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Dense Server Design for Immersion Cooling 浸入式冷却的密集服务器设计
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687965
Milin Kodnongbua, Zachary Englhardt, Ricardo Bianchini, Rodrigo Fonseca, Alvin Lebeck, Daniel S. Berger, Vikram Iyer, Fiodar Kazhamiaka, Adriana Schulz
The growing demands for computational power in cloud computing have led to a significant increase in the deployment of high-performance servers. The growing power consumption of servers and the heat they produce is on track to outpace the capacity of conventional air cooling systems, necessitating more efficient cooling solutions such as liquid immersion cooling. The superior heat exchange capabilities of immersion cooling both eliminates the need for bulky heat sinks, fans, and air flow channels while also unlocking the potential go beyond conventional 2D blade servers to three-dimensional designs. In this work, we present a computational framework to explore designs of servers in three-dimensional space, specifically targeting the maximization of server density within immersion cooling tanks. Our tool is designed to handle a variety of physical and electrical server design constraints. We demonstrate our optimized designs can reduce server volume by 25--52% compared to traditional flat server designs. This increased density reduces land usage as well as the amount of liquid used for immersion, with significant reduction in the carbon emissions embodied in datacenter buildings. We further create physical prototypes to simulate dense server designs and perform real-world experiments in an immersion cooling tank demonstrating they operate at safe temperatures. This approach marks a critical step forward in sustainable and efficient datacenter management.
云计算对计算能力的需求不断增长,导致高性能服务器的部署大幅增加。服务器日益增长的功耗及其产生的热量将超过传统空气冷却系统的能力,因此需要更高效的冷却解决方案,如液态浸入式冷却。浸入式冷却具有卓越的热交换能力,既不需要笨重的散热器、风扇和气流通道,又能释放出超越传统二维刀片服务器的潜力,实现三维设计。在这项工作中,我们提出了一个计算框架,用于探索三维空间中的服务器设计,特别是针对浸入式冷却槽中服务器密度的最大化。我们的工具旨在处理各种物理和电气服务器设计约束。与传统的平面服务器设计相比,我们的优化设计可将服务器体积减少 25-52%。密度的增加减少了土地使用量以及用于浸入的液体量,从而显著降低了数据中心建筑的碳排放量。我们进一步创建了物理原型来模拟高密度服务器设计,并在浸入式冷却槽中进行了实际实验,证明它们能在安全温度下运行。这种方法标志着在可持续和高效数据中心管理方面迈出了关键一步。
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引用次数: 0
An Impulse Ghost Fluid Method for Simulating Two-Phase Flows 模拟两相流体的脉冲幽灵流体法
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687963
Yuchen Sun, Linglai Chen, Weiyuan Zeng, Tao Du, Shiying Xiong, Bo Zhu
This paper introduces a two-phase interfacial fluid model based on the impulse variable to capture complex vorticity-interface interactions. Our key idea is to leverage bidirectional flow map theory to enhance the transport accuracy of both vorticity and interfaces simultaneously and address their coupling within a unified Eulerian framework. At the heart of our framework is an impulse ghost fluid method to solve the two-phase incompressible fluid characterized by its interfacial dynamics. To deal with the history-dependent jump of gauge variables across a dynamic interface, we develop a novel path integral formula empowered by spatiotemporal buffers to convert the history-dependent jump condition into a geometry-dependent jump condition when projecting impulse to velocity. We demonstrate the efficacy of our approach in simulating and visualizing several interface-vorticity interaction problems with cross-phase vortical evolution, including interfacial whirlpool, vortex ring reflection, and leapfrogging bubble rings.
