DOCSLIB.ORG

Implementation of GPU Virtualization Using PCI Pass-Through Mechanism

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Implementation of GPU Virtualization Using PCI Pass-Through Mechanism Implementation of GPU virtualization using PCI pass-through mechanism Chao-Tung Yang, Jung-Chun Liu, Hsien-Yi Wang & Ching-Hsien Hsu The Journal of Supercomputing An International Journal of High- Performance Computer Design, Analysis, and Use ISSN 0920-8542 J Supercomput DOI 10.1007/s11227-013-1034-4 1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be self- archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy J Supercomput DOI 10.1007/s11227-013-1034-4 Implementation of GPU virtualization using PCI pass-through mechanism Chao-Tung Yang · Jung-Chun Liu · Hsien-Yi Wang · Ching-Hsien Hsu © Springer Science+Business Media New York 2013 Abstract As a general purpose scalable parallel programming model for coding highly parallel applications, CUDA from NVIDIA provides several key abstractions: a hierarchy of thread blocks, shared memory, and barrier synchronization. It has proven to be rather effective at programming multithreaded many-core GPUs that scale transparently to hundreds of cores; as a result, scientists all over the industry and academia are using CUDA to dramatically expedite on production and codes. GPU-based clusters are likely to play an essential role in future cloud computing centers, because some computation-intensive applications may require GPUs as well as CPUs. In this paper, we adopted the PCI pass-through technology and set up virtual machines in a virtual environment; thus, we were able to use the NVIDIA graphics card and the CUDA high performance computing as well. In this way, the virtual machine has not only the virtual CPU but also the real GPU for computing. The performance of the virtual machine is predicted to increase dramatically. This paper measured the difference of performance between physical and virtual machines using CUDA, and investigated how virtual machines would verify CPU numbers under the influence of CUDA performance. At length, we compared CUDA performance of two open source virtualization hypervisor environments, with or without using PCI pass- through. Through experimental results, we will be able to tell which environment is most efficient in a virtual environment with CUDA. C.-T. Yang (B) · J.-C. Liu · H.-Y. Wang Department of Computer Science, Tunghai University, Taichung 40704, Taiwan e-mail: [email protected] J.-C. Liu e-mail: [email protected] C.-H. Hsu Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu, Taiwan e-mail: [email protected] Author's personal copy C.-T. Yang et al. Keywords CUDA · GPU virtualization · Cloud computing · PCI pass-through 1 Introduction 1.1 Motivations Graphics processing units (GPUs) are true many-core processors with hundreds of processing elements. The GPU is a specialized microprocessor that offloads and ac- celerates graphics rendering from the central microprocessor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structures make them more effective than general-purpose CPUs over a range of complex algorithms. Currently, a CPU has only 8 cores in a single chip, but a GPU has grown to 448 cores. From the number of cores, the GPU is fitting to execute programs suitable for massive parallel processing. Although the clock frequency of cores on the GPU is lower than that of the CPU, its powerful parallel processing ability conquers the problem of lower frequency. So far, the GPU has been used on supercomputers: on the TOP500 site in November 2010 [1], three of the first five supercomputers were built with NVIDIA GPU [2], and Titan, the world’s fastest supercomputer according to the TOP500 list released in November 2012 was also powered by NVIDIA GPU [1]. In recent years, the virtualization environment on Cloud [3] has become more popular than before. The balance between performance and cost is the most important factor. In order to live up to the potential of the server resource, the virtualization technology is the main solution for running many more virtual machines on a server and yet its resources can be used a lot more effectively. However, virtual machines have their own performance limitations so that users are restrained from using a lot of computing on them. Building a virtual environment in a Cloud computing system for users has be- come an important trend in the last few years. Proper use of hardware resources and computing power of each virtual machine is the aim of the Infrastructure as a Ser- vice (IaaS), which is one of the feature architectures of Cloud computing. Neverthe- less, the virtual machine has limitation when the virtual environment system does not have support of Compute Unified Device Architecture (CUDA), such as the physical General-Purpose computing on Graphics Processing Units (GPGPU) [4, 41–43]used by virtual machines in real machines to assist computing. Since the GPU is a real many-core processor, the computing power of virtual machines will be increased. 