Elastic Inference Service for Dynamic Accelerator Scaling
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Solution Overview
Problem
Deep learning inference workloads are costly and inefficient to run in production due to overprovisioning of GPUs, leading to wasteful and expensive infrastructure costs.
Innovation Solution
An elastic inference service that allows for the dynamic attachment and detachment of accelerator slots, providing cost-efficient hardware acceleration by using a hardware-independent interface to virtualize a single accelerator chip across multiple applications, enabling flexible precision and speed choices, and decoupling CPU and memory resources from machine learning computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GPUs are overprovisioned to handle deep learning inference workloads, then performance and efficiency are improved, but infrastructure costs increase and resource waste occurs
Solution Approach 1:
Multiple virtual machine instances are merged to share a single physical GPU accelerator through dynamic attachment and detachment mechanisms. The system combines computational resources from multiple virtual instances and consolidates them onto one physical accelerator, enabling efficient resource utilization without overprovisioning.
Solution Approach 2:
The system implements dynamic attachment and detachment of accelerators to virtual machine instances based on real-time workload demands. Accelerators can be attached when needed and detached when not needed, allowing the infrastructure to adapt flexibly to varying inference workload requirements and avoid continuous resource waste.
2Adaptability or versatility
If accelerators are dynamically attached and detached, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where virtual machine instances can autonomously request and receive accelerator attachments, and automatically initiate detachment when needed. The control system autonomously manages the attachment/detachment lifecycle based on workload conditions, reducing manual intervention complexity while maintaining high adaptability.
Solution Approach 2:
A control system acts as an intermediary between virtual machine instances and physical accelerators, managing the complex attachment and detachment operations. This intermediary layer abstracts the complexity by handling resource allocation, coordination, and state management, allowing individual components to remain simple while achieving system-level versatility.
3Loss of energy
If a single accelerator chip is virtualized across multiple applications, then cost efficiency is improved, but hardware access complexity increases
Solution Approach 1:
The physical accelerator is segmented into multiple virtual accelerators that can be independently allocated to different applications. Each virtual accelerator represents a partitioned portion of the physical hardware resources, allowing multiple applications to access the single physical chip simultaneously with isolated resource management, thereby improving cost efficiency while managing complexity through structured segmentation.
Solution Approach 2:
The system creates a universal accelerator interface that can serve multiple different applications and workloads through a single physical hardware platform. The virtualization layer provides multi-functionality by allowing the same physical accelerator to be dynamically configured and shared across diverse inference applications, improving hardware utilization and cost efficiency.
Data Source
AI summary
Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes provisioning an application instance and portions of at least one accelerator attached to the application instance to execute a machine learning model of an application of the application instance; loading the machine learning model onto the portions of the at least one accelerator; receiving scoring data in the application; and utilizing each of the portions of the attached at least one accelerator to perform inference on the scoring data in parallel and only using one response from the portions of the accelerator.