AMD ROCm™ Blogs
Stone Ridge Technology latest development effort was to port ECHELON from CUDA to the AMD HIP platform, enabling ECHELON to use AMD Instinct GPUs like the MI210, MI250X, and the upcoming MI300 Series
AMD Collaboration with the University of Michigan offers High Performance Open-Source Solutions to the Bioinformatics Community
Siemens taps AMD Instinct™ GPUs to expand high-performance hardware options for Simcenter STAR-CCM+
In this blog, we will show how to leverage PyTorch TunableOp to accelerate models using ROCm on AMD GPUs.
In this blog, we illustrate the process of implementing and training a Generative Pre-trained Transformer (GPT) model in JAX.
In this blog, we delve into the Mamba architecture and demonstrate how to use Mamba on AMD GPUs with the ROCm platform.
In this blog, we demonstrate how to build a simple Deep Learning Recommendation Model (DLRM) with PyTorch on a ROCm-capable AMD GPU.
The Segment Anything Model (SAM) is a cutting-edge image segmentation model that democratizes promptable segmentation.
Panoptic segmentation and instance segmentation with Detectron2 on AMD GPUs.
The AMD ROCm™ Composable Kernel (CK) library provides a programming model for writing performance-critical kernels for machine learning workloads.
Rocprof is a robust tool designed to analyze and optimize the performance of HIP programs on AMD ROCm platforms
In this blog post, we will discuss how to read and understand the ISA for AMD’s Graphics Core Next (AMDGCN) architecture
HIP enables these High-Performance Computing (HPC) facilities to transition their CUDA codes to run and take advantage of the latest AMD GPUs
The C++17 standard added the concept of parallel algorithms to the pre-existing C++ Standard Library
Affinity is a way for processes to indicate preference of hardware components so that a given process is always scheduled to the same set of compute cores and is able to access data from local memory efficiently