1.2 Goal and contribution In this paper, we explored various hypervisor environments for virtualization, differ- ent virtualization types on the cloud system, and several types of hardware virtual- ization [40]. We focused on the GPU virtualization, implemented a system with vir- tualization environment, and used the PCI pass-through [5] technology that enables the virtual machines on the system to use the GPU accelerator to increase the com- puting power. We conducted experiments to compare performance between virtual Author's personal copy Implementation of GPU virtualization using PCI pass-through mechanism machines with GPU virtualization and PCI pass-through and the native machine with GPU. Then we showed the GPU performance between the virtual machine and native machine, and also compared system time of virtual machines with that of the native machine. At last, we analyzed two other GPU virtualization technologies; the exper- imental results displayed the advantage of performance by using PCI pass-through over the other GPU virtualization technologies. 1.3 Organization of paper The rest of this work is organized as follows. Section 2 provides the background re- views of the Cloud computing, virtualization technology, and CUDA [6]. Section 3 describes the system implementation, architecture, and specifications of Tesla C1060 and Tesla C2050, and end-user’s interface. Section 4 presents the experimental envi- ronment, the used methods, results of GPU virtualization, and the improved perfor- mance of the proposed approach. Finally, conclusions are made in Sect. 5. 2 Background review 2.1 Cloud computing Cloud computing [3] is a computing approach based on the Internet, in which users can remotely use software services and data storage in remote servers. It is a new service architecture that brings a new choice of software and data storage services to users. To use the “Cloud,” users no longer need to find out details of the infrastructure in advance, not necessary to possess the professional knowledge, and are without direct control of the real machines that provide services. National Institute of Standards and Technology, aka NIST, defined following five basic features for Cloud computing in April 2009 [7]: • On-demand self-service • Broad network access • Resource pooling • Rapid elasticity • Measured service Cloud computing can be considered to include three levels of service: Infrastruc- ture as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) [3]. The architecture of cloud computing is shown in Fig. 1. • Infrastructure as a Service (IaaS): Users can follow the required level of computers and network equipment and other resources, to set the service provider subscription service, and may require changes to settings. The cost is calculated according to the use of the CPU, memory, disk space, and the network load. • Platform as a Service (PaaS): development of service vendors who rent computers with the necessary hardware and software development environment; developer fees are calculated in accordance with the amount of traffic of the use of resources. • Software as a Service (SaaS): the software stored in the data center to provide users network access services. The type of charge is on a period or pay-per-order. Author's personal copy C.-T. Yang et al. Fig. 1 Architecture of cloud computing Fig. 2 Diagram of virtualization 2.2 Virtualization Virtualization technology [8] is a technology that creates a virtual version of some- thing, such as a hardware platform, operating system, a storage device, or network resources. The goal of virtualization is to centralize administrative tasks, while im- prove scalability and overall hardware-resource utilization. By using virtualization, several operating systems can be run in parallel on a single powerful server without glitches. The diagram of virtualization is shown in Fig. 2. The case of a general operating system is shown in Fig. 3. To protect instructions, there are four levels of permissions. The user’s applications are the implementation of the Ring 3 in the CPU part, and the implementation of the operating system is in Author's personal copy Implementation of GPU virtualization using PCI pass-through mechanism Fig. 3 The general operating system Fig. 4 The virtualization operating system Ring 0 to control the CPU and hardware. The hardware directly executes requests of the operating system and instructions of user applications.
