Dynamic Hardware Selection for Mixture-of-Experts Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mixture-of-experts machine learning models often benefit from execution on heterogeneous processing devices, but existing systems lack an automated method to optimally assign experts to the most suitable hardware devices based on varying execution parameters such as speed and power consumption.
Innovation Solution
An orchestrator component maintains priority data to rank processing devices for each expert and execution parameter, and dynamically selects hardware devices and adjusts model characteristics to optimize execution, using measurements from executing experts on different devices to store and utilize this data for efficient execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If experts are executed on heterogeneous processing devices, then execution speed and power consumption are optimized, but device complexity and assignment difficulty increase
Solution Approach 1:
The system performs self-characterization by automatically executing experts on available processing devices and measuring performance metrics. This self-service approach eliminates the need for external manual characterization, allowing the system to autonomously build its priority data structures and make informed device selection decisions without human intervention
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring execution parameters (speed, power consumption) when experts run on different processing devices. These measurements feed back into the priority data structures, enabling the system to learn from actual performance and continuously optimize its device selection strategy for future expert executions
2Loss of energy
If automated device selection is implemented, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The system performs preliminary characterization of processing devices by pre-executing experts and storing performance measurements in priority data structures before actual workloads arrive. This advance preparation creates ready-to-use ranking information that enables rapid, automated device selection without complex real-time decision-making, reducing operational system complexity
Solution Approach 2:
The system changes parameters by maintaining multiple priority data structures that rank processing devices according to different execution parameters (speed, power consumption). By organizing device characteristics in terms of measurable parameters and their relative priorities, the system transforms complex heterogeneous device management into straightforward parameter-based selection queries
3Reliability
If multiple execution parameters are optimized, then overall performance improves, but measurement and selection difficulty increases
Solution Approach 1:
The system segments the optimization problem by creating separate priority data structures for each execution parameter (speed, power consumption). Instead of attempting to optimize all parameters simultaneously in a single complex model, the system divides the task into parameter-specific rankings that can be independently measured, stored, and queried based on current workload requirements
Data Source
AI summary
A system assigns experts of a mixture-of-experts artificial intelligence model to processing devices in an automated manner. The system includes an orchestrator component that maintains priority data that stores, for each of a set of experts, and for each of a set of execution parameters, ranking information that ranks different processing devices for the particular execution parameter. In one example, for the execution parameter of execution speed, and for a first expert, the priority data indicates that a central processing unit (“CPU”) executes the first expert faster than a graphics processing unit (“GPU”). In this example, for the execution parameter of power consumption, and for the first expert, the priority data indicates that a GPU uses less power than a CPU. The priority data stores such information for one or more processing devices, one or more experts, and one or more execution characteristics.