Compass: A Decentralized Scheduler for Latency-Sensitive ML Workflows

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.

摘要

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.

統計資料

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.

引述

"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.

從以下內容提煉的關鍵洞見

Compass

by Yuting Yang,... 於 arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17652.pdf

深入探究

How does Compass handle dynamic changes in system state during task execution

Compass handles dynamic changes in system state during task execution by employing a decentralized approach to scheduling. This means that each worker node has the autonomy to make scheduling decisions based on real-time information about its own load, GPU memory contents, and task queue. As tasks are executed and new information becomes available, Compass adjusts its scheduling decisions dynamically without needing centralized coordination. This flexibility allows Compass to adapt to changing conditions within the system, optimizing task assignments for reduced latency and efficient resource utilization.

What potential challenges could arise from fully decentralized scheduling compared to centralized approaches

Fully decentralized scheduling in Compass may present challenges compared to centralized approaches. One potential challenge is the increased complexity of managing a distributed system where each node can independently schedule tasks on any other node. Coordination between nodes becomes crucial to ensure optimal performance and avoid conflicts or inefficiencies due to lack of global visibility into the entire system state. Additionally, decentralized scheduling may introduce higher communication overhead as nodes need to exchange information frequently for effective decision-making.

How might advancements in hardware technology impact Compass's effectiveness over time

Advancements in hardware technology could impact Compass's effectiveness over time by potentially improving its performance and efficiency. For example, faster GPUs with larger memory capacities could enhance Compass's ability to cache more ML models closer to processing units, reducing data transfer delays and improving overall job completion times. Similarly, advancements in networking technologies such as faster interconnects or lower-latency protocols could further optimize data transmission between nodes in a distributed environment, enhancing Compass's scalability and responsiveness. Overall, as hardware technology evolves, Compass may leverage these advancements to deliver even better results in terms of latency-sensitive ML workflow processing.