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![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Application portability with HIP – AMD lab notes
This blog discusses various ROCm tools developers can leverage to port existing applications from CUDA to HIP.
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
C++17 parallel algorithms and HIPSTDPAR – AMD lab notes
This post discusses how to leverage C++17 parallel algorithms on AMD GPUs with HIPSTDPAR
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Affinity part 2 – System topology and controlling affinity – AMD lab notes
This second part introduces common tools to understand the topology of your system and to control affinity for different applications
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Affinity part 1 – Affinity, placement, and order – AMD lab notes
This first part introduces the concept of affinity and why its important for achieving better performance on AMD GPU nodes
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Sparse matrix vector multiplication – part 1 – AMD lab notes
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.
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Jacobi Solver with HIP and OpenMP offloading – AMD lab notes
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.
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Creating a PyTorch/TensorFlow Code Environment on AMD GPUs – AMD lab notes
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.
![AMD Lab Notes](https://gpuopen.com/wp-content/uploads/2023/01/AMD_Lab_Notes_White_BG.png)
Finite difference method – Laplacian part 4 – AMD lab notes
In the fourth and final part of Finite Difference Laplacian blog series we cover scaling studies and cache size limitations
![](https://gpuopen.com/wp-content/uploads/2022/08/Compute_Shaders_title_page.png)
Compute Shaders – Game Industry Conference 2021
This talk introduces compute shaders, explaining ideas from a software and hardware perspective, as well as considerations when writing compute shaders.
![Shadow Denoiser](https://gpuopen.com/wp-content/uploads/2021/04/Featured_ShadowDenoiser.jpg)
AMD FidelityFX™ Denoiser
AMD FidelityFX Denoiser is a set of denoising compute shaders which remove artefacts from reflection and shadow rendering.
![](https://gpuopen.com/wp-content/uploads/2020/06/lib-02.jpg)
Optimize Your Engine Using Compute
This talk by AMD’s Lou Kramer at 4C in 2018 discusses optimising your engine using compute.
![](https://gpuopen.com/wp-content/uploads/2020/07/gears_featured.png)
Gears 5 – High-Gear Visuals On Multiple Platforms – YouTube link
This talk will discuss Direct3D® 12 in general, as well as some of the features that were leveraged to accomplish this goal, such as Async Compute, Tiled Resources, Debugging, Copy Queues, and HDR.