BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.BUTTER-E——BUTTER实证深度学习数据集能耗数据集，将功率计采集的节点级能耗数据补充至BUTTER实证深度学习数据集的主实验批次中。本数据集涵盖63527次独立实验产生的能耗与性能数据，包含30582种不同配置：13个数据集、20种模型规模（以可训练参数数量衡量）、8种网络“结构形态”，以及14种网络深度，所有数据均通过节点级功率计在CPU与GPU硬件环境下采集得到。该数据集揭示了数据集规模、网络结构与能耗之间的复杂关联，并凸显了缓存效应的影响。BUTTER-E旨在与BUTTER数据集配合使用（详见下文“BUTTER - Empirical Deep Learning Dataset on OEDI”资源），后者仅刻画了48.3万个不同全连接神经网络的性能表现，未包含能耗测量数据。

搜集汇总

背景与挑战

背景概述

BUTTER-E数据集是BUTTER实证深度学习数据集的能源消耗补充数据，通过瓦特计测量节点级能耗，覆盖超过6.3万次实验运行和3万多种配置，包括不同数据集、规模、网络形状和深度，在CPU和GPU硬件上收集。该数据集旨在揭示数据集大小、网络结构与能源使用之间的复杂关系，并强调缓存效应的影响，可与BUTTER数据集结合以提供更全面的性能与能耗分析。