HPC - Software Tools & Optimizations#
Understanding Attention Algorithms and Their Backends for Image and Video Generation
Practical guide to attention backends in ComfyUI on AMD describing how to optimize performance, memory, and stability with the right configuration.
SPIR-V on ROCm: A Portable IR for AMD GPUs
Learn how SPIR-V brings compile-once, specialize-on-device portability to AMD GPUs — with a reproducible HIP benchmark, trade-off analysis, and quick-start guide.
Performance Profiling on AMD GPUs – Part 5: Profiling-Driven Kernel Optimization with an AI Code-Assist Tool
Ready to slash HIP kernel runtimes? See how ROCm profiling + an AI code-assist agent delivered a 28.3× speedup on AMD Instinct MI250.
Porting High-Performance HIP Kernels to FlyDSL
This blog post shows how to port HIP C++ GPU kernels to FlyDSL, AMD's new Python DSL, matching hand-tuned C++ performance with less code.
OpenXLA and JAX - ROCm Support and the State of CI
Learn how OpenXLA and JAX run on AMD ROCm: what landed this year, how every PR is gated on real Instinct hardware, and how to get started.
Performance Profiling on AMD GPUs - Part 4: Fortran OpenMP Offload Edition
Guides developers through profiling and optimizing Fortran OpenMP GPU offload applications using ROCm tools
Deep Dive Into 4-Wave Interleave FP8 GEMM
Learn how to build faster FP8 GEMM kernels on AMD CDNA™4 using 4-wave interleaving to hide memory latency and maximize Matrix Core utilization.
From Naive to Near-Peak: Building High-Performance GEMM Kernels with Gluon
Learn how a Gluon GEMM tutorial teaches profiling-driven AMD GPU optimization from FP16 baseline to BF8 and MXFP4 kernels.
TraceLens: Democratizing AI Performance Analysis
Explore how TraceLens automates profiler trace analysis to pinpoint bottlenecks and optimize AI workloads.
Getting Started with FlyDSL Nightly Wheels on ROCm
A practical guide to installing and using FlyDSL nightly wheels on ROCm for fast, Python-native GPU kernel development
FP8 GEMM Optimization on AMD CDNA™4 Architecture
Learn how to build high-performance FP8 GEMM kernels on AMD CDNA™4 GPUs using MFMA, LDS swizzling, and double-buffering.