Designing Lock-Free GPU Runtime Scheduling at Scale Using Dual Roaring Bitmaps, Sse
In high-performance AI cloud orchestration, scheduling workloads to available GPU resources is a major low-latency challenge.
Imagine you are running a serverless AI cluster. A workload request arrives requesting a specialized runtime (e.g., an H100 instance pre-loaded with Llama-3-70B on vLLM). The database says GPU Rack #4 has 3 instances available. Meanwhile, dozens of other scheduling agents are scanning the rack, all attempting to claim those exact same slots for incoming workloads.
If your scheduler displays these runtime slots as "Available" to everyone, you face scheduling starvation/overbooking workloads are routed to nodes that have just run out of memory, leading to failed requests, cold starts, and container launch timeouts. If you query your transactional database for every incoming scheduling check to verify real-time GPU availability, your database CPU hits 100% and crashes under the load.
To solve this, we don't need to know the exactmicro-quantities of free memory in...
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