Load more

Recommended publications
  • GPU Virtualization on Vmware's Hosted I/O Architecture
    GPU Virtualization on VMware’s Hosted I/O Architecture Micah Dowty, Jeremy Sugerman VMware, Inc. 3401 Hillview Ave, Palo Alto, CA 94304 [email protected], [email protected] Abstract more computational performance than CPUs. At the Modern graphics co-processors (GPUs) can produce same time, GPU acceleration has extended beyond en- high fidelity images several orders of magnitude faster tertainment (e.g., games and video) into the basic win- than general purpose CPUs, and this performance expec- dowing systems of recent operating systems and is start- tation is rapidly becoming ubiquitous in personal com- ing to be applied to non-graphical high-performance ap- puters. Despite this, GPU virtualization is a nascent field plications including protein folding, financial modeling, of research. This paper introduces a taxonomy of strate- and medical image processing. The rise in applications gies for GPU virtualization and describes in detail the that exploit, or even assume, GPU acceleration makes specific GPU virtualization architecture developed for it increasingly important to expose the physical graph- VMware’s hosted products (VMware Workstation and ics hardware in virtualized environments. Additionally, VMware Fusion). virtual desktop infrastructure (VDI) initiatives have led We analyze the performance of our GPU virtualiza- many enterprises to try to simplify their desktop man- tion with a combination of applications and microbench- agement by delivering VMs to their users. Graphics vir- marks. We also compare against software rendering, the tualization is extremely important to a user whose pri- GPU virtualization in Parallels Desktop 3.0, and the na- mary desktop runs inside a VM. tive GPU. We find that taking advantage of hardware GPUs pose a unique challenge in the field of virtu- acceleration significantly closes the gap between pure alization.
    [Show full text]
  • Gscale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics
    gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue, Shanghai Jiao Tong University and Intel Corporation; Kun Tian, Intel Corporation; Yaozu Dong, Shanghai Jiao Tong University and Intel Corporation; Jiacheng Ma, Jiajun Wang, and Zhengwei Qi, Shanghai Jiao Tong University; Bingsheng He, National University of Singapore; Haibing Guan, Shanghai Jiao Tong University https://www.usenix.org/conference/atc16/technical-sessions/presentation/xue This paper is included in the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16). June 22–24, 2016 • Denver, CO, USA 978-1-931971-30-0 Open access to the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16) is sponsored by USENIX. gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue1,2, Kun Tian2, Yaozu Dong1,2, Jiacheng Ma1, Jiajun Wang1, Zhengwei Qi1, Bingsheng He3, Haibing Guan1 {xuemochi, mjc0608, jiajunwang, qizhenwei, hbguan}@sjtu.edu.cn {kevin.tian, eddie.dong}@intel.com [email protected] 1Shanghai Jiao Tong University, 2Intel Corporation, 3National University of Singapore Abstract As one of the key enabling technologies of GPU cloud, GPU virtualization is intended to provide flexible and With increasing GPU-intensive workloads deployed on scalable GPU resources for multiple instances with high cloud, the cloud service providers are seeking for practi- performance. To achieve such a challenging goal, sev- cal and efficient GPU virtualization solutions. However, eral GPU virtualization solutions were introduced, i.e., the cutting-edge GPU virtualization techniques such as GPUvm [28] and gVirt [30]. gVirt, also known as GVT- gVirt still suffer from the restriction of scalability, which g, is a full virtualization solution with mediated pass- constrains the number of guest virtual GPU instances.
