At NVIDIA, we take pride in tackling complex infrastructure challenges with precision and innovation. When Volcano faced GPU underutilization in their NVIDIA…
Overview
This article discusses strategies for preventing GPU fragmentation in the Volcano Scheduler, focusing on an enhanced scheduling approach that integrates bin-packing with gang scheduling. It highlights the challenges faced in a Kubernetes cluster and the successful implementation that led to improved GPU occupancy and resource utilization.
What You'll Learn
1
How to integrate a bin-packing algorithm into the Volcano Scheduler
2
Why GPU fragmentation occurs in Kubernetes clusters
3
When to apply optimized workload placement strategies
Prerequisites & Requirements
- Understanding of Kubernetes and GPU scheduling
- Familiarity with Volcano Scheduler(optional)
Key Questions Answered
How can GPU fragmentation be prevented in Kubernetes clusters?
GPU fragmentation can be prevented by integrating a bin-packing algorithm with the Volcano Scheduler, which optimizes workload placement to ensure that nodes are fully utilized before moving to others. This approach addresses the inefficiencies caused by gang scheduling's all-or-nothing principle and random workload placement.
What were the results of implementing the new scheduling strategy?
The implementation of the new scheduling strategy resulted in an average GPU occupancy of 90%, significantly exceeding the contractual requirement of 80%. This improvement also increased the number of fully free nodes, enhancing resource availability for large-scale training jobs.
What challenges were faced with the default gang scheduling?
The default gang scheduling led to bottlenecks as distributed jobs requiring multiple GPUs were queued indefinitely unless all resources were available. Additionally, random placement of workloads resulted in GPU fragmentation, leaving nodes partially occupied and unusable for larger jobs.
What specific scheduling techniques were used to improve GPU utilization?
The scheduling techniques included workload prioritization based on resource importance, optimized placement through bin-packing to consolidate workloads, and maintaining gang scheduling's principle while enhancing resource consolidation. This combination maximized node utilization and minimized fragmentation.
Key Statistics & Figures
Average GPU occupancy
90%
Achieved after implementing the new scheduling strategy, exceeding the contractual requirement of 80%.
Number of fully free nodes
214 nodes
This increase allowed for seamless scheduling of large-scale training jobs.
Technologies & Tools
Scheduling
Volcano Scheduler
Used for managing GPU workloads in Kubernetes clusters.
Cloud Computing
Nvidia Dgx Cloud
Provided the infrastructure for the Kubernetes cluster.
Key Actionable Insights
1Implementing a bin-packing algorithm can significantly enhance resource utilization in GPU clusters.This approach allows for better workload consolidation, ensuring that nodes are fully utilized before moving to others, which is crucial for maximizing efficiency in multi-GPU environments.
2Regularly monitor GPU occupancy and fragmentation levels to proactively address scheduling inefficiencies.By keeping track of these metrics, organizations can adjust their scheduling strategies in real-time, preventing bottlenecks and ensuring optimal resource allocation.
3Consider integrating advanced scheduling techniques into existing systems to accommodate diverse workloads.This flexibility allows organizations to adapt to varying workload requirements without overhauling their infrastructure, thus enhancing overall performance.
Common Pitfalls
1
Relying solely on default gang scheduling can lead to significant GPU fragmentation.
This occurs because gang scheduling's all-or-nothing approach can result in many nodes being partially occupied, making them unusable for larger jobs. To avoid this, integrating smarter scheduling techniques is essential.
Related Concepts
Distributed Systems Optimization
Resource Management Strategies
Advanced Scheduling Algorithms