Workload-Aware Processor Configuration API for Power Efficiency
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Solution Overview
Problem
Existing job scheduling systems in data centers often fail to efficiently utilize computing resources, leading to suboptimal use of time and resources for performing software workloads.
Innovation Solution
An application programming interface (API) is used to identify and configure processor settings based on characteristics of a software workload, such as performance preferences and priority, to optimize resource allocation and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional job scheduling systems are used to schedule jobs by priority level, then jobs can be executed in a structured manner, but computing resources are not efficiently utilized and performance is suboptimal
Solution Approach 1:
The system dynamically adjusts processor settings based on workload characteristics and job priority levels. The job scheduler modifies processor configuration parameters in real-time to match the demands of each job, enabling adaptive resource allocation that improves both execution efficiency and energy utilization.
Solution Approach 2:
The invention changes processor operating parameters such as frequency, voltage, and power states based on job characteristics. By adjusting these parameters dynamically according to job priority and type, the system optimizes computing resource utilization and reduces energy waste while maintaining high productivity.
2Speed
If processor settings are configured for high performance, then job execution speed is improved, but power consumption increases
Solution Approach 1:
The system implements dynamic processor configuration that adjusts performance settings based on real-time job requirements. High-priority jobs receive higher performance settings with corresponding power consumption, while lower-priority jobs use energy-efficient settings, creating a dynamic balance between speed and power usage.
Solution Approach 2:
Different processor settings are applied to different jobs based on their specific requirements. Instead of uniformly high performance across all jobs, the system applies localized performance optimization where each job receives processor configuration tailored to its priority level and characteristics, reducing overall power consumption while maintaining necessary execution speeds.
3Productivity
If processor settings are optimized for each specific workload, then computing efficiency is improved, but system complexity increases
Solution Approach 1:
The job scheduler is designed as a universal system that handles multiple job types and priorities through a unified configuration approach. Rather than requiring separate optimization mechanisms for each workload type, the system uses a single multi-functional scheduler that adapts to different jobs through parameter adjustment, maintaining simplicity while achieving workload-specific optimization.
Solution Approach 2:
The system implements self-service mechanisms where the job scheduler automatically determines appropriate processor settings based on job characteristics without requiring manual intervention. The scheduler services itself by making intelligent decisions about resource allocation and configuration, reducing operational complexity while maintaining high computing efficiency.
Data Source
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AI summary
Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.