Heterogeneous Accelerator Task Allocation for Deep Learning Clusters
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Solution Overview
Problem
The high cost of building a deep learning cloud service cluster is exacerbated by the need for expensive GPUs, as existing technologies do not efficiently utilize heterogeneous accelerators to maximize performance and reduce costs.
Innovation Solution
A method for processing deep learning tasks in a cluster system that determines the most suitable accelerator based on response time and throughput, allocating tasks to primary or secondary accelerators, and dividing tasks for parallel execution across multiple nodes, thereby optimizing performance and overcoming memory size limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expensive GPUs are used to provide deep learning cloud services, then service quality and performance are improved, but the cost of building the cluster increases significantly
Solution Approach 1:
The patent segments the accelerator pool into heterogeneous components (GPUs, FPUs, TPUs, etc.) and divides deep learning tasks into multiple sub-tasks that can be distributed across different accelerator types. This allows the system to use a mix of expensive and inexpensive accelerators rather than requiring all accelerators to be high-cost GPUs, thereby reducing overall cluster building cost while maintaining service quality.
Solution Approach 2:
The patent creates a universal accelerator management system that can allocate and manage multiple types of accelerators (GPUs, FPUs, TPUs, and other heterogeneous accelerators) for deep learning tasks. The system provides multi-functional support for different accelerator architectures through a unified interface, allowing lower-cost heterogeneous accelerators to be used alongside or instead of expensive GPUs, thus reducing cluster building costs while maintaining service capability.
2Ease of manufacture
If heterogeneous accelerators are used to reduce cost, then cluster building cost is reduced, but system performance and efficiency may deteriorate
Solution Approach 1:
The patent implements a dynamic task allocation system that automatically selects the most appropriate accelerator type for each deep learning task based on real-time conditions. The system can dynamically adjust which heterogeneous accelerators (GPUs, FPUs, TPUs, etc.) are used for specific tasks, optimizing processing efficiency while utilizing lower-cost accelerators. This dynamic adaptation ensures that performance is maximized for each task while maintaining cost efficiency across the overall cluster.
Solution Approach 2:
The patent changes the parameter of accelerator selection from static (fixed to use only high-performance GPUs) to dynamic (selecting from heterogeneous accelerators based on task characteristics). The system evaluates task parameters and matches them with appropriate accelerator capabilities, allowing lower-cost heterogeneous accelerators to be utilized effectively. This parameter change enables the system to maintain high processing efficiency while reducing dependency on expensive GPUs.
3Speed
If tasks are allocated to specific accelerators based on performance optimization, then processing speed is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary accelerator management system that handles the complexity of heterogeneous accelerator allocation. This intermediary layer sits between the deep learning tasks and the physical accelerators, automatically selecting and allocating appropriate accelerators (GPUs, FPUs, TPUs, etc.) based on task requirements. This intermediary absorbs the management complexity, allowing tasks to be allocated optimally for speed without requiring users to directly manage the heterogeneous accelerator infrastructure.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors accelerator performance, task completion times, and resource utilization. Based on this feedback, the system learns and adjusts its task allocation strategy to optimize response time. The feedback loop allows the system to automatically adapt to changing conditions and improve allocation decisions over time, maintaining high processing speed while managing the complexity of heterogeneous accelerator coordination through data-driven decisions.
Data Source
AI summary
Provided is a method for processing a deep learning task through a deep learning framework. The method may include executing, by a computing device, a deep learning task on a deep learning framework, determining at least one of a primary accelerator or a secondary accelerator to execute the deep learning task, allocating the deep learning task to at least one of the determined primary accelerator or secondary accelerator, and generating, based on a result processed by at least one of the determined primary accelerator or secondary accelerator, result data for the deep learning task. The secondary accelerator may be an accelerator heterogeneous to the primary accelerator.