本文介绍了一种基于脉冲变量的两相界面流体模型,以捕捉复杂的涡度-界面相互作用。我们的主要想法是利用双向流图理论同时提高涡度和界面的传输精度,并在统一的欧拉框架内解决它们之间的耦合问题。我们框架的核心是一种脉冲鬼流体方法,用于求解以界面动力学为特征的两相不可压缩流体。为了处理跨动态界面的量规变量的历史相关跃迁,我们开发了一种新颖的路径积分公式,该公式由时空缓冲器赋权,在将冲量投影到速度时,将历史相关跃迁条件转换为几何相关跃迁条件。我们在模拟和可视化几个具有跨相涡旋演变的界面-涡度相互作用问题(包括界面漩涡、涡环反射和跃迁气泡环)时证明了我们的方法的有效性。
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引用次数: 0
Computational Biomimetics of Winged Seeds 有翼种子的计算仿生学
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687899
Qiqin Le, Jiamu Bu, Yanke Qu, Bo Zhu, Tao Du
We develop a computational pipeline to facilitate the biomimetic design of winged seeds. Our approach leverages 3D scans of natural winged seeds to construct a bio-inspired design space by interpolating them with geodesic coordinates in the 3D diffeomorphism group. We formulate aerodynamic design tasks with probabilistic performance objectives and adapt a gradient-free optimizer to explore the design space and minimize the expectation of performance objectives efficiently and effectively. Our pipeline discovers novel winged seed designs that outperform natural counterparts in aerodynamic tasks, including long-distance dispersal and guided flight. We validate the physical fidelity of our pipeline by showcasing paper models of selected winged seeds in the design space and reporting their similar aerodynamic behaviors in simulation and reality.
我们开发了一种计算管道,以促进有翼种子的生物仿生设计。我们的方法利用天然有翼种子的三维扫描,通过在三维差分群中对它们进行大地坐标插值来构建生物启发设计空间。我们用概率性能目标制定空气动力学设计任务,并调整无梯度优化器来探索设计空间,高效率、高效益地最小化性能目标期望值。我们的管道发现了新型有翼种子设计,它们在空气动力学任务(包括远距离扩散和制导飞行)中的表现优于自然对应物。我们通过展示设计空间中选定有翼种子的纸质模型,验证了我们管道的物理保真度,并报告了它们在模拟和现实中类似的空气动力学行为。
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引用次数: 0
Online Neural Denoising with Cross-Regression for Interactive Rendering 用于交互式渲染的交叉回归在线去噪神经技术
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687938
Hajin Choi, Seokpyo Hong, Inwoo Ha, Nahyup Kang, Bochang Moon
Generating a rendered image sequence through Monte Carlo ray tracing is an appealing option when one aims to accurately simulate various lighting effects. Unfortunately, interactive rendering scenarios limit the allowable sample size for such sampling-based light transport algorithms, resulting in an unbiased but noisy image sequence. Image denoising has been widely adopted as a post-sampling process to convert such noisy image sequences into biased but temporally stable ones. The state-of-the-art strategy for interactive image denoising involves devising a deep neural network and training this network via supervised learning, i.e., optimizing the network parameters using training datasets that include an extensive set of image pairs (noisy and ground truth images). This paper adopts the prevalent approach for interactive image denoising, which relies on a neural network. However, instead of supervised learning, we propose a different learning strategy that trains our network parameters on the fly, i.e., updating them online using runtime image sequences. To achieve our denoising objective with online learning, we tailor local regression to a cross-regression form that can guide robust training of our denoising neural network. We demonstrate that our denoising framework effectively reduces noise in input image sequences while robustly preserving both geometric and non-geometric edges, without requiring the manual effort involved in preparing an external dataset.
通过蒙特卡洛光线追踪生成渲染图像序列,是精确模拟各种光照效果的理想选择。遗憾的是,交互式渲染场景限制了这种基于采样的光线传输算法所允许的样本大小,从而产生了无偏但有噪声的图像序列。图像去噪作为一种采样后处理方法已被广泛采用,以将此类噪声图像序列转换为有偏差但时间稳定的图像序列。最先进的交互式图像去噪策略包括设计一个深度神经网络,并通过监督学习来训练该网络,即使用包含大量图像对(噪声图像和地面实况图像)的训练数据集来优化网络参数。本文采用了目前流行的交互式图像去噪方法,即依赖于神经网络。不过,我们提出了一种不同的学习策略,即利用运行时图像序列在线更新网络参数,而不是监督学习。为了通过在线学习实现去噪目标,我们将局部回归调整为交叉回归形式,以指导去噪神经网络的稳健训练。我们证明,我们的去噪框架能有效减少输入图像序列中的噪声,同时稳健地保留几何和非几何边缘,而无需人工准备外部数据集。
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引用次数: 0
3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting 3DGSR: 利用三维高斯拼接进行隐式曲面重构
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687952
Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Xiuzhe Wu, Ziyi Yang, Yilun Chen, Jiangmiao Pang, Xiaojuan Qi
In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate details while inheriting the high efficiency and rendering quality of 3DGS. The key insight is to incorporate an implicit signed distance field (SDF) within 3D Gaussians for surface modeling, and to enable the alignment and joint optimization of both SDF and 3D Gaussians. To achieve this, we design coupling strategies that align and associate the SDF with 3D Gaussians, allowing for unified optimization and enforcing surface constraints on the 3D Gaussians. With alignment, optimizing the 3D Gaussians provides supervisory signals for SDF learning, enabling the reconstruction of intricate details. However, this only offers sparse supervisory signals to the SDF at locations occupied by Gaussians, which is insufficient for learning a continuous SDF. Then, to address this limitation, we incorporate volumetric rendering and align the rendered geometric attributes (depth, normal) with that derived from 3DGS. In sum, these two designs allow SDF and 3DGS to be aligned, jointly optimized, and mutually boosted. Our extensive experimental results demonstrate that our 3DGSR enables high-quality 3D surface reconstruction while preserving the efficiency and rendering quality of 3DGS. Besides, our method competes favorably with leading surface reconstruction techniques while offering a more efficient learning process and much better rendering qualities.