    [Show full text]
  • Crane: Fast and Migratable GPU Passthrough for Opencl Applications
    Crane: Fast and Migratable GPU Passthrough for OpenCL Applications James Gleeson, Daniel Kats, Charlie Mei, Eyal de Lara University of Toronto Toronto, Canada {jgleeson,dbkats,cmei,delara}@cs.toronto.edu ABSTRACT (IaaS) providers now have virtualization offerings with ded- General purpose GPU (GPGPU) computing in virtualized icated GPUs that customers can leverage in their guest vir- environments leverages PCI passthrough to achieve GPU tual machines (VMs). For example, Amazon Web Services performance comparable to bare-metal execution. However, allows VMs to take advantage of NVIDIA’s high-end GPUs GPU passthrough prevents service administrators from per- in EC2 [6]. Microsoft and Google recently introduced simi- forming virtual machine migration between physical hosts. lar offerings on Azure and GCP platforms respectively [34, Crane is a new technique for virtualizing OpenCL-based 21]. GPGPU computing that achieves within 5:25% of pass- Virtualized GPGPU computing leverages modern through GPU performance while supporting VM migra- IOMMU features to give the VM direct use of the GPU tion. Crane interposes a virtualization-aware OpenCL li- through DMA and interrupt remapping – a technique which brary that makes it possible to reclaim and subsequently is usually called PCI passthrough [17, 50]. By running reassign physical GPUs to a VM without terminating the the GPU driver in the context of the guest operating guest or its applications. Crane also enables continued GPU system, PCI passthrough bypasses the hypervisor and operation while the VM is undergoing live migration by achieves performance that is comparable to bare-metal OS transparently switching between GPU passthrough opera- execution [47]. tion and API remoting.
    [Show full text]
  • A Full GPU Virtualization Solution with Mediated Pass-Through
    A Full GPU Virtualization Solution with Mediated Pass-Through Kun Tian, Yaozu Dong, David Cowperthwaite Intel Corporation Abstract shows the spectrum of GPU virtualization solutions Graphics Processing Unit (GPU) virtualization is an (with hardware acceleration increasing from left to enabling technology in emerging virtualization right). Device emulation [7] has great complexity and scenarios. Unfortunately, existing GPU virtualization extremely low performance, so it does not meet today’s approaches are still suboptimal in performance and full needs. API forwarding [3][9][22][31] employs a feature support. frontend driver, to forward the high level API calls inside a VM, to the host for acceleration. However, API This paper introduces gVirt, a product level GPU forwarding faces the challenge of supporting full virtualization implementation with: 1) full GPU features, due to the complexity of intrusive virtualization running native graphics driver in guest, modification in the guest graphics software stack, and and 2) mediated pass-through that achieves both good incompatibility between the guest and host graphics performance and scalability, and also secure isolation software stacks. Direct pass-through [5][37] dedicates among guests. gVirt presents a virtual full-fledged GPU the GPU to a single VM, providing full features and the to each VM. VMs can directly access best performance, but at the cost of device sharing performance-critical resources, without intervention capability among VMs. Mediated pass-through [19], from the hypervisor in most cases, while privileged passes through performance-critical resources, while operations from guest are trap-and-emulated at minimal mediating privileged operations on the device, with cost. Experiments demonstrate that gVirt can achieve good performance, full features, and sharing capability.
    [Show full text]
  • GPU Virtualization on Vmware's Hosted I/O Architecture
    GPU Virtualization on VMware's Hosted I/O Architecture Micah Dowty Jeremy Sugerman VMware, Inc. VMware, Inc. 3401 Hillview Ave, Palo Alto, CA 94304 3401 Hillview Ave, Palo Alto, CA 94304 [email protected] [email protected] ABSTRACT 1. INTRODUCTION Modern graphics co-processors (GPUs) can produce high fi- Over the past decade, virtual machines (VMs) have be- delity images several orders of magnitude faster than general come increasingly popular as a technology for multiplexing purpose CPUs, and this performance expectation is rapidly both desktop and server commodity x86 computers. Over becoming ubiquitous in personal computers. Despite this, that time, several critical challenges in CPU virtualization GPU virtualization is a nascent field of research. This paper have been overcome, and there are now both software and introduces a taxonomy of strategies for GPU virtualization hardware techniques for virtualizing CPUs with very low and describes in detail the specific GPU virtualization archi- overheads [2]. I/O virtualization, however, is still very tecture developed for VMware’s hosted products (VMware much an open problem and a wide variety of strategies are Workstation and VMware Fusion). used. Graphics co-processors (GPUs) in particular present a challenging mixture of broad complexity, high performance, We analyze the performance of our GPU virtualization with rapid change, and limited documentation. a combination of applications and microbenchmarks. We also compare against software rendering, the GPU virtual- Modern high-end GPUs have more transistors, draw more ization in Parallels Desktop 3.0, and the native GPU. We power, and offer at least an order of magnitude more com- find that taking advantage of hardware acceleration signif- putational performance than CPUs.
