Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision (2205.11913v3)

Published 24 May 2022 in cs.DC, cs.AI, and cs.LG

Abstract: Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constructed into a GPU datacenter. An efficient scheduler design for such GPU datacenter is crucially important to reduce the operational cost and improve resource utilization. However, traditional approaches designed for big data or high performance computing workloads can not support DL workloads to fully utilize the GPU resources. Recently, substantial schedulers are proposed to tailor for DL workloads in GPU datacenters. This paper surveys existing research efforts for both training and inference workloads. We primarily present how existing schedulers facilitate the respective workloads from the scheduling objectives and resource consumption features. Finally, we prospect several promising future research directions. More detailed summary with the surveyed paper and code links can be found at our project website: https://github.com/S-Lab-System-Group/Awesome-DL-Scheduling-Papers

Citations (20)

Summary

  • The paper categorizes deep learning workload scheduling challenges, emphasizing unique training and inference complexities in GPU datacenters.
  • It demonstrates tailored scheduling techniques, including reinforcement learning models and dynamic GPU sharing, to enhance timing and cost efficiency.
  • The study advocates for future research on predictive analytics and adaptive scheduling to optimize energy use and resource provisioning.

Deep Learning Workload Scheduling in GPU Datacenters: Insights and Implications

The paper "Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision" systematically surveys the methodologies and challenges associated with scheduling deep learning (DL) workloads in GPU datacenters. With the proliferation of DL applications across industries, efficient resource management in GPU datacenters has become imperative to mitigate operational costs and optimize resource utilization. This work provides a comprehensive taxonomy of existing scheduling mechanisms, focusing on both training and inference workloads, and outlines future research directions that hold promise for improving DL workload management in datacenters.

Deep Learning Workloads and Resource Management

The paper identifies the unique challenges posed by DL workloads, emphasizing GPU heterogeneity, communication sensitivity, iterative workload nature, and gang scheduling. DL workloads, particularly training jobs, demand significant computational resources due to their complexity and iterative nature. The paper emphasizes that traditional big data or HPC scheduling strategies are ineffective for DL workloads due to their distinct requirements. Instead, tailored scheduling solutions have emerged, focusing on maximizing resource utilization and operational efficiency while ensuring fairness and deadline adherence.

Taxonomy and Scheduling Objectives

The paper categorizes DL scheduling research efforts based on objectives such as timing efficiency, cost efficiency, fairness, and deadline guarantees. Timing efficiency strategies aim to reduce job completion times through innovative queue management and resource allocation mechanisms. Whereas cost efficiency focuses on minimizing energy and operational costs through strategic resource provisioning and utilization of lower-cost cloud resources. Fairness in resource allocation remains a critical concern, addressing heterogeneous GPU resource allocation while ensuring equitable access among competing jobs.

The implications of scheduling decisions are profound, particularly in balancing latency, accuracy, and cost, especially for inference workloads. The research highlights how batching, model caching, and dynamic resource scaling can mitigate underutilization in inference scenarios, thus improving overall system throughput.

Scheduling Techniques and Resource Features

The survey examines advanced scheduling techniques such as RL-based models, which provide adaptive and efficient scheduling decisions by learning from workload characteristics. Scheduling solutions also leverage GPU sharing and elasticity, dynamically adjusting resource allocation based on workload demands and priorities. The paper presents systems that take advantage of modern GPU capabilities, enabling fine-grained multiplexing and improved data communication through advanced interconnects.

Future Directions

The paper outlines promising directions for future research, particularly in optimizing energy consumption for inference workloads and exploring model partitioning to accommodate large DL models with limited resources. Additionally, the integration of predictive analytics for workload forecasting and resource provisioning is recognized as a critical area for enhancing system responsiveness and efficiency.

The paper concludes by advocating for innovative solutions that marry system-level insights with DL model requirements, ensuring continued advancements in scheduling techniques that adapt to the evolving demands of GPU datacenters. As DL applications become increasingly integral to varied domains, efficient scheduling in datacenters will be crucial in sustaining performance improvements and operational scalability.

Github Logo Streamline Icon: https://streamlinehq.com