在本文中,我们提出了一种采用三维高斯拼接(3DGS)的隐式曲面重建方法,即3DGSR,它在继承3DGS的高效率和渲染质量的同时,还能实现具有复杂细节的精确三维重建。其关键之处在于将隐式签名距离场(SDF)纳入三维高斯曲面建模,并实现 SDF 和三维高斯的对齐和联合优化。为此,我们设计了耦合策略,将 SDF 与三维高斯进行对齐和关联,从而实现统一优化,并对三维高斯执行曲面约束。通过对齐,优化三维高斯可为 SDF 学习提供监督信号,从而实现复杂细节的重建。然而,这只能在高斯占据的位置为 SDF 提供稀疏的监督信号,不足以学习连续的 SDF。然后,为了解决这一局限性,我们采用了体积渲染技术,并将渲染的几何属性(深度、法线)与 3DGS 得出的属性保持一致。总之,这两种设计使 SDF 和 3DGS 能够相互配合、共同优化和相互促进。大量的实验结果表明,我们的 3DGSR 可以实现高质量的三维表面重建,同时保持 3DGS 的效率和渲染质量。此外,我们的方法在提供更高效的学习过程和更好的渲染质量的同时,还能与领先的曲面重建技术相媲美。
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引用次数: 0
Volume Scattering Probability Guiding 体积散射概率引导
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687982
Kehan Xu, Sebastian Herholz, Marco Manzi, Marios Papas, Markus Gross
Simulating the light transport of volumetric effects poses significant challenges and costs, especially in the presence of heterogeneous volumes. Generating stochastic paths for volume rendering involves multiple decisions, and previous works mainly focused on directional and distance sampling, where the volume scattering probability (VSP), i.e., the probability of scattering inside a volume, is indirectly determined as a byproduct of distance sampling. We demonstrate that direct control over the VSP can significantly improve efficiency and present an unbiased volume rendering algorithm based on an existing resampling framework for precise control over the VSP. Compared to previous state-of-the-art, which can only increase the VSP without guaranteeing to reach the desired value, our method also supports decreasing the VSP. We further present a data-driven guiding framework to efficiently learn and query an approximation of the optimal VSP everywhere in the scene without the need for user control. Our approach can easily be combined with existing path-guiding methods for directional sampling at minimal overhead and shows significant improvements over the state-of-the-art in various complex volumetric lighting scenarios.