    [Show full text]
  • Enabling VDI for Engineers and Designers Positioning Information
    Enabling VDI for Engineers and Designers Positioning Information The ability to work remotely has been a critical business need for many years. But with the recent pandemic, the focus has shifted from supporting the occasional business trip or conference attendee, to supporting all non-essential employees. Virtual Desktop Infrastructure (VDI) provides a robust solution for businesses, allowing their employees to access business data on any mobile device – be it a laptop, tablet, or smartphone – without risk of losing that business data if the device is lost or stolen. With VDI the data resides in the data center, behind company firewalls. VDI allows companies to implement flexible work schedules, where employees have the freedom to pick up the kids from school and log on later in the evening to finish up. VDI also provides for centralized management of security fixes and application upgrades, ensuring all workers are using the latest version of applications. Although a great solution for your typical office workers, the benefits of VDI have largely been unavailable to another set of employees. Power users – engineers and designers using 3D or other graphics visualization applications – have generally been left out of these flexible arrangements. The graphics visualization applications they use are processor intensive, and the files they create and modify can be very large. The workstations that run the applications and the high-resolution monitors that display the designs are heavy and thus difficult to move. In order to have decent response times, power users have needed to be connected to a high-speed local area network. Work has been underway for several years to expand the benefits of VDI to the power user, and today solutions exist to support the majority of visualization applications.
    [Show full text]
  • Virtual GPU Software User Guide Is Organized As Follows: ‣ This Chapter Introduces the Capabilities and Features of NVIDIA Vgpu Software
    Virtual GPU Software User Guide DU-06920-001 _v13.0 Revision 02 | August 2021 Table of Contents Chapter 1. Introduction to NVIDIA vGPU Software..............................................................1 1.1. How NVIDIA vGPU Software Is Used....................................................................................... 1 1.1.2. GPU Pass-Through.............................................................................................................1 1.1.3. Bare-Metal Deployment.....................................................................................................1 1.2. Primary Display Adapter Requirements for NVIDIA vGPU Software Deployments................2 1.3. NVIDIA vGPU Software Features............................................................................................. 3 1.3.1. GPU Instance Support on NVIDIA vGPU Software............................................................3 1.3.2. API Support on NVIDIA vGPU............................................................................................ 5 1.3.3. NVIDIA CUDA Toolkit and OpenCL Support on NVIDIA vGPU Software...........................5 1.3.4. Additional vWS Features....................................................................................................8 1.3.5. NVIDIA GPU Cloud (NGC) Containers Support on NVIDIA vGPU Software...................... 9 1.3.6. NVIDIA GPU Operator Support.......................................................................................... 9 1.4. How this Guide Is Organized..................................................................................................10
    [Show full text]
  • With NVIDIA Virtual GPU
