Dooly reduces LLM inference profiling costs by 56.4% via configuration-agnostic taint-based labeling and selective database reuse, delivering simulation accuracy within 5% MAPE for TTFT and 8% for TPOT across 12 models.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
EnergAIzer predicts module-level GPU utilization from structured kernel patterns and feeds it into a power model to estimate dynamic power with 8% error on Ampere GPUs and 7% on H100 forecasts.
Wattlytics is a public web platform that integrates benchmark-driven GPU performance scaling, DVFS-aware power modeling, and TCO analysis to support informed HPC cluster design and procurement decisions.
citing papers explorer
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Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation
Dooly reduces LLM inference profiling costs by 56.4% via configuration-agnostic taint-based labeling and selective database reuse, delivering simulation accuracy within 5% MAPE for TTFT and 8% for TPOT across 12 models.
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EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads
EnergAIzer predicts module-level GPU utilization from structured kernel patterns and feeds it into a power model to estimate dynamic power with 8% error on Ampere GPUs and 7% on H100 forecasts.
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Wattlytics: A Web Platform for Co-Optimizing Performance, Energy, and TCO in HPC Clusters
Wattlytics is a public web platform that integrates benchmark-driven GPU performance scaling, DVFS-aware power modeling, and TCO analysis to support informed HPC cluster design and procurement decisions.