Accelerator Worker Context Switching for Elastic AI Workloads
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Solution Overview
Problem
Existing cloud-based Infrastructure as a Service (IaaS) systems struggle to efficiently manage and distribute artificial intelligence (AI) workloads, such as Deep Learning Training (DLT) and inferencing, due to exponential growth in workload demands, leading to inefficiencies and limits in hardware utilization and flexibility.
Innovation Solution
A computerized method for elastically managing multi-worker workloads on accelerator devices using a device proxy interface that decouples host and accelerator device address spaces, enabling transparent migration and context switching of workers, and employs checksums and semantic patterns to optimize memory usage and reduce computing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more hardware accelerators and nodes are added to IaaS environments to handle growing DLT workloads, then the system can accommodate more workloads, but the complexity of managing and distributing workloads across hardware increases and efficiency deteriorates
Solution Approach 1:
The patent introduces a workload management system that acts as an intermediary between the cloud infrastructure and AI workloads. This system includes components such as a workload manager, device proxy, and scheduler that coordinate resource allocation, manage context switching, and optimize hardware utilization, thereby reducing management complexity while maintaining high productivity
Solution Approach 2:
The patent creates a universal workload management framework that can handle multiple types of AI workloads (DLT, inferencing, etc.) across diverse hardware accelerators (GPUs, TPUs, FPGAs) through a unified interface. The device proxy and scheduler provide multi-functional capabilities to manage different workload types and hardware platforms, reducing the complexity of managing heterogeneous systems
2Adaptability or versatility
If general-purpose IaaS is used for AI workloads, then infrastructure flexibility is maintained, but efficiency in executing AI-specific workloads deteriorates
Solution Approach 1:
The patent implements local quality by introducing AI-optimized management components (device proxy, workload scheduler, context switch manager) that provide specialized handling for AI workloads while maintaining the underlying general-purpose IaaS infrastructure. These components add AI-specific optimizations at the appropriate layer without sacrificing infrastructure flexibility
Solution Approach 2:
The patent enables dynamic workload management through context switching between different AI workloads on the same hardware accelerators. The system can dynamically allocate and reconfigure accelerator resources based on workload demands, maintaining flexibility while improving execution efficiency through optimized resource utilization
3Productivity
If cloud infrastructure is scaled exponentially to match DLT workload growth, then workload capacity increases, but the limits of infrastructure size and management efficiency are reached
Solution Approach 1:
The patent merges multiple AI workloads onto shared hardware accelerator resources through time-multiplexed execution. By combining workload management, resource scheduling, and context switching in a unified system, the infrastructure can handle exponential workload growth without proportionally increasing hardware规模，maintaining scalability and management reliability
Data Source
AI summary
The disclosure herein describes elastically managing the execution of workers of multi-worker workloads on accelerator devices. A first worker of a workload is executed on an accelerator device during a first time interval. A first context switch point is identified when the first worker is in a first worker state. At the identified context switch point, a first memory state of the first worker is stored in a host memory and the accelerator device is configured to a second memory state of the second worker. The second worker is executed during a second time interval and a second context switch point is identified at the end of the second time interval when the second worker is in a state that is equivalent to the first worker state. During the intervals, collective communication operations between the workers are accumulated and, at the second context switch point, the accumulated operations are performed.