Kernel Assignment for XPU Energy Efficiency
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Solution Overview
Problem
Current computational frameworks for XPU-based systems prioritize performance over power and thermal efficiency, leading to increased costs and carbon footprint, which is at odds with the industry's vision for sustainable computing.
Innovation Solution
The implementation of Power Thermal Cognizant Compute Kernels (PTCCK) within the Intel oneAPI framework, which determines energy-related metrics for each XPU and optimally assigns compute kernels to minimize energy consumption and thermal impact by considering capabilities and constraints of various processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If performance-driven heuristics are used to drive computational kernels on XPU hardware, then computational performance is improved, but energy consumption increases and power efficiency decreases
Solution Approach 1:
The system dynamically selects and switches between different compute kernels based on runtime conditions including energy metrics, thermal state, and performance requirements. This dynamic adaptation allows the system to optimize for performance when needed and for energy efficiency when appropriate, resolving the contradiction between computational performance and energy consumption
Solution Approach 2:
The system changes operational parameters by selecting different compute kernels with varying characteristics (performance vs. energy efficiency). The kernel selection is based on multiple parameters including energy metrics, thermal state, and performance requirements, allowing the system to adjust its operational mode to resolve the performance-energy tradeoff
2Productivity
If performance-driven heuristics are used to drive computational kernels on XPU hardware, then computational performance is improved, but thermal efficiency decreases
Solution Approach 1:
The system incorporates feedback mechanisms that monitor thermal state and energy metrics in real-time. Based on this feedback, the system adjusts kernel selection to maintain thermal efficiency while meeting performance requirements, resolving the contradiction between computational performance and thermal efficiency
Solution Approach 2:
The system dynamically adapts kernel selection based on runtime thermal conditions. When thermal state indicates overheating or high power consumption, the system switches to more thermally efficient kernels, thereby resolving the contradiction between performance and thermal efficiency
3Use of energy by moving object
If energy-related metrics are considered in kernel assignment, then power efficiency and thermal efficiency are improved, but system complexity increases
Solution Approach 1:
The system implements a universal kernel selection mechanism that handles multiple objectives (performance, energy efficiency, thermal efficiency) through a single integrated framework. This multi-functional approach manages system complexity by providing a unified solution rather than separate mechanisms for each objective
4Loss of energy
If energy-related metrics are considered in kernel assignment, then total cost of ownership is reduced, but computational performance may be compromised
Solution Approach 1:
The system dynamically balances energy efficiency and performance based on runtime conditions. When performance requirements are critical, the system selects high-performance kernels; when energy efficiency is prioritized, it selects energy-efficient kernels. This dynamic balancing resolves the contradiction between reducing total cost of ownership and maintaining computational performance
Data Source
AI summary
Examples relate to an apparatus, a device, a method, and a computer program for controlling the execution of a computer program by a computer system comprising two or more different Processing Units (XPUs), and to a corresponding computer system. The apparatus comprises processing circuitry configured to obtain the computer program, wherein at least a portion of the computer program is based on one or more compute kernels to be executed by the two or more different XPUs. The processing circuitry is configured to determine, for each XPU, an energy-related metric for executing the one or more compute kernels on the respective XPU. The processing circuitry is configured to assign the execution of the one or more compute kernels to the two or more different XPUs based on the respective energy-related metric.