Processor Thermal Capacity Management for Sprinting Workloads
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Solution Overview
Problem
Existing computational sprinting techniques in multi-core processors often fail to efficiently utilize thermal headroom, leading to suboptimal performance due to incorrect application or phase of sprinting, limited memory, small parallelism, or insufficient workloads.
Innovation Solution
A method and apparatus that utilize thermal sensors and an analyzer to monitor and manage thermal capacity, enabling sprinting only when the benefits exceed a threshold and ensuring the remaining capacity is not exhausted, with parameters determined for each workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sprinting is performed to boost processor performance beyond nominal levels, then computational performance is improved, but thermal capacity may be exhausted leading to overheating
Solution Approach 1:
The system dynamically adjusts processor frequency and core activation based on real-time thermal capacity monitoring. The analyzer continuously tracks thermal headroom and modifies sprinting parameters (frequency boost level, duration, number of active cores) to match available thermal capacity, enabling performance optimization without exceeding thermal limits.
Solution Approach 2:
The system implements a feedback loop where thermal sensors continuously monitor processor temperature and thermal capacity, feed this information to the analyzer, which then adjusts sprinting decisions accordingly. This closed-loop control ensures performance boosting occurs only when thermal headroom is sufficient, preventing thermal exhaustion.
2Use of energy by stationary object
If sprinting is performed incorrectly for the wrong application or application phase, then thermal headroom is wasted, but energy efficiency deteriorates
Solution Approach 1:
The system performs preliminary analysis of workload characteristics and application phases before initiating sprinting. The analyzer evaluates whether the current workload can benefit from performance boosting (checking for sufficient parallelism, memory availability, and work amount) and only activates sprinting when conditions are favorable, preventing wasted thermal headroom on unsuitable workloads.
Solution Approach 2:
The system changes sprinting parameters (whether to sprint, frequency level, duration) based on workload characteristics and application phase. By adapting sprinting behavior to match workload demands, the system maximizes thermal headroom utilization for beneficial workloads while avoiding energy waste on workloads that cannot leverage the performance boost.
3Productivity
If sprinting parameters are not optimized for each workload, then performance is improved, but sprinting effectiveness deteriorates
Solution Approach 1:
The system applies different sprinting parameters tailored to each specific workload and application phase. Rather than using a uniform sprinting approach, the analyzer determines optimal frequency boosts, core activation patterns, and duration based on the characteristics of the current workload, ensuring each sprinting event is optimized for its specific context.
Solution Approach 2:
The system dynamically determines sprinting parameters based on real-time conditions including workload type, available thermal capacity, and application phase. This dynamic parameter adjustment ensures that sprinting effectiveness is maintained across diverse workloads by adapting the boosting strategy to match each workload's specific requirements and constraints.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances energy efficiency and effectiveness of sprinting by optimizing thermal usage, allowing processors to temporarily exceed nominal power and performance ratings only when beneficial, thereby improving computational performance without overheating.
Implementation Method 1
A processor may include a plurality of thermal sensors and an analyzer in communication with the thermal sensors. The analyzer may be configured to monitor thermal capacity remaining in the processor while not sprinting.
Data Source
AI summary
A method and apparatus are described for performing sprinting in a processor. An analyzer in the processor may monitor thermal capacity remaining in the processor while not sprinting. When the remaining thermal capacity is sufficient to support sprinting, the analyzer may perform sprinting of a new workload when a benefit derived by sprinting the new workload exceeds a threshold and does not cause the remaining thermal capacity in the processor to be exhausted. The analyzer may perform sprinting of the new workload in accordance with sprinting parameters determined for the new workload. The analyzer may continue to monitor the remaining thermal capacity while not sprinting when the benefit derived by sprinting the new workload does not exceed the threshold.