Posts tagged Scientific Computing
Running Variational Quantum Eigensolver with Qiskit Aer on AMD Instinct
- 29 May 2026
Quantum computing offers a fundamentally different approach to computational problems by leveraging quantum mechanical properties such as superposition and entanglement. Unlike a classical bit, which is always 0 or 1, a qubit can exist in a superposition of both, and in principle this gives a significant resource advantage: \(n\) qubits represent a state that would otherwise require \(2^n\) complex numbers on a classical computer. However, current quantum hardware is still in its early stages - noise and limited qubit counts constrain the scale of problems it can handle reliably. GPU-accelerated simulators efficiently emulate quantum circuits on classical hardware, though they inherit the same exponential memory cost and become impractical past a few dozen qubits. Of course, any problem whose quantum circuit can be fully simulated on classical hardware can also be handled with other methods that avoid the simulation overhead, but the real value of circuit simulation is the opportunity to develop, validate, and benchmark quantum algorithms in a controlled setting where exact solutions are known, so that the same algorithms can be trusted on future hardware tackling problems that remain intractable at scale today.
Diffusion-based Atmospheric Downscaling on AMD Instinct GPUs
- 20 May 2026
In addition to forecasting, reconstruction is also a commonplace procedure in atmospheric and climate sciences. Reconstruction in this context generally means estimating and infilling missing data, most often due to lack of sensor coverage. The data could be missing spatially or temporally, that is from some regions or at certain times respectively. The data could also be present at some scales and missing at some other scales – for example satellite ice coverage data would have larger scales present but be missing smaller scales due to resolution limitations. Reconstructing these missing smaller scales is called downscaling in climate sciences.
GROMACS on AMD Instinct GPUs: A Complete Build Guide
- 24 March 2026
Molecular dynamics simulations power breakthroughs in drug discovery, materials science, and computational biology. GROMACS stands as one of the most widely used molecular dynamics engines, and pairing it with AMD’s latest GPU accelerators unlocks exceptional simulation throughput. This guide walks you through installing a complete GROMACS stack with OpenMPI support on AMD MI300X and MI355X systems — whether you’re deploying on bare metal or in containers.
Utilizing AMD Instinct GPU Accelerators for Weather and Precipitation Forecasting with NeuralGCM
- 19 March 2026
In recent years, the landscape of weather forecasting has evolved tremendously, employing cutting-edge AI technologies to enhance prediction accuracy and speed. In previous blogs, we have demonstrated how to run several state-of-the-art AI weather forecasting models, such as Pangu-Weather, GenCast, and Aurora. Following that, this blog focuses on emerging trends in weather forecasting models, particularly the innovative NeuralGCM, which melds the strengths of General Circulation Models (GCMs) and Machine Learning (ML). We will briefly outline the design of NeuralGCM and its hybrid approach for weather and precipitation forecasting. We will then go through the required environments, installation steps, and the inference process for generating forecasts and creating plots to compare the outputs to the ground truth provided by ERA5 data.
GROMACS Performance on AMD Instinct MI355X
- 13 March 2026
Are you planning a hardware upgrade for your molecular dynamics workflows? In this blog, we benchmark GROMACS on AMD’s latest Instinct MI355X GPU and compare it head-to-head with the MI300X, demonstrating significant throughput improvements that accelerate time-to-results for life-science research. You will see exactly how much faster MI355X runs the standard ADH dodec benchmark across 1 to 8 GPUs. Use these results to make informed decisions about your next HPC deployment.
Fine-Tuning AI Surrogate Models for Physics Simulations with Walrus on AMD Instinct GPU Accelerators
- 06 March 2026
Physics simulations are used for studying complex systems and are essential where experiments are difficult, expensive, or impossible. In our context, a simulation means numerically solving mathematical equations that are believed to describe a physical system and evolving them forward in time on a computer. They enable controlled exploration of physical behavior for science and engineering, but at a high computational cost, which in most cases increases rapidly with scale. Our focus is on continuum dynamics, where the system is represented by fields such as density, velocity, or temperature, defined on a grid and evolving over time. High-resolution physics simulations are slow to run, sensitive to numerical error and impractical for large parameter spaces. Surrogate models address these limitations by learning to approximate simulation dynamics directly from data. Once trained, they can produce fast predictions at a fraction of the cost, giving researchers the ability to rapidly explore parameter space and generate long rollouts.
