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崎岖边缘生存电子书

Trend | Insight |解决方案

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从云端到边缘的数据洞察

由于企业云提供商的需求需要性能驱动的处理、存储和连接,专门构建的硬件服务器将改变传统的向外扩展架构访问、传输和存储大量数据的方式。新的硬件架构需要最新的技术,以满足现代需求的数据中心和边缘。传统机架服务器的设计正在迅速改变,以实现更快、更高效的实时数据处理,这正在被用于新一波的机器智能。

DPU加速服务器在数据中心架构中汇聚了最新技术,为数据中心网络乃至整个网络提供了令人难以置信的计算能力、效率和可伸缩性的新性能基准。


数据处理。加速性能问题

性能提升

高性能计算(HPC)

工业4.0和智能产品图标集智能工业革命、自动化、机器人助手、云和创新于一体。股票插图(转换)

机器学习

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数据分析Web 2.0

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云管理

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大规模数据存储


FlacheStreams DPU加速服务器

FlacheStreams DPU加速rackmount服务器旨在提供高性能的结果,支持cpu、gpu和DPU的最新技术的新架构。这个专门构建的服务器解决了当今公共、私有和混合云模型基础设施中最复杂的数据中心工作负载,这些基础设施需要惊人的硬件加速平衡。

物理:

  • 2 u Rackmountable底盘
  • 12独特的DPU卡热插拔托盘
  • 单AMD EPYC或双AMD EPYC或英特尔可扩展处理器

连通性:

  • 支持36x 25Gb/s网络加速端口
  • 2x 96lane PCIe交换板用于点对点通信
  • 2个GbE RJ45 LAN口,1个IPMI管理RJ45口

可伸缩性:

  • 扩展到18 DPUs或14 DPUs + 2全高双宽350W GPGPUs
  • 最多支持300TB+的NVMe SSD Storage
  • CPU核数:8核~ 128核
  • 多达18x 8 Armv8 A72内核(64位)DPU加速器

硬件加速:

  • 压缩和减压加速
  • NVMe-oF™加速度
  • SR-IOV
  • TCP / IP传输分流

产品系列:

FlacheSAN2N12C-DM 2U 12双AMD EPYC DPU服务器

  • 2台AMD EPYC米兰处理器,128核
  • 4 tb RDIMM 3200 mhz
  • 16x PCIe扩展插槽

FlacheSAN2N12C-UM 2U 12台AMD EPYC单DPU服务器

  • 1个AMD EPYC米兰64核处理器
  • 4 tb RDIMM 3200 mhz
  • 16x PCIe扩展插槽

FlacheSAN2N12C-D5 2U 12双Intel可扩展处理器DPU服务器

  • 2x Intel可扩展cpu
  • 16 dimm RDIMM 2933 mhz
  • 16x PCIe扩展插槽

如何配置DPU加速服务器?

网络应用程序:

支持SmartNIC/FPGA/加速器,从CPU卸载工作负载,增加网络吞吐量和减少延迟。

存储应用程序:

12x PCIe 3.0 x8 Add-on-Card支持NVMe SSD设备,最多可存储300TB的数据。

计算应用程序:

128x AMD EPYC核(2x 64核),2倍双宽gpu,最多18x dpu计算ssd,使客户能够运行最需要计算的应用程序。

HPC应用程序:

通过两个96 lane交换板,扩展到12x PCIe x8扩展卡和4x PCIe x16扩展卡,该系统允许smartnic和ssd通过点对点DMA相互通信。通过这样做,它消除了不必要的数据移动,以减少延迟和增加计算资源。


数据中心的硬件加速

今天的数据中心仍然主要采用基于x86 cpu的计算。然而,随着摩尔定律被证明是半导体创新的一个更大的挑战,数据中心基础设施需要新的技术和方法从现有的硬件提取性能。如今的数据中心解决方案依赖于各种性能加速备用yabovip技术,以满足ai驱动应用程序中更快的处理、存储、网络和安全性的计算需求。

  • X86服务器在2020年第四季度的收入为209.3亿美元——占所有服务器收入的92.8% (IDC)
  • 现代数据中心基础设施使用cpu、gpu、dpu和fpga中的硬件技术来实现性能驱动的扩展架构

首先,gpu被用来加速AI工作负载。现在,计算存储设备和数据处理单元正在进入数据中心,以便更好地跨硬件架构利用资源。CPU、GPU和现在的dpu加速了工作负载,减轻了传统处理器的开销,为其他关键应用释放了宝贵的CPU资源。今天,流行的数据中心性能加速器包括多核处理器、gpu(图形处理单元)、计算存储设备和dpu(数据处理单元),它们共同提高性能并消除不必要的瓶颈。

通用引擎:X86多核处理器

多核处理器在数据中心中非常流行,因为数据中心服务器CPU的核心越多,它在每个周期中可以执行的程序指令就越多。因此,大多数数据中心服务器都配备了多核CPU,为企业应用程序提供更多的CPU处理能力,从而提高服务器的性能。

深度学习与机器智能:图形处理器(gpu)

gpu作为性能加速器在数据中心很受欢迎,因为gpu非常适合执行AI工作负载,比如机器学习和深度学习训练。这是因为它们配备了数千个核,使它们能够并行执行许多计算,显著减少了完成复杂工作负载所需的时间。

计算NAND闪存:炽热的快速NVMe

高性能服务器可以配置计算存储设备,在不依赖主机CPU资源的情况下,将数据处理从服务器的CPU转移到存储设备,从而加速存储功能。这可以通过存储层内置的处理和内存技术实现,允许服务器以超低延迟实时处理数据。

A SmarterNIC: dpu(数据处理单元)

