核心概念
Compass proposes a novel framework to reduce job latency in ML workflows by optimizing task placement and GPU memory management. The decentralized approach outperforms centralized alternatives with low overheads.
要約
Compass introduces a decentralized scheduler for ML workflows, focusing on reducing job latency and optimizing resource utilization. The system addresses challenges like data dependencies and GPU memory management, showcasing significant improvements in completion times while requiring fewer resources. By unifying task placement and GPU cache management, Compass offers a promising solution for edge ML applications.
The content discusses the motivation behind Compass's development, the challenges faced in interactive applications, and the unique features of the proposed framework. It highlights experiments showing reduced latency and efficient resource utilization compared to traditional schedulers.
Compass leverages dataflow graphs to represent ML workflows, emphasizing the importance of GPU memory as a cache and the impact of cache hit rates on performance. The system's architecture includes components like Workflow Profiling, Task Dispatcher, and GPU Memory Manager to optimize task scheduling and execution.
Overall, Compass demonstrates superior performance in reducing job latency while efficiently managing resources in distributed systems handling complex ML queries.
統計
Comparison with other state-of-the-art schedulers shows a significant reduction in completion times.
In one case study, just half the servers were needed for processing the same workload.
Model parameters can be hundreds of megabytes in size.
GPU memories are smaller and expensive compared to host memories.
The cache hit rate is considered an important metric for performance optimization.
引用
"The main contributions of this paper include a decentralized scheduler that reduces end-to-end latency for ML applications on edge clusters." - Yuting Yang et al.
"Compass plays two roles: platform-level GPU cache management and job/task placement." - Yuting Yang et al.