6 min read time
Use case-specific system setup and tuning guides.
High Performance Computing#
High Performance Computing (HPC) workloads have unique requirements. The default hardware and BIOS configurations for OEM platforms may not provide optimal performance for HPC workloads. To enable optimal HPC settings on a per-platform and per-workload level, this guide calls out:
BIOS settings that can impact performance
Hardware configuration best practices
Supported versions of operating systems
Workload-specific recommendations for optimal BIOS and operating system settings
There is also a discussion on the AMD Instinct™ software development environment, including information on how to install and run the DGEMM, STREAM, HPCG, and HPL benchmarks. This guidance provides a good starting point but is not exhaustively tested across all compilers.
Prerequisites to understanding this document and to performing tuning of HPC applications include:
Experience in configuring servers
Administrative access to the server’s Management Interface (BMC)
Administrative access to the operating system
Familiarity with the OEM server’s BMC (strongly recommended)
Familiarity with the OS specific tools for configuration, monitoring, and troubleshooting (strongly recommended)
This document provides guidance on tuning systems with various AMD Instinct™ accelerators for HPC workloads. This document is not an all-inclusive guide, and some items referred to may have similar, but different, names in various OEM systems (for example, OEM-specific BIOS settings). This document also provides suggestions on items that should be the initial focus of additional, application-specific tuning.
This document is based on the AMD EPYC™ 7003-series processor family (former codename “Milan”).
While this guide is a good starting point, developers are encouraged to perform their own performance testing for additional tuning.
Workstation workloads, much like High Performance Computing have a unique set of requirements, a blend of both graphics and compute, certification, stability and the list continues.
The document covers specific software requirements and processes needed to use these GPUs for Single Root I/O Virtualization (SR-IOV) and Machine Learning (ML).
The main purpose of this document is to help users utilize the RDNA 2 GPUs to their full potential.