    Virtualizing AI/DL platform: NVIDIA Virtual GPU Solution Doyoung Kim, Sr. Solution Architect NVIDIA Virtual GPU Solution Support, Updates & Maintenance NVIDIA Virtual GPU Software Tesla Datacenter GPUs How it works? CPU Only VDI With NVIDIA Virtual GPU Apps and VMs NVIDIA Graphics Drivers Apps and VMs NVIDIA Virtual GPU Hypervisor NVIDIA virtualization software Hypervisor Server NVIDIA Tesla GPU Server Evolution of Virtual GPU Live Migration Ultra High End Simulation, NGC Support Photo Realism, 3D Rendering, AL/DL Business Designers, User Architects, Engineers 2013 2016 2017 Today GPU Compute on Virtual GPU Optimized DL Container from NGC Virtual Machine Virtual Quadro GPU CUDA & OpenCL enabled NVIDIA Virtualization Software Hypervisor NVIDIA Tesla GPU Server Virtual GPU can…. ✓ Run GPU compute workload - Any CUDA / OpenCL applications - Requires NVIDIA Virtual GPU 5.0 or higher - Requires Pascal or later GPU ✓ Be fully integrated with Virtualization solution - Live migration support from Virtual GPU 6.0 or higher - Support Cluster / Host / VM / Application level performance monitoring - Enabled for all major solutions such as vSphere / XenServer / KVM ✓ Provide every level of AI / DL Platforms - From desktop level to multi-GPU server - Fully support NVIDIA GPU Cloud (NGC) - Support up to 4 multi-vGPU per Virtual Machine* * Requires Virtual GPU 7.0 or higher / RHEL KVM only Why Virtualization? Which is best for AI/DL research system? PC with Consumer GPU GPU Server w/o Virtualization NVIDIA GPU Virtualization Manageability Require MGMT
    [Show full text]
  • GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments
    S S symmetry Article GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments Jihun Kang and Heonchang Yu * Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea; [email protected] * Correspondence: [email protected] Abstract: In remote procedure call (RPC)-based graphic processing unit (GPU) virtualization envi- ronments, GPU tasks requested by multiple-user virtual machines (VMs) are delivered to the VM owning the GPU and are processed in a multi-process form. However, because the thread executing the computing on general GPUs cannot arbitrarily stop the task or trigger context switching, GPU monopoly may be prolonged owing to a long-running general-purpose computing on graphics processing unit (GPGPU) task. Furthermore, when scheduling tasks on the GPU, the time for which each user VM uses the GPU is not considered. Thus, in cloud environments that must provide fair use of computing resources, equal use of GPUs between each user VM cannot be guaranteed. We propose a GPGPU task scheduling scheme based on thread division processing that supports GPU use evenly by multiple VMs that process GPGPU tasks in an RPC-based GPU virtualization environment. Our method divides the threads of the GPGPU task into several groups and controls the execution time of each thread group to prevent a specific GPGPU task from a long time monopolizing the GPU. The Citation: Kang, J.; Yu, H. GPGPU Task Scheduling Technique for efficiency of the proposed technique is verified through an experiment in an environment where Reducing the Performance Deviation multiple VMs simultaneously perform GPGPU tasks.
    [Show full text]
  • NVIDIA GRID GPU Acceleration for Virtualization
    NVIDIA GRID™ GPU Acceleration for Virtualization GRID For VDI GRID Enabled Solutions AGENDA User Profiles and Experiences VDI Circa 2011 Task Worker GRID Powered VDI Designer Task Worker A TRUE PC EXPERIENCE Delivered to any device for the hundreds of millions of power users who want to bring their own devices to work. Power Knowledge User Worker Key Components of GRID GRID VGX GRID GRID VCA Software GPUs Visual Computing Appliance VDI VIRTUAL MACHINE VIRTUAL DESKTOPS NVIDIA GRID Enabled Virtual Desktop NVIDIA Driver NVIDIA GRID ENABLED Hypervisor NVIDIA GRID GPU Key Components of GRID GRID VGX GRID GRID VCA Software GPUs Visual Computing Appliance Key Components of GRID GRID GPUs NVIDIA Brands ® GeForce Quadro® Tegra® NVIDIA GRID™ Tesla® NVIDIA GRID K1 NVIDIA GRID K2 GPU 4 Kepler GPUs 2 High End Kepler GPUs CUDA cores 768 (192 / GPU) 3072 (1536 / GPU) Memory Size 16GB DDR3 (4GB / GPU) 8GB GDDR5 Max Power 130 W 225 W Form Factor