PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
Regression quantiles,
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A quantile-regression ensemble with safety factor reduces under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on SAP build data.
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PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
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Optimizing Memory Allocation in Distributed Clusters with Predictive Modeling
A quantile-regression ensemble with safety factor reduces under-allocated jobs from 4.17% to 2.89% and average overallocation from 148% to 44.51% on SAP build data.