EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hugging Face Blog·Aug 17·5 min read
Same Cluster, 33 Points More Utilization: What Changed Was the Order
TL;DR
A new constraint-aware GPU allocator significantly improved GPU utilization by up to 33 percentage points compared to a FIFO scheduler, without any hardware changes. The key to this improvement was the order of allocation decisions made for GPU jobs.
✦ Why It Matters
Consider implementing a constraint-aware GPU allocator to enhance your system's GPU utilization today.
Key Takeaways
- Adopt a constraint-aware approach for GPU scheduling.
- Prioritize jobs based on their urgency and resource needs.
- Forecast real-time demand to optimize GPU availability.
- Regularly review and adjust scheduling strategies based on workload changes.
How It Works
The allocator treats real-time demand as a curve rather than a fixed ceiling, allowing it to allocate GPUs dynamically based on current demand. It uses a formal model to ensure that all allocations respect defined constraints, such as contiguous GPU blocks for batch jobs and priority-based placements.
This holistic view enables the allocator to maximize GPU utilization by ensuring that jobs fit into the available capacity effectively.