Thread Runtime Telemetry Circuitry for Workload Consolidation

Solution Overview

Problem

Existing operating systems lack the ability to efficiently schedule workloads across compute modules with varying power and performance capabilities, leading to suboptimal power and performance tradeoffs.

Innovation Solution

The implementation of thread runtime telemetry circuitry to monitor workload behavior, determine compute capability requirements, and provide hints to the operating system to consolidate work on specific modules, thereby optimizing scheduling decisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If workloads are scheduled across multiple compute modules, then performance capability is improved, but energy efficiency deteriorates

Engineering Contradiction:

Improveperformance capabilityVSAvoidenergy efficiency

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system dynamically adjusts workload scheduling by monitoring runtime telemetry data and adapting scheduling decisions based on current system state. The hardware guidance logic continuously evaluates performance and power metrics to determine optimal scheduling strategies, transitioning between consolidating workloads on fewer modules for efficiency and distributing across multiple modules for performance based on real-time conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes scheduling parameters based on runtime conditions by using hardware guidance logic to provide suggestions to the OS scheduler. The guidance logic adjusts scheduling parameters such as module selection and workload distribution based on monitored telemetry data, enabling the system to optimize the balance between performance and energy efficiency dynamically rather than using fixed scheduling policies.

Inventive Principle:

Principle #35Parameter changes

2Use of energy by moving object

If workloads are consolidated on a single compute module, then energy efficiency is improved, but performance capability deteriorates

Engineering Contradiction:

Improveenergy efficiencyVSAvoidperformance capability

Core Design Contradiction:

Use of energy by moving objectVSProductivity

Solution Approach 1:

The system dynamically adjusts workload scheduling by monitoring runtime telemetry data and adapting scheduling decisions based on current system state. The hardware guidance logic continuously evaluates performance and power metrics to determine optimal scheduling strategies, transitioning between consolidating workloads on fewer modules for efficiency and distributing across multiple modules for performance based on real-time conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes scheduling parameters based on runtime conditions by using hardware guidance logic to provide suggestions to the OS scheduler. The guidance logic adjusts scheduling parameters such as module selection and workload distribution based on monitored telemetry data, enabling the system to optimize the balance between performance and energy efficiency dynamically rather than using fixed scheduling policies.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If the operating system schedules workloads without understanding compute module capabilities, then software complexity is reduced, but scheduling optimization deteriorates

Engineering Contradiction:

Improvesoftware complexityVSAvoidscheduling optimization

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The hardware guidance logic acts as an intermediary between the compute modules and the OS scheduler. It monitors runtime telemetry data from the compute modules and translates low-level hardware characteristics into high-level scheduling suggestions that the OS can act upon without needing to understand underlying hardware details. This intermediary layer preserves software simplicity while enabling optimized scheduling decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements a feedback mechanism where runtime telemetry data from compute modules is continuously monitored and fed back to the hardware guidance logic. This feedback loop enables the guidance logic to make informed scheduling recommendations based on actual hardware performance and power consumption characteristics, improving scheduling optimization without increasing software complexity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250061003A1Hardware guidance for efficiently scheduling workloads to the optimal compute module

Publication Date: 2025.02.20 INTEL CORP

AI summary

Techniques for providing hardware provided guidance for efficiently scheduling workloads to an optimal compute module are described. In some examples, hardware includes a first plurality of physical processor cores of a first type to implement a plurality of logical processor cores of the first type; a second plurality of physical processor cores of a second type, wherein each core of the second type is to implement a plurality of logical processor cores of the second type; a power management unit to monitor telemetry data on the first plurality of processor cores and second plurality of processor cores and to update hardware feedback telemetry data; and thread runtime telemetry circuitry to provide a hint using the hardware feedback telemetry data to consolidate tasks on one of core types.