Dual Slot ATX, 10.5” Dual Slot ATX, 10.5” Display IO None None Aux power requirement 6-pin connector 8-pin connector PCIe x16 x16 PCIe Generation Gen3 (Gen2 compatible) Gen3 (Gen2 compatible) Cooling solution Passive Passive # users 4 - 1001 2 – 641 Watts per user ~ 1.5 W ~ 3.5 W OpenGL 4.x 4.x Microsoft DirectX 11 11 GRID VGX Virtualization support Yes Yes 1 Number of users depends on software solution, workload, and screen resolution GRID Enabled OEM Platforms IBM iDataPlex DX360 Cisco UCS C240 M3 2 GRID K1 or 2 GRID K2 2 GRID K1 or 2 GRID K2 Dell PowerEdge R720 2 GRID K1 or 2 GRID K2 HP ProLiant SL250 2 GRID
    [Show full text]
  • CUSTOMER EXPERIENCES with GPU VIRTUALIZATION and 3D REMOTING Derek Thorslund Director of Product Management, HDX
    CUSTOMER EXPERIENCES WITH GPU VIRTUALIZATION AND 3D REMOTING Derek Thorslund Director of Product Management, HDX © 2014 Citrix HDX 3D PRO – REAL CUSTOMER EXPERIENCES . Industry leaders have been delivering centralized 3D graphics applications using Citrix XenDesktop HDX 3D Pro since 2009 . Range of software and GPU hardware technologies — “Software GPU” — Dedicated GPUs — Shared GPUs including GRID vGPU © 2014 Citrix CITRIX MILESTONES IN 3D GRAPHICS REMOTING 2006 2009 2011 2012 2013 Project K2 GA of XenServer 6.0 Higher fps via OpenGL & delivers CATIA XenDesktop hypervisor NVIDIA GRID™ DirectX vGPU / to Boeing HDX 3D Pro introduces API plus GPU Sharing Dreamliner with Deep GPU improved with NVIDIA designers Compression Passthrough compression GRID™ cards © 2014 Citrix XenDesktop HDX 3D Pro XenDesktop VDI XenApp Any Multi-user device Single-user Windows Server ICA Windows 7 or 8 2008/2012 RDS XenServer or vSphere/ESXi or Bare Metal 1 user/machine: N users/machine: . XenServer/vSphere direct GPU . XenServer/vSphere direct GPU passthrough or bare metal passthrough or bare metal N users/machine: . XenServer NVIDIA GRID vGPU © 2014 Citrix Insider tips: GPU passthrough is a great option for high-end users doing intensive simulations • Servers like the HP ws460c Gen8 can take 8 GPUs, and even larger servers are coming this year • Full CUDA and OpenCL support (not yet available with GRID vGPU) • Well-established technology (XenServer and vSphere/ESXi) • Can mix GPU passthrough with vGPU on NVIDIA GRID K2 and K1 cards • High-end designers often require other parts of the HDX portfolio • 3D Space Mouse, printers, secure smart card access, etc. Industrial Automation © 2014 Citrix HDX 3D Pro client options Desktop Virtualization for High-end Graphics Users Content creation: Content access: © 2014 Citrix Segmenting the user population Tier 1: Professional users (e.g.
    [Show full text]
  • Using PCI Pass-Through for GPU Virtualization with CUDA*
    Using PCI Pass-Through for GPU Virtualization with CUDA* Chao-Tung Yang**, Hsien-Yi Wang, and Yu-Tso Liu Department of Computer Science, Tunghai University, Taichung 40704, Taiwan ROC [email protected], [email protected], [email protected] Abstract. Nowadays, NVIDIA’s CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming mul- tithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. GPU-base clusters are likely to play an important role in future cloud data centers, because some com- pute-intensive applications may require both CPUs and GPUs. The goal of this paper is to develop a VM execution mechanism that could run these applica- tions inside VMs and allow them to effectively leverage GPUs in such a way that different VMs can share GPUs without interfering with one another. Keywords: CUDA, GPU virtualization, Cloud computing, IaaS, PCI pass- through. 1 Introduction GPUs are really “manycore” processors, with hundreds of processing elements. A graphics processing unit (GPU) is a specialized microprocessor that offloads and ac- celerates graphics rendering from the central (micro-) processor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algo- rithms. We know that a CPU has only 8 cores at single chip currently, but a GPU has grown to 448 cores.
    [Show full text]