HPC Blogs#

Performance Profiling on AMD GPUs – Part 1: Foundations
Part 1 of our GPU profiling series introduces ROCm tools, setup steps, and key concepts to prepare you for deeper dives in the posts to follow.

AMD ROCm: Powering the World's Fastest Supercomputers
Discover how ROCm drives the world’s top supercomputers, from El Capitan to Frontier, and why its shaping the future of scalable, open and sustainable HPC

LLM Quantization with Quark on AMD GPUs: Accuracy and Performance Evaluation
Learn how to use Quark to apply FP8 quantization to LLMs on AMD GPUs, and evaluate accuracy and performance using vLLM and SGLang on AMD MI300X GPUs.

The ROCm Revisited Series
We present our ROCm Revisited Series. Discover ROCm's role in leading edge supercomputing, its growing ecosystem-from HIP, to developer tools-powering AI, HPC, and data science across multi-GPU and cluster systems

ROCm Revisited: Getting Started with HIP
New to HIP? This blog will introduce you to the HIP runtime API, its key concepts and installation and practical code examples to showcase its functionality.

ROCm Revisited: Evolution of the High-Performance GPU Computing Ecosystem
Learn how ROCm evolved to support HPC, AI, and containerized workloads with modern tools, libraries, and deployment options.

HIP 7.0 Is Coming: What You Need to Know to Stay Ahead
Get ready for HIP 7.0—explore key API changes that boost CUDA compatibility and streamline portable GPU development, start preparing your code today.

ROCm 6.4: Breaking Barriers in AI, HPC, and Modular GPU Software
Explore ROCm 6.4's key advancements: AI/HPC performance boosts, enhanced profiling tools, better Kubernetes support and modular drivers, accelerating AI and HPC workloads on AMD GPUs.

Seismic stencil codes - part 1
Seismic Stencil Codes - Part 1: Seismic workloads in the HPC space have a long history of being powered by high-order finite difference methods on structured grids. This trend continues to this day.

Seismic stencil codes - part 2
Seismic Stencil Codes - Part 2: In the previous post, recall that the kernel with stencil computation in the z-direction suffered from low effective bandwidth. This low performance comes from generating substantial amounts of data to movement to global memory.

Seismic stencil codes - part 3
Seismic Stencil Codes - Part 3: In the last two blog posts, we developed a HIP kernel capable of computing high order finite differences commonly needed in seismic wave propagation.

Graph analytics on AMD GPUs using Gunrock
Graph analytics on AMD GPUs using Gunrock

Introducing ROCm-DS: GPU-Accelerated Data Science for AMD Instinct™ GPUs
Accelerate data science with ROCm-DS: AMD’s GPU-optimized toolkit for faster data frames and graph analytics using hipDF and hipGRAPH

Installing ROCm from source with Spack
Install ROCm and PyTorch from source using Spack. Learn how to optimize builds, manage dependencies, and streamline your GPU software stacks.

Introducing ROCprofiler SDK - The Latest Toolkit for Performance Profiling
Discover ROCprofiler SDK – ROCm’s next-generation, unified, scalable, and high-performance profiling toolkit for AI and HPC workloads on AMD GPUs.

Understanding RCCL Bandwidth and xGMI Performance on AMD Instinct™ MI300X
The blog explains the reasons behind RCCL bandwidth limitations and xGMI performance constraints, and provides actionable steps to maximize link efficiency on AMD MI300X
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