模拟体积效应的光传输带来了巨大的挑战和成本,尤其是在存在异质体积的情况下。为体积渲染生成随机路径涉及多个决策,以前的工作主要集中在方向和距离采样上,其中体积散射概率(VSP),即体积内部的散射概率,是作为距离采样的副产品间接确定的。我们证明,直接控制 VSP 可以显著提高效率,并基于现有的重采样框架提出了一种无偏的体积渲染算法,以精确控制 VSP。与以往只能增加 VSP 而不能保证达到理想值的先进方法相比,我们的方法还支持减小 VSP。我们进一步提出了一个数据驱动的指导框架,可在场景中的任何地方有效地学习和查询最佳 VSP 的近似值,而无需用户控制。我们的方法可以很容易地与现有的路径引导方法相结合,以最小的开销进行定向采样,并在各种复杂的体积照明场景中显示出比最先进方法的显著改进。
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引用次数: 0
Learned Multi-aperture Color-coded Optics for Snapshot Hyperspectral Imaging 用于快照高光谱成像的学习型多光圈彩色编码光学器件
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687976
Zheng Shi, Xiong Dun, Haoyu Wei, Siyu Dong, Zhanshan Wang, Xinbin Cheng, Felix Heide, Yifan Peng
Learned optics, which incorporate lightweight diffractive optics, coded-aperture modulation, and specialized image-processing neural networks, have recently garnered attention in the field of snapshot hyperspectral imaging (HSI). While conventional methods typically rely on a single lens element paired with an off-the-shelf color sensor, these setups, despite their widespread availability, present inherent limitations. First, the Bayer sensor's spectral response curves are not optimized for HSI applications, limiting spectral fidelity of the reconstruction. Second, single lens designs rely on a single diffractive optical element (DOE) to simultaneously encode spectral information and maintain spatial resolution across all wavelengths, which constrains spectral encoding capabilities. This work investigates a multi-channel lens array combined with aperture-wise color filters, all co-optimized alongside an image reconstruction network. This configuration enables independent spatial encoding and spectral response for each channel, improving optical encoding across both spatial and spectral dimensions. Specifically, we validate that the method achieves over a 5dB improvement in PSNR for spectral reconstruction compared to existing single-diffractive lens and coded-aperture techniques. Experimental validation further confirmed that the method is capable of recovering up to 31 spectral bands within the 429--700 nm range in diverse indoor and outdoor environments.
学习型光学器件结合了轻型衍射光学器件、编码光圈调制和专门的图像处理神经网络,最近在快照高光谱成像(HSI)领域备受关注。虽然传统方法通常依赖于单透镜元件与现成的彩色传感器配对,但这些设置尽管普遍可用,却存在固有的局限性。首先,拜耳传感器的光谱响应曲线没有针对 HSI 应用进行优化,从而限制了重建的光谱保真度。其次,单透镜设计依赖于单个衍射光学元件(DOE)来同时编码光谱信息并保持所有波长的空间分辨率,这限制了光谱编码能力。这项工作研究的是一种多通道透镜阵列,该阵列与孔径彩色滤光片相结合,所有这些都与图像重建网络共同优化。这种配置实现了每个通道独立的空间编码和光谱响应,改进了空间和光谱两个维度的光学编码。具体来说,我们验证了与现有的单衍射透镜和编码光圈技术相比,该方法在光谱重建方面的 PSNR 提高了 5 分贝以上。实验验证进一步证实,该方法能够在不同的室内和室外环境中恢复 429-700 nm 范围内的多达 31 个光谱带。
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引用次数: 0
Bijective Volumetric Mapping via Star Decomposition 通过星形分解进行双射体积映射
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687950
Steffen Hinderink, Hendrik Brückler, Marcel Campen
A method for the construction of bijective volumetric maps between 3D shapes is presented. Arbitrary shapes of ball-topology are supported, overcoming restrictions of previous methods to convex or star-shaped targets. In essence, the mapping problem is decomposed into a set of simpler mapping problems, each of which can be solved with previous methods for discrete star-shaped mapping problems. Addressing the key challenges in this endeavor, algorithms are described to reliably construct structurally compatible partitions of two shapes with constraints regarding star-shapedness and to compute a parsimonious common refinement of two triangulations.
