Posts in Ecosystems and Partners
AMD Advances Enterprise AI Through OPEA Integration
- 12 March 2025
AMD is excited to support Open Platform for Enterprise AI (OPEA) to simplify and accelerate enterprise AI adoption. With the enablement of OPEA GenAI framework on AMD ROCm™ software stack, businesses and developers can now create scalable, efficient GenAI applications on AMD data center GPUs. Enterprises today face significant challenges when deploying AI at scale, including the complexity of integrating GenAI models, managing GPU resources, ensuring security, and maintaining workflow flexibility. AMD and OPEA aim to address these challenges and streamline AI adoption. This blog will explore the significance of this collaboration, AMD’s contribution to the OPEA project, and demonstrate how to deploy a code translation OPEA GenAI use case on the AMD Instinct™ MI300X GPU.
Boosting Computational Fluid Dynamics Performance with AMD Instinct™ MI300X
- 14 January 2025
This blog will guide you, step-by-step, through the process of installing and running benchmarks with Ansys Fluent and AMD MI300X. We start with an overview of the Ansys Fluent CFD application and then show you how to set up an AMD MI300X system to run benchmarks. The blog benchmarks results demonstrate the dramatic impact the MI300X has on speeding up simulations, improving design efficiency, and reducing costs in the automotive, aerospace, and environmental engineering industries.
Training Transformers and Hybrid models on AMD Instinct MI300X Accelerators
- 10 December 2024
This blog is contributed by Zyphra: a Palo Alto-based AI research lab and AMD Instinct Partner.
Introducing AMD’s Next-Gen Fortran Compiler
- 13 November 2024
We are excited to share a brief preview of AMD’s Next-Gen Fortran Compiler, our new open source Fortran complier supporting OpenMP offloading. AMD’s Next-Gen Fortran Compiler is a downstream flavor of LLVM Flang, optimized for AMD GPUs. Our Next-Gen Fortran Compiler enables OpenMP offloading and offers a direct interface to ROCm and HIP. In this blog post you will:
Stone Ridge Expands Reservoir Simulation Options with AMD Instinct™ Accelerators
- 10 June 2024
Stone Ridge Technology (SRT) pioneered the use of GPUs for high performance reservoir simulation (HPC) nearly a decade ago with ECHELON, its flagship software product. ECHELON, the first of its kind, engineered from the outset to harness the full potential of massively parallel GPUs, stands apart in the industry for its power, efficiency, and accuracy. Now, ECHELON has added support for AMDInstinct accelerators into its simulation engine, offering new flexibility and optionality to its clients.
Siemens taps AMD Instinct™ GPUs to expand high-performance hardware options for Simcenter STAR-CCM+
- 16 May 2024
Siemens recently announced that its Simcenter STAR-CCM+ multi-physics computational fluid dynamics (CFD) software now supports AMD Instinct™ GPUs for GPU-native computation. This move addresses its users’ needs for computational efficiency, reduced simulation costs and energy usage, and greater hardware choice.
AMD Collaboration with the University of Michigan offers High Performance Open-Source Solutions to the Bioinformatics Community
- 16 May 2024
Long read DNA sequencing technology is revolutionizing genetic diagnostics and precision medicine by helping us discover structural variants and assemble whole genomes. It also helps us study evolutionary relationships. Lower sequencing costs and high-throughput portable long read sequencers are revolutionizing precision medicine today. Long read sequencers from the top manufacturers including Oxford Nanopore (ONT) and PacBio, can produce reads that are much longer than previous generations of sequencers. However, long reads vary in length and are significantly more error prone than short reads. Sequence alignment (on CPUs) is one of the main bottlenecks in long read processing workflows.
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.