Introducing AMD lab notes – new programming tutorials for HPC and ML
In this blog series, we share the lessons learned from tuning a wide range of scientific applications, libraries, and frameworks for AMD GPUs.
In this blog series, we share the lessons learned from tuning a wide range of scientific applications, libraries, and frameworks for AMD GPUs.
Explore our huge collection of detailed tutorials, sample code, presentations, and documentation to find answers to your graphics development questions.
This second part introduces common tools to understand the topology of your system and to control affinity for different applications
The machine learning ecosystem is quickly exploding and this article is designed to assist data scientists/ML practitioners get their machine learning environments up and running on AMD GPUs.
In the fourth and final part of Finite Difference Laplacian blog series we cover scaling studies and cache size limitations
This blog discusses various ROCm tools developers can leverage to port existing applications from CUDA to HIP.
This post discusses how to leverage C++17 parallel algorithms on AMD GPUs with HIPSTDPAR
This first part introduces the concept of affinity and why its important for achieving better performance on AMD GPU nodes
Sparse matrix vector multiplication (SpMV) is a core computational kernel of nearly every implicit sparse linear algebra solver. This is the first post in the series covering SpMV.
In this blog, we explore GPU offloading using HIP and OpenMP target directives and discuss their relative merits in terms of implementation efforts and performance.
Browse our technical blogs, and find valuable advice on developing with AMD hardware, ray tracing, Vulkan®, DirectX®, Unreal Engine, and lots more.
In this third part, we cover additional optimizations to fine tune the performance of the kernel, and introduce temporary files, register pressure, and occupancy.