HPC - Software Tools & Optimizations - Page 3#
Introducing the AMD Network Operator v1.0.0: Simplifying High-Performance Networking for AMD Platforms
Introducing the AMD Network Operator for automating high-performance AI NIC networking in Kubernetes for AI and HPC workloads
Performance Profiling on AMD GPUs - Part 3: Advanced Usage
Part 3 of our GPU profiling series guides beginners through practical steps to identify and optimize kernel bottlenecks using ROCm tools
ROCm 7.9 Technology Preview: ROCm Core SDK and TheRock Build System
Introduce ROCm Core SDK, and learn to install and build ROCm components easily using TheRock.
GPU Partitioning Made Easy: Pack More AI Workloads Using AMD GPU Operator
What’s New in AMD GPU Operator: Learn About GPU Partitioning and New Kubernetes Features
Matrix Core Programming on AMD CDNA™3 and CDNA™4 architecture
This blog post explains how to use Matrix Cores on CDNA3 and CDNA4 architecture, with a focus on low-precision data types such as FP16, FP8, and FP4
Performance Profiling on AMD GPUs – Part 2: Basic Usage
Part 2 of our GPU profiling series guides beginners through practical steps to identify and optimize kernel bottlenecks using ROCm tools
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.
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
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.