Ensemble High-Resolution Weather Forecasting on AMD Instinct GPU Accelerators
- 06 March 2026
Weather prediction is fraught with uncertainty, as is the inference of any real-world phenomena dependent on physical observations. The consequence is that any estimated current state of the atmosphere as well as any forecast both carry a level of uncertainty. As such, any weather forecasting model, whether AI or traditional, needs to produce reasonable outputs despite the inherent uncertainty of inputs, and, if possible, quantify the uncertainty of the outputs for the user in some practical fashion.
Accelerating Graph Layout with AI and ROCm on AMD GPUs
- 06 February 2026
Learn how easy it is to implement established graph algorithms, and deploy them on AMD GPUs with immediate performance improvements, using AI as a coding partner!
Foundations of Molecular Generation with GP-MoLFormer on AMD Instinct MI300X Accelerators
- 03 February 2026
Nearly every technological breakthrough we celebrate begins with a material that did not exist before someone imagined it. Modern computing rests on engineered semiconductors, energy storage depends on carefully designed electrolytes, and sustainable technologies increasingly rely on alternatives to scarce or environmentally costly rare earth elements. Designing such materials with specific properties at scale is one of the most challenging and consequential problems in science.
Applying Compute Partitioning for Workloads on MI300X GPUs
- 14 January 2026
This blog explains how to use AMD GPU compute partitioning to increase throughput, utilization and reduce time-to-results for two different types of workloads:
Installing AMD HIP-Enabled GROMACS on HPC Systems: A LUMI Supercomputer Case Study
- 12 January 2026
Running molecular dynamics (MD) simulations efficiently is critical for accelerating scientific discovery in many life science use cases, e.g., drug discovery. GROMACS is a widely used, GPU-accelerated molecular dynamics engine powering many life science workflows, but its performance can vary significantly depending on the installation method and hardware configuration. For broader context on GROMACS applications in drug design, see recent research on GROMACS in cloud environments for alchemical drug design.
High-Resolution Weather Forecasting with StormCast on AMD Instinct GPU Accelerators
- 07 January 2026
The traditional approach to numerical weather prediction is based on propagating a known atmospheric state forward in time in short steps using systems of partial differential equations directly obtained from physical considerations. A new approach is to use machine learning methods to directly proceed to a later state in one large step, typically moving forward multiple hours in a single jump.
3D Scene Reconstruction from the Inside: Explore the Mathematics Behind gsplat
- 16 December 2025
3D Gaussian Splatting (3DGS) reconstructs 3D scenes from multiple 2D images and renders novel views in real time. In this blog, which serves as a follow up to a previous post, Elevating 3D Scene Rendering with GSplat, you will learn the core mathematics and the practical library components behind 3DGS using gsplat.
Modernizing Taichi Lang to LLVM 20 for MI355X GPU Acceleration
- 04 December 2025
Our first Taichi Lang blog intrduced you to Taichi Lang on AMD’s MI210 and MI250X GPUs. This previous version of Taichi was limited by it’s dependence on outdated versions of LLVM. We have modernized Taichi to LLVM 20 to take advantage of the latest advances in LLVM’s code generation capabilities. This modernization also allows us to make Taichi available for execution on newer AMD Instinct GPUs, MI300X, MI325X and MI355X. As with our previous blog, we provide you with a guide for understanding Taichi, and walk you through installing Taichi, as well as, writing and executing a Taichi program.
HPC Coding Agent - Part 1: Combining GLM-powered Cline and RAG Using MCP
- 03 December 2025
Navigating through extensive High Performance Computing (HPC) documentation can be challenging, especially when working with complex supercomputer environments like LUMI, one of the pan-European pre-exascale supercomputers. Traditional search methods often fall short when you need contextual, actionable answers to technical questions. RAG (Retrieval-Augmented Generation) agents offer a solution by combining large language model reasoning with domain-specific knowledge retrieval to provide accurate, cited responses to your HPC queries.
Accelerating AI-Driven Crystalline Materials Design with MatterGen on AMD Instinct MI300X
- 21 November 2025
The search for new inorganic materials has always been central to scientific and technological progress. From the silicon that powered the microelectronics revolution to the lithium compounds enabling modern batteries, advances in materials have defined entire eras of innovation. Yet, discovering new compounds with desired properties remains an exceptionally difficult challenge.
Plug-and-Play CuPy on ROCm: Data Analytics Acceleration Made Simple
- 14 November 2025
AMD is committed to ensuring that CuPy works seamlessly on AMD Instinct GPUs through ROCm and has worked to support the latest features in upstream CuPy on ROCm. In this blog, you will learn about the enhancements in the current and upcoming AMD CuPy releases that will supercharge your analytics and data science projects. In an earlier blog on CuPy and hipDF, it was demonstrated that CuPy and hipDF can be applied to complex analytics tasks with large datasets on ROCm using AMD GPUs. That blog used a PyPI wheel forked from earlier versions of CuPy and cuDF, and both CuPy and ROCm have advanced since then. In the latest AMD CuPy release, you will find many exciting improvements from the upstream CuPy library as well as ROCm 7.
