworks that enables developers to program GPUs using various languages and abstraction levels.

#5about 3 minutes

Leveraging specialized hardware like Tensor Cores

Specialized hardware like Tensor Cores can be used transparently through high-level libraries like cuDNN or programmed directly with low-level APIs for maximum performance.

#6about 6 minutes

High-level frameworks for domain-specific acceleration

Frameworks like Rapids provide GPU-accelerated, drop-in replacements for popular data science libraries such as Pandas (cuDF) and NetworkX (cuGraph) with minimal code changes.

#7about 10 minutes

A progressive approach to programming GPUs in Python

Developers can choose from a spectrum of Python libraries, from simple drop-in replacements like CuNumeric and CuPy to JIT compilers like Numba and direct kernel programming with PyCUDA.

#8about 6 minutes

Developer tools and learning resources for GPUs

NVIDIA offers a comprehensive suite of developer tools for profiling and debugging, along with learning resources like the NGC repository, DLI courses, and community events.

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