StageFrontier computes an exact additive accounting of exposed step time in distributed training by taking the frontier of per-rank coarse stage durations reported with unsynchronized CPU wall clocks.
MLPerf training benchmark
4 Pith papers cite this work, alongside 172 external citations. Polarity classification is still indexing.
4
Pith papers citing it
172
external citations · external index
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2026 4roles
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Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
OFU is a hardware-counter metric that approximates application MFU to within 2 percentage points after tile correction and shows r=0.78 correlation on 608 production jobs.
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.
citing papers explorer
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StageFrontier: Synchronization-Aware Stage Accounting for Distributed ML Training
StageFrontier computes an exact additive accounting of exposed step time in distributed training by taking the frontier of per-rank coarse stage durations reported with unsynchronized CPU wall clocks.
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Benchmarking Compound AI Applications for Hardware-Software Co-Design
Introduces a benchmarking suite for compound AI applications to support cross-stack performance, cost, and resource analysis for hardware-software co-design.
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Instant GPU Efficiency Visibility at Fleet Scale
OFU is a hardware-counter metric that approximates application MFU to within 2 percentage points after tile correction and shows r=0.78 correlation on 608 production jobs.
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Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
High-resolution power profiles for AI workloads on H100 GPUs are measured and scaled to whole-facility energy demand using a bottom-up model, with the dataset made public.