Windows Hyper-V单虚拟机实现千万IOPS:存储技术与挑战

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本文档标题为《在Windows Hyper-V上通过单个虚拟机实现1000万IOPS》(Achieving 10-Million IOPS from a single VM on Windows Hyper-V),发表于2018年的存储开发者大会,由微软公司提供。该研究探讨了在云计算时代背景下,如何通过技术创新和优化来满足客户对于高I/O性能的需求。 首先,作者提到了驱动需求的原因,包括强劲的客户需求、云计算市场的增长与竞争压力、数据中心对更高密度和容量的需求、以及对吞吐量和延迟更低的高性能要求。随着硬件技术的迅速发展,特别是更快的存储解决方案和高级处理器的出现,云服务提供商开始提供针对高I/O工作负载优化的存储产品,能够处理每秒数百万次I/O操作(IOPS)的工作场景。 其次,文章着重讨论了存储技术的进步如何推动IOPS的提升。从传统的SAS/SATA接口升级到更高速度的PCIe NVMe标准,例如第一代PCIe NVMe可以达到约100千IOPS,而到第三代,性能已经显著提升至约1百万IOPS,这主要得益于数据中心固态硬盘(SSD)使用的高速接口和协议带宽的增强。4KB大小的数据读写测试表明,新一代接口如NVMe显著提高了实际的I/O操作能力。 Windows Hyper-V作为微软的虚拟化平台,其在支持这种高IOPS需求方面发挥着关键作用。通过优化虚拟机配置和利用最新的硬件技术,研究人员展示了如何在一个单一的虚拟机中实现1000万IOPS的性能目标,这对于处理大数据分析、实时交易系统等高负载应用具有重要意义。 这篇论文揭示了在云计算环境中,通过技术创新和合理配置,如何突破传统限制,实现高性能的存储解决方案,从而支持业务的快速发展和竞争。它不仅提供了理论依据,也对IT专业人士在设计和优化云存储架构时提供了实用的参考。

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.请详细介绍这段话中的技术点和实现方式

2023-05-29 上传

帮我解释一下这段话:The connection growth algorithm greedily activates useful, but currently ‘dormant,’ connections. We incorporate it in the following learning policy: Policy 1: Add a connection w iff it can quickly reduce the value of loss function L. The DNN seed contains only a small fraction of active connections to propagate gradients. To locate the ‘dormant’ connections that can reduce L effectively, we evaluate ∂L/∂w for all the ‘dormant’ connections w (computed either using the whole training set or a large batch). Policy 1 activates ‘dormant’ connections iff they are the most efficient at reducing L. This can also assist with avoiding local minima and achieving higher accuracy [28]. To illustrate this policy, we plot the connections grown from the input to the first layer of LeNet-300-100 [7] (for the MNIST dataset) in Fig. 3. The image center has a much higher grown density than the margins, consistent with the fact that the MNIST digits are centered. From a neuroscience perspective, our connection growth algorithm coincides with the Hebbian theory: “Neurons that fire together wire together [29]." We define the stimulation magnitude of the mth presynaptic neuron in the (l + 1)th layer and the n th postsynaptic neuron in the l th layer as ∂L/∂ul+1 m and x l n , respectively. The connections activated based on Hebbian theory would have a strong correlation between presynaptic and postsynaptic cells, thus a large value of (∂L/∂ul+1 m )x l n . This is also the magnitude of the gradient of L with respect to w (w is the weight that connects u l+1 m and x l n ): |∂L/∂w| = (∂L/∂ul+1 m )x l n (1) Thus, this is mathematically equivalent to Policy 1.

2023-05-17 上传