Context-Aware Deep Learning Autotuning with Stateless Scheduling
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Solution Overview
Problem
Existing autotuning frameworks for deep learning models on specialized hardware suffer from inefficient resource utilization due to sequential and monolithic design, leading to idle resources and performance degradation from task interference.
Innovation Solution
A redesigned autotuning framework that decomposes tasks into functional sub-procedures and schedules them simultaneously on different resources, using a context-aware scheduler with Shortest Job First (SJF) policy and Multi-Process Service (MPS) capability to ensure exclusive resource access and reduce queuing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autotuning frameworks use sequential and monolithic design, then implementation is simple, but resource utilization is inefficient and resources become idle
Solution Approach 1:
The autotuning framework is divided into independent functional sub-procedures (select, build, profile, update model) that can be executed in parallel on different resources. This segmentation allows each sub-procedure to be scheduled independently, eliminating idle resources and improving overall productivity while maintaining manageable complexity through modular design.
2Reliability
If autotuning frameworks use sequential design, then task interference is minimized, but autotuning completion time increases
Solution Approach 1:
By segmenting the autotuning process into independent sub-procedures, the system can execute multiple tasks simultaneously on different resources without interference. The scheduler manages these parallel tasks using Shortest Job First policy, ensuring that while tasks run in parallel (reducing completion time), resource access is controlled to prevent interference.
Solution Approach 2:
The scheduler dynamically assigns tasks to resources based on availability and task characteristics using Shortest Job First policy. This dynamic scheduling allows the system to adapt to changing resource states, minimizing queuing delays and ensuring efficient completion while preventing task interference through intelligent resource allocation.
3Manufacturing precision
If manual optimizations are used, then hardware knowledge is required, but optimization efficiency decreases and device-specific optimizations may be missed
Solution Approach 1:
The autotuning framework performs self-optimization by automatically selecting, building, profiling, and updating models without requiring manual intervention. The system uses built-in schedulers and resource managers to autonomously execute optimization tasks, achieving both high accuracy in device-specific optimizations and efficient productivity by eliminating manual optimization processes.
Data Source
AI summary
Systems and methods are provided for improving autotuning procedures using stateless processing with a remote key-value store. For example, the system can implement a task launcher, a scheduler, and an agent to launch, schedule, and execute decomposed autotuning stages, respectively. The scheduling policy implemented by the scheduler may perform operations beyond a simple scheduling policy (e.g., a FIFO-based scheduling policy), which produces a high queuing delay. Compared to the traditional systems, by leveraging autotuning specific domain knowledge, queueing delay is reduced and resource utilization is improved.