CUDA latency matrix measurements produce unprivileged certificates that fingerprint individual GPU dies, recover cross-generation topology, and bind to datacenter location via public network probes.
When light bends to the collective will: A theory and vision for adaptive photonic scale-up domains
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Bridge reduces All-to-All completion time by typically 3x to 10x and improves AllReduce by up to 6.6x over Ring by reusing optical subrings across multiple steps in reconfigurable networks.
A greedy max-weight decomposition strategy for MoE all-to-all communication on photonic fabrics improves overlap efficiency and reduces compute overheads compared to BvN by bounding the number of matchings.
ReTri achieves all-to-all in ⌈log₃ n⌉ phases for ORNs by co-designing bidirectional exchanges and reconfiguration strategy, with simulations showing up to 10× improvement over static and 2.1× over prior reconfigurable Bruck.
Sema reduces uplink bandwidth by 64x for audio and 130-210x for screenshots while keeping multimodal agent task accuracy within 0.7 percentage points of raw baselines in WAN simulations.
citing papers explorer
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Unprivileged Topology Certificates for Cloud GPU Attestation
CUDA latency matrix measurements produce unprivileged certificates that fingerprint individual GPU dies, recover cross-generation topology, and bind to datacenter location via public network probes.
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Birkhoff Decompositions and Photonic Interconnects Wait! Don't Forget the Compute!
A greedy max-weight decomposition strategy for MoE all-to-all communication on photonic fabrics improves overlap efficiency and reduces compute overheads compared to BvN by bounding the number of matchings.
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Revisiting Bruck: Phase-Efficient All-to-All Communication in Reconfigurable Networks
ReTri achieves all-to-all in ⌈log₃ n⌉ phases for ORNs by co-designing bidirectional exchanges and reconfiguration strategy, with simulations showing up to 10× improvement over static and 2.1× over prior reconfigurable Bruck.
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Sema: Semantic Transport for Real-Time Multimodal Agents
Sema reduces uplink bandwidth by 64x for audio and 130-210x for screenshots while keeping multimodal agent task accuracy within 0.7 percentage points of raw baselines in WAN simulations.