AMD ROCm™ Blogs
Stone Ridge Expands Reservoir Simulation Options with AMD Instinct™ Accelerators
Stone Ridge Technology (SRT) pioneered the use of GPUs for high performance reservoir simulation (HPC) nearly a decade ago with ECHELON, its flagship...
AMD Collaboration with the University of Michigan offers High Performance Open-Source Solutions to the Bioinformatics Community
Long read DNA sequencing technology is revolutionizing genetic diagnostics and precision medicine by helping us discover structural variants and assem...
Siemens taps AMD Instinct™ GPUs to expand high-performance hardware options for Simcenter STAR-CCM+
Siemens recently announced that its Simcenter STAR-CCM+ multi-physics computational fluid dynamics (CFD) software now supports AMD Instinct™ GPUs for...
Benchmarking Machine Learning using ROCm and AMD GPUs: Reproducing Our MLPerf Inference Submission
Measuring the performance of new technologies is as old as human history, and often as intriguing. The AMD MLPerf Inference v4.1 submission has three entries for Llama 2 70B. The submission used a fully open-source software stack based on the ROCm platform and vLLM inference engine. Read More >
Performing natural language processing tasks with LLMs on ROCm running on AMD GPUs
In this blog you will learn how to use ROCm, running on AMD’s Instinct GPUs, for a range of popular and useful natural language processing (NLP) tasks, using different large language models (LLMs).
Using AMD GPUs for Enhanced Time Series Forecasting with Transformers
Time series forecasting (TSF) is a key concept in fields such as signal processing, data science, and machine learning (ML).
Inferencing with Grok-1 on AMD GPUs
We demonstrate that the massive Grok-1 model from xAI can run seamlessly on the AMD MI300X GPU accelerator by leveraging the ROCm software platform.
Graph analytics on AMD GPUs using Gunrock
Can AMD GPUs help with graph analytic operations? We will show some cases where GPUs can improve the performance of these valuable algorithms.
Optimizing RoBERTa: Fine-Tuning with Mixed Precision on AMD
In this blog we explore how to fine-tune the Robustly Optimized BERT Pretraining Approach (RoBERTa) large language model, with emphasis on PyTorch’s mixed precision capabilities.
TensorFlow Profiler in practice: Optimizing TensorFlow models on AMD GPUs
TensorFlow Profiler consists of a set of tools designed to measure resource utilization and performance during the execution of TensorFlow models…
SmoothQuant model inference on AMD Instinct MI300X using Composable Kernel
The AMD ROCm™ Composable Kernel (CK) library provides a programming model for writing performance-critical kernels…
Reading AMD GPU ISA
Rocprof is a robust tool designed to analyze and optimize the performance of HIP programs on AMD ROCm platforms…
AMD in Action: Unveiling the Power of Application Tracing and Profiling
Rocprof is a robust tool designed to analyze and optimize the performance of HIP programs on AMD ROCm platforms…
Application portability with HIP
Many scientific applications run on AMD-equipped computing platforms and supercomputers, including Frontier…
C++17 parallel algorithms and HIPSTDPAR
The C++17 standard added the concept of parallel algorithms to the pre-existing C++ Standard Library. The parallel version of algorithms like…