Elastic Inference Service for GPU Slot Segmentation
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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 attachment and detachment of accelerator slots, providing cost-efficient hardware acceleration by decoupling CPU and memory resources, and supporting multiple precision levels, enabling flexible and efficient use of hardware resources across multiple applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If full GPU capacity is provisioned for deep learning inference, then performance and efficiency are improved, but infrastructure cost increases and resource wastage occurs
Solution Approach 1:
The patent segments the GPU accelerator into multiple independent slots (e.g., 8 slots per GPU) that can be independently allocated to different inference workloads. This allows partial utilization of GPU capacity rather than requiring full GPU provisioning, reducing infrastructure cost while maintaining inference performance through efficient resource sharing
Solution Approach 2:
The accelerator slot interface enables a single GPU to serve multiple inference workloads simultaneously through slot-based multiplexing. The same physical accelerator resources are universally shared across multiple applications and customers, eliminating the need for dedicated full-GPU provisioning for each workload and reducing overall infrastructure cost
2Productivity
If accelerator slots are decoupled from CPU and memory resources, then hardware resource efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer (accelerator slot manager and virtualization software) that handles the complexity of decoupled resource allocation. This intermediary manages the mapping between accelerator slots and CPU/memory resources, providing automated resource orchestration that improves hardware efficiency while shielding users from the underlying complexity through standardized interfaces
3Adaptability or versatility
If multiple precision levels are supported, then adaptability to different applications is improved, but hardware complexity increases
Solution Approach 1:
The patent implements support for multiple precision levels (FP16, INT8, etc.) by allowing dynamic configuration of accelerator slot parameters without changing the underlying hardware architecture. The same physical accelerator can be reconfigured to operate at different precision levels through software control, providing application compatibility while avoiding the complexity of multiple specialized hardware units
Data Source
AI summary
Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes attaching a first set of one or more accelerator slots of an accelerator appliance to an application instance of a multi-tenant provider network according to an application instance configuration, the application instance configuration to define per accelerator slot capabilities to be used by an application of the application instance, wherein the multi-tenant provider network comprises a plurality of computing devices configured to implement a plurality of virtual compute instances, and wherein the first set of one or more accelerator slots is implemented using physical accelerator resources accessible to the application instance; while performing inference using the loaded machine learning model of the application using the first set of one or more accelerator slots on the attached accelerator appliance, managing resources of the accelerator appliance using an accelerator appliance manager of the accelerator appliance.