Paul Graham
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.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
03:13 MIN
How AI can create more human moments in HR
The Future of HR Lies in AND – Not in OR
05:10 MIN
How the HR function has evolved over three decades
The Future of HR Lies in AND – Not in OR
03:28 MIN
Shifting from talent acquisition to talent architecture
The Future of HR Lies in AND – Not in OR
06:51 MIN
Balancing business, technology, and people for holistic success
The Future of HR Lies in AND – Not in OR
06:04 MIN
The importance of a fighting spirit to avoid complacency
The Future of HR Lies in AND – Not in OR
06:59 MIN
Moving from 'or' to 'and' thinking in HR strategy
The Future of HR Lies in AND – Not in OR
06:10 MIN
Understanding global differences in work culture and motivation
The Future of HR Lies in AND – Not in OR
04:22 MIN
Navigating ambiguity as a core HR competency
The Future of HR Lies in AND – Not in OR
Featured Partners
Related Videos
Accelerating Python on GPUs
Paul Graham
Accelerating Python on GPUs
Paul Graham
CUDA in Python
Andy Terrel
WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA
Ankit Patel
Coffee with Developers - Stephen Jones - NVIDIA
Stephen Jones
Concurrency in Python
Fabian Schindler
Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.
Jodie Burchell
30 Golden Rules of Deep Learning Performance
Anirudh Koul
Related Articles
View all articles



From learning to earning
Jobs that call for the skills explored in this talk.

Ebg Medaustron Gmbh
Wiener Neustadt, Austria
€46K
GIT
NumPy
Python
Pandas
+3

MedAustron EBG
Neustadt an der Weinstraße, Germany
€46K
GIT
NumPy
Python
Pandas
+3


Nvidia
Glasgow, United Kingdom
Senior
C++
Python
PyTorch
Red Hat Enterprise Linux - RHEL



Nvidia
Sheffield, United Kingdom
Senior
C++
Python
PyTorch
Red Hat Enterprise Linux - RHEL

Nvidia
Nottingham, United Kingdom
Senior
C++
Python
PyTorch
Red Hat Enterprise Linux - RHEL

Client Server
Charing Cross, United Kingdom
Remote
£60-70K
CSS
HTML
MySQL
+6