DeepSeek open-sources Huawei chip tools as a simpler alternative to CUDA
‘Independent, self-controlled’: DeepSeek open-sources TileLang and six modules for Huawei’s Ascend chips, offered as an alternative to Nvidia’s CUDA.
cuda
CUDA from First Principles
Understanding Threads, Warps, Streaming Multiprocessors, and the Complete Kernel Execution Pipeline
wafer-ai/gpu-perf-engineering-resources: A curriculum for learning about gpu performance engineering, from scratch to what the frontier AI…
A curriculum for learning about gpu performance engineering, from scratch to what the frontier AI labs do - wafer-ai/gpu-perf-engineering-resources
gralhix #004
just a blog
Hello, CUDA! Full tutorial on getting started from scratch, with profiling!
Profiling 7 Implementations: Why bad CUDA code makes GPUs slower than CPUs
What happens when you run a CUDA kernel
Tracing one vector-add kernel from nvcc all the way down to the warps that execute it.
CUDA for Deep Learning - Elliot Arledge
CUDA (Compute Unified Device Architecture) provides a powerful parallel programming model AI engineers can use to tap the massive processing power of NVIDIA GPUs. CUDA delivers direct control, debugging power, and acceleration at the GPU level that can’t be matched by other…
alternbits/awesome-cuda-books: A curated list of best cuda programming books
A curated list of best cuda programming books
CuPy – NumPy & SciPy for GPU — CuPy 14.0.1 documentation
A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling - MarkTechPost
Note: https://gist.github.com/bjpcjp/c85fa4214c1b59c3aa578a6e8e9d871c
Writing Your First GPU Kernel in Python with Numba and CUDA - KDnuggets
80x Faster Python? Discover How One Line Turns Your Code Into a GPU Beast!
An Even Easier Introduction to CUDA (Updated) | NVIDIA Technical Blog
A quick and easy introduction to CUDA programming for GPUs. This post dives into CUDA C++ with a simple, step-by-step parallel programming example.
No Fooling With Voxel Pooling
Porting functions that don't exist in common graph manipulation tools to an NPU.
Understanding PTX, the Assembly Language of CUDA GPU Computing | NVIDIA Technical Blog
Parallel thread execution (PTX) is a virtual machine instruction set architecture that has been part of CUDA from its beginning. You can think of PTX as the…
nvidia - Installing CUDA on Ubuntu 23.10 - libt5info not installable - Ask
Today I've installed from scratch Ubuntu 23.10 on my computer. After having installed all the software I need, I tried to install CUDA from the NVIDIA website, following their instructions: https://
CUTLASS 2.8
CUDA Templates for Linear Algebra Subroutines.
Dynamic Control Flow in CUDA Graphs with Conditional Nodes
CUDA Graphs can provide a significant performance increase, as the driver is able to optimize execution using the complete description of tasks and dependencies. Graphs provide incredible benefits for…
AMD Quietly Funded A Drop-In CUDA Implementation Built On ROCm: It's Now Op
While there have been efforts by AMD over the years to make it easier to port codebases targeting NVIDIA's CUDA API to run atop HIP/ROCm, it still requires work on the part of developers.
How AMD May Get Across the CUDA Moat
When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically...
Idiot-proof checklist for installation on Ubuntu 22.10? : CUDA
I've tried multiple ways to get CUDA running but can't past the driver recognition problem (at least that's what I think the issue is). Surely…
CUDA Quick Start Guide
Minimal first-steps instructions to get CUDA running on a standard system.
CUDA Toolkit 12.0 Released for General Availability
NVIDIA announces the newest CUDA Toolkit software release, 12.0. This release is the first major release in many years and it focuses on new programming models and CUDA application acceleration…
CUDA by Numba Examples
Follow this series to learn about CUDA programming from scratch with Python. Part 4 of 4.
29 Pytorch Snippets to Speed Up Your Machine Learning Cycle
From creating tensors to writing neural networks
Custom C and CUDA Extensions — PyTorch Tutorials 1.7.0 documentation
drivers - How do you install CUDA 11 on Ubuntu 20.10 and verify the install
There seem be be several options to install CUDA on Ubuntu 20.10: It is pre-bundled with 20.10, there are various installers at the official NVIDIA page, etc. Question: What is a recommended way to
Start Locally | PyTorch
CUDA 11 Features Revealed | NVIDIA Developer Blog
The new NVIDIA A100 GPU based on the NVIDIA Ampere GPU architecture delivers the greatest generational leap in accelerated computing. The A100 GPU has revolutionary hardware capabilities and we’re…
Numba: High-Performance Python with CUDA Acceleration
Numba is an open-source Python compiler from Anaconda that can compile Python code for high-performance execution on CUDA-capable GPUs or multicore CPUs.
RAPIDS cuDF Unified Memory Accelerates pandas up to 30x on Large Datasets
NVIDIA has released RAPIDS cuDF unified memory and text data processing features that help data scientists continue to use pandas when working with larger and text-heavy datasets in demanding…