Thermal-Aware Workload Scheduling in Multi-Core Processors
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Solution Overview
Problem
Modern computer systems face challenges with thermal variations that lead to reliability issues, increased cooling costs, and performance degradation due to the lack of temperature-aware scheduling in load balancing across processor cores.
Innovation Solution
A computer system that schedules loads across processor cores based on thermal measurements, using noise-reduced data from sensors and regression models to analyze thermal properties, thereby reducing spatial and temporal thermal variations through probabilistic and adaptive-random scheduling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional load balancing schedulers are used to distribute workload evenly across processor cores, then processor utilization is balanced and performance is improved, but temperature variations are exacerbated and thermal reliability issues increase
Solution Approach 1:
The scheduler assigns workloads based on local thermal conditions of each processor core, treating each core's thermal state as a unique property. The system monitors temperature sensors on individual cores and dynamically routes tasks to cores with lower thermal states, creating localized thermal management that prevents hot spots while maintaining load balance.
Solution Approach 2:
The scheduling system dynamically adapts to changing thermal conditions in real-time. Temperature sensors continuously monitor core states, and the scheduler adjusts task assignment dynamically based on current thermal profiles. This dynamic adaptation allows the system to respond to thermal variations as they occur, preventing thermal accumulation and maintaining both performance and reliability.
2Device complexity
If first-available scheduling is used when processor utilization is between 10-20%, then scheduling simplicity is maintained, but spatial and temporal thermal variations are exacerbated
Solution Approach 1:
The system performs preliminary thermal monitoring and profiling before task assignment. Temperature sensors continuously measure core temperatures, and the system maintains thermal state information in advance. When a task needs scheduling, this pre-collected thermal data is immediately available to guide assignment decisions, avoiding the need for complex real-time thermal analysis while still achieving thermal-aware scheduling.
Solution Approach 2:
The scheduler implements feedback loops where temperature sensor readings from processor cores are continuously fed back to the scheduling algorithm. This feedback mechanism allows the system to adjust task assignments based on actual thermal states, creating a closed-loop control system that naturally balances thermal distribution without requiring complex predictive models or additional hardware.
3Speed
If locality-based load balancing is used to assign jobs to cores sharing the same memory, then memory access efficiency is improved, but thermal variations are increased due to concentrated workload on specific cores
Solution Approach 1:
The system introduces asymmetric task distribution by considering thermal states as an additional factor beyond memory locality. While memory affinity is maintained to preserve access efficiency, the scheduler deliberately assigns tasks to cores with lower thermal states even if this means slightly less optimal memory access patterns. This asymmetric approach prioritizes thermal balance while maintaining acceptable memory performance.
Solution Approach 2:
The scheduling algorithm changes its decision parameters by incorporating thermal state as a weighted factor alongside memory locality. Instead of purely optimizing for memory access speed, the system adjusts the optimization criteria to balance both memory efficiency and thermal distribution. This parameter change allows the scheduler to make trade-offs between memory performance and thermal management based on system conditions.
Data Source
AI summary
A computer system that schedules loads across a set of processor cores is described. During operation, the computer system receives thermal measurements from sensors associated with the set of processor cores, and removes noise from the thermal measurements. Then, the computer system analyzes thermal properties of the set of processor cores based on the thermal measurements. Next, the computer system receives a process to be executed, and schedules the process to be executed by at least one of the processor cores based on the analysis. This scheduling is performed in a manner that reduces spatial and temporal thermal variations in the integrated circuit.