HPC Blogs#
Introducing 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.
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.
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.
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
Measuring Max-Achievable FLOPs – Part 2
AMD measures Max-Achievable FLOPS through controlled benchmarking: real-world data patterns, thermally stable devices, and cold cache testing—revealing how actual performance differs from theoretical peaks.
Understanding Peak, Max-Achievable & Delivered FLOPs, Part 1
Understanding Peak, Max-Achievable & Delivered FLOPs
Deep dive into the MI300 compute and memory partition modes
This blog explains how to use the MI300 compute and memory partitioning modes to optimize your performance-critical applications.