tuned-amdgpu

hacky solution to integrate AMDGPU power profile control in tuned with Ansible

Notable variables

• card: Sets the card# to use in the qualified sysfs path /sys/class/drm/{{ card }}/device/pp_power_profile_mode. Default: card0
• base_profiles: List of base tuned profiles to clone in the new AMDGPU profiles. Defaults:
  • desktop
  • network-latency
  • powersave
• amdgpu_profiles: Mapping of AMDGPU power profiles (name/value) defined in the sysfs path above. Varies, sample is with a 6900XT. Defaults:
  • { name: 'bootup_default', value: 0 }
  • { name: '3D_fullscreen', value: 1 }
  • { name: 'powersaving', value: 2 }
  • { name: 'video', value: 3 }
  • { name: 'VR', value: 4 }
  • { name: 'compute', value: 5 }
  • { name: 'custom', value: 6 }

Example profiles/output

 $ tuned-adm profile                           
Available profiles:
- accelerator-performance     - Throughput performance based tuning with disabled higher latency STOP states
- balanced                    - General non-specialized tuned profile
- desktop                     - Optimize for the desktop use-case
- desktop_amdgpu_3D_fullscreen- desktop based profile with AMDGPU pp_power_profile_mode = 1 (3D_fullscreen)
- desktop_amdgpu_VR           - desktop based profile with AMDGPU pp_power_profile_mode = 4 (VR)
- desktop_amdgpu_bootup_default- desktop based profile with AMDGPU pp_power_profile_mode = 0 (bootup_default)
- desktop_amdgpu_compute      - desktop based profile with AMDGPU pp_power_profile_mode = 5 (compute)
- desktop_amdgpu_custom       - desktop based profile with AMDGPU pp_power_profile_mode = 6 (custom)
- desktop_amdgpu_powersaving  - desktop based profile with AMDGPU pp_power_profile_mode = 2 (powersaving)
- desktop_amdgpu_video        - desktop based profile with AMDGPU pp_power_profile_mode = 3 (video)
- hpc-compute                 - Optimize for HPC compute workloads
- intel-sst                   - Configure for Intel Speed Select Base Frequency
- latency-performance         - Optimize for deterministic performance at the cost of increased power consumption
- network-latency             - Optimize for deterministic performance at the cost of increased power consumption, focused on low latency network performance
- network-latency_amdgpu_3D_fullscreen- network-latency based profile with AMDGPU pp_power_profile_mode = 1 (3D_fullscreen)
- network-latency_amdgpu_VR   - network-latency based profile with AMDGPU pp_power_profile_mode = 4 (VR)
- network-latency_amdgpu_bootup_default- network-latency based profile with AMDGPU pp_power_profile_mode = 0 (bootup_default)
- network-latency_amdgpu_compute- network-latency based profile with AMDGPU pp_power_profile_mode = 5 (compute)
- network-latency_amdgpu_custom- network-latency based profile with AMDGPU pp_power_profile_mode = 6 (custom)
- network-latency_amdgpu_powersaving- network-latency based profile with AMDGPU pp_power_profile_mode = 2 (powersaving)
- network-latency_amdgpu_video- network-latency based profile with AMDGPU pp_power_profile_mode = 3 (video)
- network-throughput          - Optimize for streaming network throughput, generally only necessary on older CPUs or 40G+ networks
- optimize-serial-console     - Optimize for serial console use.
- powersave                   - Optimize for low power consumption
- powersave_amdgpu_3D_fullscreen- powersave based profile with AMDGPU pp_power_profile_mode = 1 (3D_fullscreen)
- powersave_amdgpu_VR         - powersave based profile with AMDGPU pp_power_profile_mode = 4 (VR)
- powersave_amdgpu_bootup_default- powersave based profile with AMDGPU pp_power_profile_mode = 0 (bootup_default)
- powersave_amdgpu_compute    - powersave based profile with AMDGPU pp_power_profile_mode = 5 (compute)
- powersave_amdgpu_custom     - powersave based profile with AMDGPU pp_power_profile_mode = 6 (custom)
- powersave_amdgpu_powersaving- powersave based profile with AMDGPU pp_power_profile_mode = 2 (powersaving)
- powersave_amdgpu_video      - powersave based profile with AMDGPU pp_power_profile_mode = 3 (video)
- throughput-performance      - Broadly applicable tuning that provides excellent performance across a variety of common server workloads
- virtual-guest               - Optimize for running inside a virtual guest
- virtual-host                - Optimize for running KVM guests