本文介绍了一种在三维形状之间构建双射体积映射的方法。该方法支持球拓扑结构的任意形状,克服了以往方法对凸形或星形目标的限制。从本质上讲,映射问题被分解成一系列更简单的映射问题,每个问题都可以用以前的离散星形映射问题方法来解决。针对这一领域的关键挑战,本文介绍了如何可靠地构建两个形状的结构兼容分区,并对星形性进行约束,以及如何计算两个三角形的合理共同细化。
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引用次数: 0
Neural Differential Appearance Equations 神经差分方程
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687900
Chen Liu, Tobias Ritschel
We propose a method to reproduce dynamic appearance textures with space-stationary but time-varying visual statistics. While most previous work decomposes dynamic textures into static appearance and motion, we focus on dynamic appearance that results not from motion but variations of fundamental properties, such as rusting, decaying, melting, and weathering. To this end, we adopt the neural ordinary differential equation (ODE) to learn the underlying dynamics of appearance from a target exemplar. We simulate the ODE in two phases. At the "warm-up" phase, the ODE diffuses a random noise to an initial state. We then constrain the further evolution of this ODE to replicate the evolution of visual feature statistics in the exemplar during the generation phase. The particular innovation of this work is the neural ODE achieving both denoising and evolution for dynamics synthesis, with a proposed temporal training scheme. We study both relightable (BRDF) and non-relightable (RGB) appearance models. For both we introduce new pilot datasets, allowing, for the first time, to study such phenomena: For RGB we provide 22 dynamic textures acquired from free online sources; For BRDFs, we further acquire a dataset of 21 flash-lit videos of time-varying materials, enabled by a simple-to-construct setup. Our experiments show that our method consistently yields realistic and coherent results, whereas prior works falter under pronounced temporal appearance variations. A user study confirms our approach is preferred to previous work for such exemplars.
我们提出了一种重现动态外观纹理的方法,这种纹理具有空间静止但时间变化的视觉统计数据。以往的研究大多将动态纹理分解为静态外观和运动,而我们则专注于动态外观,它不是由运动而是由基本属性的变化(如生锈、腐烂、融化和风化)产生的。为此,我们采用神经常微分方程 (ODE) 从目标示例中学习外观的基本动态。我们分两个阶段模拟 ODE。在 "热身 "阶段,ODE 将随机噪音扩散到初始状态。然后,我们对该 ODE 的进一步演变进行约束,以复制生成阶段示例中视觉特征统计数据的演变。这项工作的创新之处在于,神经 ODE 可同时实现动态合成的去噪和演化,并采用了建议的时间训练方案。我们研究了可重照(BRDF)和不可重照(RGB)外观模型。对于这两种模型,我们都引入了新的试验数据集,首次对此类现象进行了研究:对于 RGB,我们提供了 22 种从免费在线资源中获取的动态纹理;对于 BRDF,我们进一步获取了 21 个闪光灯下的时变材料视频数据集,并通过简单的构建设置实现。我们的实验表明,我们的方法能持续产生逼真、连贯的结果,而之前的方法在明显的时间外观变化下会出现问题。一项用户研究证实,对于此类示例,我们的方法优于之前的工作。
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引用次数: 0
Differential Walk on Spheres 球面微分行走
IF 6.2 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-11-19 DOI: 10.1145/3687913
Bailey Miller, Rohan Sawhney, Keenan Crane, Ioannis Gkioulekas
We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary points, without performing a global solve or constructing a volumetric grid or mesh. The method is hence well suited to inverse problems with complex geometry, such as PDE-constrained shape optimization. Like other walk on spheres (WoS) algorithms, our method is trivial to parallelize, and is agnostic to boundary representation (meshes, splines, implicit surfaces, etc. ), supporting large topological changes. We focus in particular on screened Poisson equations, which model diverse problems from scientific and geometric computing. As in differentiable rendering, we jointly estimate derivatives with respect to all parameters---hence, cost does not grow significantly with parameter count. In practice, even noisy derivative estimates exhibit fast, stable convergence for stochastic gradient-based optimization, as we show through examples from thermal design, shape from diffusion, and computer graphics.
我们介绍一种蒙特卡罗方法,用于计算偏微分方程(PDE)解相对于问题参数(如域几何或边界条件)的导数。导数可以在任意点进行评估,而无需执行全局求解或构建体积网格或网格。因此,该方法非常适合复杂几何形状的逆问题,如 PDE 受限形状优化。与其他球面行走(WoS)算法一样,我们的方法易于并行化,并且与边界表示(网格、样条、隐式曲面等)无关,支持大规模拓扑变化。我们尤其专注于筛选泊松方程,它可以模拟科学和几何计算中的各种问题。与可微分渲染一样,我们联合估计所有参数的导数--因此,成本不会随着参数数量的增加而显著增加。在实践中,对于基于随机梯度的优化,即使是有噪声的导数估计也能表现出快速、稳定的收敛性,我们将通过热设计、扩散形状和计算机图形学中的实例来说明这一点。
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引用次数: 0
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ACM Transactions on Graphics
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