From bf30c3fe9a5559385c9f414c6625f445fc721652 Mon Sep 17 00:00:00 2001 From: Ruofan Wu Date: Mon, 26 Jan 2026 22:47:17 -0500 Subject: [PATCH] Kareus arXiv --- source/_data/SymbioticLab.bib | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/source/_data/SymbioticLab.bib b/source/_data/SymbioticLab.bib index 283f37bc..240e2a10 100644 --- a/source/_data/SymbioticLab.bib +++ b/source/_data/SymbioticLab.bib @@ -2238,3 +2238,23 @@ @article{sigdgx:cacm25 publist_confkey = {CACM:69(1)}, publist_link = {paper || https://dl.acm.org/doi/full/10.1145/3736713}, } + +@Article{kareus:arxiv26, + author = {Ruofan Wu and Jae-Won Chung and Mosharaf Chowdhury}, + title = {{Kareus}: Joint Reduction of Dynamic and Static Energy in Large Model Training}, + year = {2026}, + month = {Jan}, + volume = {abs/2601.17654}, + archivePrefix = {arXiv}, + eprint = {2601.17654}, + url = {https://arxiv.org/abs/2601.17654}, + publist_confkey = {arXiv:2601.17654}, + publist_link = {paper || https://arxiv.org/abs/2601.17654}, + publist_topic = {Systems + AI}, + publist_topic = {Energy-Efficient Systems}, + publist_abstract = { +The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a result, energy has become an expensive, contended resource that requires explicit management and optimization. Although recent works have made significant progress in large model training optimization, they focus only on a single aspect of energy consumption: dynamic or static energy. + +We find that fine-grained kernel scheduling and frequency scaling jointly and interdependently impact both dynamic and static energy consumption. Based on this finding, we design Kareus, a training system that pushes the time--energy tradeoff frontier by optimizing both aspects. Kareus decomposes the intractable joint optimization problem into local, partition-based subproblems. It then uses a multi-pass multi-objective optimization algorithm to find execution schedules that push the time--energy tradeoff frontier. Compared to the state of the art, Kareus reduces training energy by up to 28.3% at the same training time, or reduces training time by up to 27.5% at the same energy consumption. + } +}