Dynamic Scheduling Algorithm for Distributed Deep Learning Training Jobs
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Solution Overview
Problem
Current deep learning training systems face inefficiencies in resource utilization due to the lack of effective dynamic scheduling in multi-server environments, particularly in ring architectures, leading to suboptimal performance and longer compute times.
Innovation Solution
Adaptation of dynamic scheduling algorithms to ring architectures using a doubling heuristic, which iteratively assigns additional processing units to jobs based on time improvement calculations, optimizing resource allocation and job completion times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used in deep learning training, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring training job progress, resource availability, and performance metrics to adjust GPU assignments in real-time. The scheduler transitions from static to dynamic allocation, allowing resources to be reallocated based on current system state and job priorities, thereby improving resource utilization without requiring overly complex infrastructure
Solution Approach 2:
The scheduling system incorporates feedback mechanisms that monitor training job performance, resource usage patterns, and completion rates. This feedback is used to continuously optimize resource allocation decisions, enabling the system to adapt to changing conditions and improve efficiency while maintaining manageable complexity through data-driven decisions
2Speed
If more GPUs are allocated to a training job, then training speed is improved, but resource availability for other jobs deteriorates
Solution Approach 1:
The system dynamically adjusts GPU allocation based on real-time monitoring of training job progress and resource availability. When jobs are completed or paused, previously allocated GPUs become available for reallocation to other waiting jobs, enabling the system to optimize training speed for active jobs while maintaining flexibility for multiple concurrent workloads
Solution Approach 2:
The scheduler changes the parameter of GPU allocation dynamically based on job priority, progress, and availability. By adjusting the number of GPUs assigned to each job according to changing conditions, the system achieves high training speeds for priority jobs while preserving resource availability for other tasks through flexible parameter modification
3Productivity
If dynamic scheduling is implemented, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The scheduling system operates autonomously by automatically monitoring job progress, detecting resource availability changes, and reallocating GPUs without manual intervention. This self-service capability improves resource utilization while keeping the control logic centralized and manageable, avoiding the need for complex manual scheduling mechanisms
Solution Approach 2:
The system uses feedback from job monitoring and resource tracking to automatically make scheduling decisions. By basing allocations on actual system state and performance data rather than static rules, the system achieves high resource utilization with relatively simple decision logic driven by observable metrics
Data Source
AI summary
A scheduling algorithm for scheduling training of deep neural network (DNN) weights on processing units identifies a next job to provisionally assign a processing unit (PU) based on a doubling heuristic. The doubling heuristic makes use of an estimated number of training sets needed to complete training of weights for a given job and/or a training speed function which indicates how fast the weights are converging. The scheduling algorithm solves a problem of efficiently assigning PUs when multiple DNN weight data structures must be trained efficiently. In some embodiments, the training of the weights uses a ring-based message passing architecture. In some embodiments, performance using a nested loop approach or nested loop fashion is provided. In inner iterations of the nested loop, PUs are scheduled and jobs are launched or re-started. In outer iterations of the nested loop, jobs are stopped, parameters are updated and the inner iteration is re-entered.