With the rise of AI in recent years and the increase in complexity of the models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly more potent accelerators and the use of large compute clusters. However, the gain in prediction accuracy from large models trained on distributed and accelerated systems comes at the price of a substantial increase in energy demand, and researchers have started questioning the environmental friendliness of such AI methods at scale. Consequently, energy efficiency plays an important role for AI model developers and infrastructure operators alike. The energy consumption of AI workloads depends on the model implementation and the utilized hardware. Therefore, accurate measurements of the power draw of AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware. To this end, we present measurements of the energy consumption of two typical applications of deep learning models on different types of compute nodes. Our results indicate that 1. deriving energy consumption directly from runtime is not accurate, but the consumption of the compute node needs to be considered regarding its composition; 2. neglecting accelerator hardware on mixed nodes results in overproportional inefficiency regarding energy consumption; 3. energy consumption of model training and inference should be considered separately - while training on GPUs outperforms all other node types regarding both runtime and energy consumption, inference on CPU nodes can be comparably efficient. One advantage of our approach is that the information on energy consumption is available to all users of the supercomputer, enabling an easy transfer to other workloads alongside a raise in user-awareness of energy consumption.
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在X射线游离电子激光器(XFELS)处的单粒子成像(SPI)特别适合于确定其本地环境中颗粒的3D结构。对于成功的重建,必须从大量获取的图案中分离出来的衍射模式。我们建议将此任务作为图像分类问题制定,并使用卷积神经网络(CNN)架构来解决它。开发了两个CNN配置:一个最大化F1分数的CNN配置和强调高召回的一个配置。我们还将CNN与期望最大化(EM)选择以及尺寸过滤结合起来。我们观察到,我们的CNN选择在我们之前的工作中使用的电子选择的功率谱密度函数的对比度较低。但是,基于CNN的选择的重建提供了类似的结果。将CNN引入SPI实验允许简化重建管道,使研究人员能够在飞行中对模式进行分类,并且因此,它们使他们能够严格控制其实验的持续时间。我们认为,在描述的SPI分析工作流程中提出基于非标准的人工智能(AI)解决方案可能对SPI实验的未来发展有益。
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The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. As such, ionospheric models play a crucial part in improving our understanding of ionospheric behavior in response to different spatial, temporal, and space weather conditions. This work represents an initial attempt to construct an electron density prediction model of the Martian ionosphere using machine learning. The model targets the ionosphere at solar zenith ranging from 70 to 90 degrees, and as such only utilizes observations from the Mars Global Surveyor mission. The performance of different machine learning methods was compared in terms of root mean square error, coefficient of determination, and mean absolute error. The bagged regression trees method performed best out of all the evaluated methods. Furthermore, the optimized bagged regression trees model outperformed other Martian ionosphere models from the literature (MIRI and NeMars) in finding the peak electron density value, and the peak density height in terms of root-mean-square error and mean absolute error.
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电离层中存在的电子密度不规则性会引起全球导航卫星系统(GNSS)信号的显着波动。信号功率的波动称为振幅闪烁,可以通过S4指数进行监测。当实时数据不可用时,基于历史S4索引数据的幅度闪烁的严重程度是有益的。在这项工作中,我们研究了使用单个GPS闪烁监测接收器中使用历史数据来训练机器学习(ML)模型的可能性参数。评估了六种不同的ML型号,其中包装的树模型是其中最准确的,使用平衡数据集获得了预测准确性$ 81 \%$,使用不平衡数据集获得了$ 97 \%$ $。
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这项工作提出了诸如卷积神经网络(CNN),长短期记忆(LSTM),门控复发单元(GRU),它们的混合动力和情绪的浅学习分类器等深度学习模型的性能的详细比较阿拉伯语评论分析。另外,比较包括最先进的模型,例如变压器架构和阿拉伯的预先训练模型。本研究中使用的数据集是多方面的阿拉伯语酒店和书评数据集,这些数据集是阿拉伯评论的一些最大的公共数据集。结果表明,二元和多标签分类的浅层学习表现优于浅层学习,与文献中报告的类似工作的结果相比。结果中的这种差异是由数据集大小引起的,因为我们发现它与深度学习模型的性能成比例。在准确性和F1分数方面分析了深层和浅层学习技术的性能。最好的浅学习技术是随机森林,后跟决策树,以及adaboost。深度学习模型类似地使用默认的嵌入层进行,而变压器模型在增强Arabert时表现最佳。
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我们介绍了一种从电磁(EM)采样量热计收集的数据重建多个淋浴的第一算法。这种探测器广泛用于高能量物理中,以测量进入粒子的能量和运动学。在这项工作中,我们考虑许多电子通过乳液云室(ECC)砖的情况,启动电子诱导的电磁淋浴,这可以是长曝光时间或大输入粒子通量的情况。例如,船舶实验计划使用乳液检测器进行暗物质搜索和中微子物理调查。船舶实验的预期完整通量约为10 ^ 20颗粒。为了降低与替换ECC砖和离线数据的实验的成本(乳液扫描),决定增加暴露时间。因此,我们希望观察大量重叠阵雨,将EM淋浴重建变为挑战的点云分割问题。我们的重建管线包括图形神经网络,其预测邻接矩阵和聚类算法。我们提出了一种新的层型(乳液CONV),其考虑了ECC砖中淋浴开发的几何特性。对于重叠阵雨的聚类,我们使用修改后的基于分层密度的聚类算法。我们的方法不使用有关进入粒子的任何先前信息,并识别乳液检测器中的高达87%的电磁淋浴。用于重建电磁淋浴的算法的主要测试台将是SND @ LHC。
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