Most Machine Learning (ML) engineers use single precision (FP32) datatype for developing ML models. TensorFloat32 (TF32) has recently become popular as a drop-in replacement for these FP32 based models. However, there is a pressing need to provide additional performance gains for these models by using faster datatypes (such as BFloat16 (BF16)) without requiring additional code changes.

AMD is excited to announce the release of the AMD ROCm™ 5.7. With AMD ROCm open software platform built for flexibility and performance, the HPC and AI communities can gain access to open compute languages, compilers, libraries and tools designed to accelerate code development and solve the toughest challenges in the world today. The latest version of the AMD ROCm platform adds new functionality while building on your favorite features from ROCm 5.7 and other previous releases. Here we will be introducing HIPTensor and highlighting some of our favorite and newly enhanced features in the rocRAND RNG, and MIGraphX libraries. If you are interested in a more in-­depth look at ROCm 5.7, we encourage you to check out the release notes.

Learn how AMD is working with Devito using high performance computing to help break down barriers to accurately model and predict the location of valuable resources in the oil and gas industry. 

AMD's open-source HIP API helps developers of GPU-accelerated applications like renderers and simulation tools write code that runs on AMD and NVIDIA hardware. Now the new HIP SDK makes it possible for anyone to use it.

This book aims to provide helpful guidance to GPU programmers looking to develop HIP programs for the ROCm platform using GPUs. The reader of this book will learn how to reason through real-world problems and break them down into independent parts so tha

AI is the defining technology shaping the next generation of computing. Learn how the latest release of the AMD ROCm open software platform enhances AI capabilities with new software add-ons for large language (and other) models.