AMD is excited to announce the release of the AMD ROCm™ 5.7. With AMD ROCm open software platform built for flexibility and performance, the HPC and AI communities can gain access to open compute languages, compilers, libraries and tools designed to accelerate code development and solve the toughest challenges in the world today. The latest version of the AMD ROCm platform adds new functionality while building on your favorite features from ROCm 5.7 and other previous releases. Here we will be introducing HIPTensor and highlighting some of our favorite and newly enhanced features in the rocRAND RNG, and MIGraphX libraries. If you are interested in a more in-­depth look at ROCm 5.7, we encourage you to check out the release notes.

We live in an age of rapid technological advancement. Machine learning has become a term that has permeated every facet of our lives. From the personalized recommendations suggested to you when you sit down to stream a movie, to self-driving cars navigating our streets, machine learning is what makes these innovations possible. But what exactly is machine learning, and how is it transforming the world around us?

AMD knows a lot about supercomputing. In fact, AMD powered supercomputers hold 2 of the top 5 spots on the Top500 list – including the first system to bring the world into the era of exascale (a computer capable of performing a quintillion or 10^18 floating point operations per second), holding an HPL (High Performance LINPACK) score that is nearly triple that of ­­­the second-place listing. Though, fewer know how much work it is to get to the point of flexing a system’s computing muscle to a worldwide stage. There is a ton of work that happens behind the scenes before any of these massive systems can “open its doors” to production workloads. One of the main milestones for any supercomputer is called acceptance. Let’s break down what leads a supercomputer to be accepted and find out what that term really means. Then, learn how AMD contributes to building the most performant systems in the world and some of the science they enable.

High performance computing (HPC) has become an essential part of our modern world performing complex simulations and calculations that are essential to scientific research, engineering, security and other fields. However, as the demand for HPC has grown, often in supercomputers and large datacenters, so has the concern about its environmental impact. In recent years, there has been a growing focus on data center sustainability given the implications on total cost of ownership and climate concerns. In this blog post, we'll explore some of the key issues around data center energy efficiency and discuss some of the strategies from AMD to help reduce environmental impacts from HPC.

Rice University is hosting the 16th annual Energy High Performance Computing Conference in Houston, Texas from February 28th through March 1st. This highly technical conference brings together the energy industry to engage in topics about high performance computing, computational science, machine learning, and data science.

Since its inception, the Graphics Processing Unit (GPU) has had promising possibilities as an accelerator for tasks other than graphics rendering. While the GPU in a gaming PC and one used as a general-purpose accelerator have considerable base-feature overlap, there are many benefits when optimizing these cards specifically for compute workloads. The ability to execute a plethora of simple calculations in parallel vastly improves the performance of any workload that could benefit from repetitive iteration. This is now coming of age as GPUs guide the future of supercomputing – bringing us into the Exascale Era.