Accelerating Vector Search: hipVS and hipRAFT on AMD
- 13 November 2025
In this blog, you’ll get an introductory look at hipVS, AMD’s GPU-accelerated vector search library, and its relationship to hipRAFT, a foundational library used by hipVS and other ROCmDS projects. Using an interactive Jupyter notebook, you’ll explore four major vector search methods available in hipVS: Brute-Force KNN, IVF-Flat, IVF-PQ, and CAGRA—each illustrating different trade-offs in accuracy, performance, and memory. You’ll see how to build and query vector search indexes using the hipVS API for applications such as semantic search, recommendation systems, and RAG pipelines. Since the API is compatible with NVIDIA’s cuVS, migrating workflows to AMD hardware is seamless and requires minimal changes.
ROCm 7.0: An AI-Ready Powerhouse for Performance, Efficiency, and Productivity
- 16 September 2025
Artificial intelligence now defines the performance envelope for modern computation. In this blog, we introduce the AI-centric ROCm 7.0 designed to help our community directly benefit from this dramatic paradigm shift. ROCm 7.0 delivers a platform purpose-built for the era of generative AI, large-scale inference and training, and accelerated discovery, helping you boost the performance, efficiency, and scalability of your workloads.
Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs
- 31 July 2025
Taichi Lang is an open-source, imperative, parallel programming language for high-performance numerical computation. It is embedded in Python and uses just-in-time (JIT) compiler frameworks (e.g. LLVM) to offload the compute-intensive Python code to the native GPU or CPU instructions. The language has broad applications spanning real-time physical simulation, numerical computation, augmented reality, artificial intelligence, vision and robotics, visual effects in films and games, general-purpose computing, and much more [1].
AMD ROCm: Powering the World’s Fastest Supercomputers
- 10 June 2025
From breaking the exaFLOP barrier with Frontier to setting new performance records with El Capitan, AMD is transforming what’s possible in high-performance computing (HPC). But the story goes beyond hardware. At the core of these world-class systems is ROCm, AMD’s open, high-performance software platform enabling new levels of scientific discovery and AI advancement.
Introducing ROCm-DS: GPU-Accelerated Data Science for AMD Instinct™ GPUs
- 20 May 2025
AMD is excited to announce the early access release of ROCm-DS (ROCm Data Science), a new toolkit designed to accelerate data processing workloads on AMD Instinct™ GPUs. Built on the core ROCm toolkit, ROCm-DS promises to significantly enhance performance and scalability for data-intensive applications, catering to the pressing needs of today’s data-driven landscape. ROCm-DS is based on the open source libraries in the RAPIDS ecosystem. This collection of libraries enables a multitude of data processing operations, allowing new and existing workloads to tap into the computational advantages offered by AMD Instinct Datacenter GPUs. This early access release introduces two powerful new libraries: hipDF and hipGRAPH.
Installing ROCm from source with Spack
- 14 April 2025
In this guide you will learn how Spack makes building ROCm components from source easier and more flexible than other methods. This blog will walk you through installing ROCm from source using the Spack package manager. We will also discuss Spack’s place among other ROCm installation methods, the landscape of ROCm components, and show you how ROCm, as an open-source software platform, allows developers to streamline software stacks for their applications.
Deep dive into the MI300 compute and memory partition modes
- 09 February 2025
This blog introduces the inner compute and memory architecture of the AMD Instinct™ MI300, showing you how to use the MI300 GPU’s different partition modes to supercharge performance critical applications. In this blog, you will first get a brief introduction to the MI300 architecture, explaining how the MI300 compute and memory partitions can be used to your advantage. You will then learn in detail the compute partitioning modes and the memory partitioning modes, Further, two case studies demonstrate and benchmark the performance of the different modes. For convenience this blog uses the MI300X as a case-in-point example.
Graph analytics on AMD GPUs using Gunrock
- 29 July 2024
Graphs and graph analytics are related concepts that can help us understand complex data and relationships. In this context, a graph is a mathematical model that represents entities (called nodes or vertices) and their connections (called edges or links). And graph analytics is a form of data analysis that uses graph structures and algorithms to reveal insights from the data.
Programming AMD GPUs with Julia
- 16 April 2024
Julia is a high-level, general-purpose dynamic programming language that automatically compiles to efficient native code via LLVM, and supports multiple platforms. With LLVM, comes the support for programming GPUs, including AMD GPUs.