One signal

Update

What changed

NVIDIA has supplied new performance figures for Topograph, its system for placing AI workloads according to the physical layout of GPUs and their interconnects. In a test, the company says a constraint-aware allocator using Topograph increased GPU utilisation by up to 33 percentage points compared with first-in, first-out scheduling.

NVIDIA also reports a 105% increase in priority-weighted output in that comparison. The figures add a measurable result to the earlier explanation of Topograph’s purpose: keeping workloads close to the links and hardware they need, rather than treating every available GPU as interchangeable.

The result is a vendor-supplied test, not an independent production benchmark, and the evidence does not specify the cluster configuration, workload mix or baseline utilisation. Even so, it gives operators a more useful question than “does topology-aware scheduling sound sensible?” They can now ask whether their own workloads show a similar gap when scheduling follows the wiring rather than a queue.

Sources and evidence

• Topology-Aware Workload Scheduling with NVIDIA Topograph: NVIDIA says a constraint-aware Topograph allocator increased GPU utilisation by up to 33 percentage points and priority-weighted output by 105% compared with FIFO scheduling in its test.

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