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* Create issue_retrieval.yml I am tasked with adding a GitHub action to process incoming GitHub issues. The AMD GitHub admin team asked me to try out one of their runners and to do so, I need to load in a workflow file. * changed group to ROCM-Ubuntu * Added a field to specify project number This action receives an org name and project number and adds issues to it using this information * Update issue_retrieval.yml * Update issue_retrieval.yml * Generate release notes for 6.0.1 from autotag script (#2790) * Update CONTRIBUTING.md (#2791) * Update CONTRIBUTING.md * Fixed link to licensing document Also, changed to use relative links for internal files. * Revert "Update CONTRIBUTING.md" (#2795) * Text change to direct PRs into default branch, since not all repos have develop branch * add keywords (#2799) * Update issue_retrieval.yml * ci(default.xml): Add hipBLASLt to manifest (#2796) * Deleting issue_report.yml in favor of a global issue template placed in ROCm/.github (#2803) * Delete .github/ISSUE_TEMPLATE/issue_report.yml * Delete .github/ISSUE_TEMPLATE/config.yml * Delete .github/ISSUE_TEMPLATE directory (#2805) * docs(conf.py): Update article info for release page (#2806) * docs(conf.py): Update article info for release page * Update conf.py * Fix typo (#2809) --------- Co-authored-by: abhimeda <138710508+abhimeda@users.noreply.github.com> Co-authored-by: David Galiffi <dgaliffi@amd.com> Co-authored-by: Lisa <lisa.delaney@amd.com> Co-authored-by: Young Hui <young.hui@amd.com> Co-authored-by: yhuiYH <145490163+yhuiYH@users.noreply.github.com>
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Tuning guides
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.
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:::{grid-item-card} AMD Instinct™ MI200
This chapter goes through how to configure your AMD Instinct™ MI200 accelerated compute nodes to get the best performance out of them.
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:::{grid-item-card} AMD Instinct™ MI100
This chapter briefly reviews hardware aspects of the AMD Instinct™ MI100 accelerators and the CDNA™ 1 architecture that is the foundation of these GPUs.
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Workstation
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.
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:::{grid-item-card} AMD Radeon™ PRO W6000 and V620
This chapter describes the AMD GPUs with RDNA™ 2 architecture, namely AMD Radeon PRO W6800 and AMD Radeon PRO V620
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