数据处理单元正在进入数据中心服务器,因为它们能够将存储、网络和安全功能从主机服务器的CPU转移到DPU,从而释放CPU周期,用于运行企业应用程序和操作系统。


推动需要新的解决方案的趋势备用yabovip

存在的问题:为管理数据中心中的其他关键数据处理和存储而浪费CPU资源的瓶颈。

备用yabovip解决方案:智能芯片、fpga、gpu以及现在的dpu正在形成新的硬件架构,以实现难以置信的性能和超低延迟。

5G和PCIe Gen 4产生的数据快速增长,使得边缘计算成为处理大量数据的必要条件。特定的附加卡,即SmartNIC fpga和dpu,通过专注于处理网络任务来帮助减轻工作负载,从而使CPU对更通用的工作负载有更好的利用。收听Premio的崎岖边缘生存播客,了解DPUs的好处。


这些(dpu)将代表计算发展的三大支柱之一。CPU用于通用计算,GPU用于加速计算,DPU用于在数据中心周围移动数据,进行数据处理。

——英伟达首席执行官黄延森


什么是基于dpu(数据处理单元)的智能技术?

dpu是数据处理单元的简称,是一种新型可编程处理器,通常与高性能网络接口结合在一起。通过将主机服务器CPU处理网络数据的方式直接交给DPU,这就释放了宝贵的CPU资源,用于管理数据中心中的其他关键任务应用程序。例如,dpu经过优化,可以直接在网卡上加速网络、存储和安全管理功能。

dpu可以从主机CPU释放30%到50%的利用率

dpu作为智能芯片,对数据管理和工作负载进行高效的把关

在深度学习应用中,dpu可以绕过CPU并解码gpu加速所需的数据。

PCIe Gen 4支持

dpu支持PCIe 4.0 PCIe (Peripheral Component Interconnect Express),这是一个连接高端加速器到服务器的标准接口。
了解更多…

  • dpu由几个主要部件组成,包括:
  • 高性能,软件可编程多核ARM处理紧密耦合到其他SoC组件

  • 一个高性能的网络接口,允许DPU通过网络解析、处理和高效地移动数据

  • 一套丰富的灵活、可编程的加速引擎,将网络、存储、安全和AI工作负载从服务器的CPU卸载到DPU

DPU特性

数据安全

从100GbE到200gbe的高速网络连接

无CPU的高速分组处理

强大的多核处理器

性能加速器

数据和本地存储管理

为什么dpu对数据中心和边缘计算基础设施很重要?

dpu对于数据中心和边缘网络非常重要,因为它们可以释放宝贵的服务器CPU资源用于其他通用处理。DPU smartNics还通过管理数据包来缓解特定的网络瓶颈,而无需与服务器主机cpu进行任何交互。许多新的边缘计算应用还需要对输入数据进行强大的硬件加速,以实现实时机器学习和深度学习算法的训练。

网络虚拟化

与x86 cpu相比,更高效地支持网络虚拟化。从CPU卸下网络负荷,如SRIOV、vFirewall、OVS、覆盖网络流量封装等。

包来回踱步

通过限制出站数据包,确保更稳定的网络流量,同时仍然保持相同的吞吐量和质量,使内容的传递更顺畅

提升5G流量路由性能

支持vRAN,前移IO,向前纠错,精确时间戳,eCPRI开窗,实时传输硬件加速

可信SmartNIC、FPGA、DPU合作伙伴

Nvidia提供的Nvidia Mellanox BlueField 2数据处理单元(DPU)将行业领先的ConnectX网卡、强大的多核处理器和许多其他性能加速器结合到一个可重新编程的单个封装中,允许组织重新编程设备以运行最新的算法。
了解更多…

Xilinx提供基于fpga的alveso系列SmartNIC卡,实现硬件加速并避免不必要的数据移动。Xilinx alvio能够加速计算密集型应用程序,包括机器学习推理、数据分析、视频转码和许多其他工作负载
了解更多…

为什么选择DPU加速服务器

在主要企业市场的服务器硬件设计、工程和制造(ODM)方面具有专业知识

  • 专为高性能计算亚博在体育官网入口、横向扩展数据存储、机器和深度学习、公有和私有云管理等工作负载而设计的服务器产品
  • 在服务器和存储架构的性能,存储和高速连接方面有30多年的丰富设计经验
  • 美国的全球交钥匙制造和支持基础设施,加速服务器和存储解决方案的可扩展大规模部署备用yabovip
  • 深刻理解物联网和数据中心技术在计算,存储和连接设计的高性能工作负载
  • 北美市场中服务器和存储解决方案的法规测试和遵从性选项备用yabovip

常见问题(FAQs)

就像GPU被用来将数学密集型任务从CPU转移到GPU, DPU(数据处理单元)被用来将网络和存储功能从CPU转移到DPU,从处理普通的网络和存储功能中释放出宝贵的CPU资源。

可以把CPU看作是计算机的大脑,专门用于处理通用计算。gpu在数据中心中有自己的位置,因为它们能够加速某些工作负载,例如,由于它们提供的并行计算,人工智能的深度学习。dpu现在正在进入数据中心,因为它们能够在不使用主机CPU资源的情况下处理和破译关键任务数据,节省时间并减少延迟。

并非所有服务器都配置了dpu。事实上,DPU加速服务器现在正被引入数据中心,因为对由服务器执行的加速网络和存储功能的需求更大。根据工作负载和应用程序的不同,dpu可以为现代数据中心基础设施提供巨大的好处。

配备dpu的服务器的主要好处是,它们能够将数据解析、数据处理和数据从主机服务器的CPU转移到GPU,从而释放出CPU用于各种企业应用程序。

dpu配备了强大的多核处理器和加速引擎,对于人工智能、机器学习、安全、电信、存储和许多其他现代数据中心应用程序都非常好。