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World Congress 2025 β€’ Aug 20, 2025 β€’ Session details

Accelerating Python on GPUs

Paul Graham

Astrophysicists boosted data processing speeds 39x without writing any low-level C++. Discover how the CUDA Python ecosystem accelerates your workloads using simple library drop-ins.

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#1 about 4 min

Evolution of general purpose GPU computing and Python

An overview of why Python developers need native GPU ecosystems and a brief history of GPU computing scaling to AI workloads.

#2 about 3 min

Understanding GPU architecture and massive parallel execution

How streaming multiprocessors and tensor cores enable energy-efficient parallel computations using hundreds of thousands of threads.

#3 about 3 min

Exploring the CUDA ecosystem and levels of abstraction

How developers can access GPU compute power through applications, accelerated libraries, parallel languages, and specialized compilers.

#4 about 3 min

Leveraging domain-specific frameworks and RAPIDS for data science

Accelerating typical data science workflows using drop-in GPU replacements for scikit-learn and pandas without altering existing Python commands.

#5 about 2 min

Replacing NumPy with cuPy for straightforward GPU acceleration

How to achieve significant performance gains by replacing standard CPU mathematical array operations with a seamless GPU alternative.

#6 about 2 min

Accelerating math operations and kernel fusion with nvmath-python

Accessing low-level kernel routines directly from Python to optimize array transformations and scale across platforms.

#7 about 4 min

Real-world example of supernova identification using Python libraries

How domain scientists reduced deep-space image processing time from 45 minutes to one minute using GPU libraries.

#8 about 2 min

Upcoming tools for array-based programming and core compute libraries

Emerging solutions designed to abstract thread management by offering purely array-based logic and native Python access to core routines.

#9 about 3 min

Scaling performance across multiple GPUs using specialized frameworks

Transitioning high-performance code from a single machine to a multi-node cluster with topology-aware communication algorithms.

#10 about 2 min

Profiling and debugging GPU code with Nsight developer tools

Identifying performance bottlenecks and hardware interaction issues using dedicated code profilers and sanitization utilities.

#11 about 2 min

Accessing educational resources and the accelerated compute hub

How to apply these GPU techniques practically through interactive labs, free developer courses, and cloud-based notebooks.

Matching moments

4:37 min

Simplifying parallel programming with the CUDA ecosystem

Paul Graham Paul Graham Β· LIVE

2:15 min

Introduction to CUDA and general-purpose GPU computing

Paul Graham Paul Graham Β· WWC Europe 2026

1:30 min

Exploring the Python-enabled GPU programming software stack

Paul Graham Paul Graham Β· WWC Europe 2026

1:49 min

Accelerating script execution with CuPy and Numba kernels

Paul Graham Paul Graham Β· WWC Europe 2026

1:37 min

Accelerating compute with focused developer tools

Julia Koch Julia Koch +1 Β· WWC Europe 2026

1:11 min

The expanded CUDA ecosystem and native Python support

Paul Graham Paul Graham Β· WWC Europe 2026

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