GPU Virtualization Support in Cloud System

Yeh, Chih-Yuan; Kao, Chung‐Yao; Hung, Wei-Shu; Lin, Ching-Chi; Liu, Pangfeng; Wu, Jan‐Jan; Liu, Kuang-Chih

doi:10.1007/978-3-642-38027-3_45

Cited by 8 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2010, Amazon EC2 introduced Cluster GPU Instances (CGIs), which provide two NVIDIA Tesla GPUs per VM [Yeh et al 2013]. CGIs can support HPC applications requiring massive parallel processing power by exposing native GPUs to each guest OS directly.…”

Section: Methods Supporting a Single Vmmentioning

confidence: 99%

GPU Virtualization and Scheduling Methods

2017

View full text Add to dashboard Cite

The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey paper, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.

show abstract

Section: Methods Supporting a Single Vmmentioning

confidence: 99%

GPU Virtualization and Scheduling Methods

2017

View full text Add to dashboard Cite

show abstract

“…Amazon Elastic Compute Cloud (Amazon EC2) [59] is the first platform that introduced GPUs for cloud users, and used Intel's pass-through technology [60]. Amazon introduced Cluster GPU Instances (CGI), which gives couple of NVIDIA GPGPUs to every VM [61].…”

Section: Hardware-based Gpgpu Virtualizationmentioning

confidence: 99%

GPGPU Virtualization Techniques a Comparative Survey

Alyas¹,

Hassan²

2018

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

The Graphic Processing Units (GPU) are being adopted in many High Processing Computing (HPC) facilities because of their massively parallel and extraordinary computing power, which makes it possible to accelerate many general purpose implementations from different domains. A general-purpose GPU (GPGPU) is a GPU that performs computations that were traditionally handled by central processing unit (CPU) to accelerate applications along with handling traditional computations for graphics rendering. However, GPUs have some limitations, such as increased acquisition costs as well as larger space requirements, more powerful energy supplies, and their utilization is usually low for most workloads. That results in the need of GPU virtualization to maximize the use of an acquired GPU to share between the virtual machines for optimal use, to reduce power utilization and minimize the costs. This study comparatively reviews the recent GPU virtualization techniques including API remoting, para, full and hardware based virtualization, targeted for general-purpose accelerations.

show abstract

“…For example, in [33], a framework for high performance computing applications that uses hardware acceleration provided by GPUs to address the performance issues associated with system-level virtualization technology is proposed. The goal of [34] is, instead, to design a GPU provision system that combines Compute Unified Device Architecture (CUDA) programs from different virtual machines and execute them concurrently so as to support the concept of GPU sharing among virtual machines.…”

Section: Related Workmentioning

confidence: 99%

Modeling and analysis of performances for concurrent multithread applications on multicore and graphics processing unit systems

Cerotti

Gribaudo

Iacono

et al. 2015

Concurrency and Computation

View full text Add to dashboard Cite

The capabilities of multicore processors lead them to be widely adopted in systems at any scale, since their are able to provide more computing power at a lower consumption and dissipation cost. System designers are challenged to a deeper understanding of multicore functioning in order to fully exploit them while keeping the optimal balance between cores utilization and optimal throughput, response time and energy usage.Besides the advancement of general purpose CPUs, the same technological evolution leads to the rise of GPUs, dramatic evolution of graphical coprocessors, that are now affordable, efficient, dedicated computing units, capable of parallel computing and equipped with facilities that make them suited for supporting the main CPU of a system in running ordinary applications. The availability of commercial off-the-shelf (COTS) multicore computers, eventually equipped with one or more GPUs, makes them the basic building block of data centers devoted to cloud applications or scientific computing.The way to optimal exploitation of such a wide amount of computing power passes through the ability of matching the best scheduling of hardware resources with the software characteristics of the applications. This requires appropriate models and evaluation methods.Simulation and analytical techniques are essential tools to support the design and the management process of such architectures, but a sound characterization of the workloads is required. Typical workloads consist in multithreaded applications, with different characteristics, that dynamically span over the cores of multiple machines, connected by fast networks.In this paper we propose several parametric performance models for different configurations of multicore machines, with or without GPU support, running multiple class multithreaded applications, aiming to supply a detailed modeling help for complex data centers. ANALYSIS OF PERFORMANCES ON MULTITHREAD APPLICATIONS 445 ‡ The transcoding of image elements may be performed by means of multiple threads, but the most of the processing is run by a single thread. ANALYSIS OF PERFORMANCES ON MULTITHREAD APPLICATIONS 449 Figure 11. Queuing network of a system running two different types of application with one characterized by general-purpose computing on graphics processing units workload. I/O, input/output; CPU, central processing unit.

show abstract

GPU Virtualization Support in Cloud System

Cited by 8 publications

References 8 publications

GPU Virtualization and Scheduling Methods

GPU Virtualization and Scheduling Methods

GPGPU Virtualization Techniques a Comparative Survey

Modeling and analysis of performances for concurrent multithread applications on multicore and graphics processing unit systems

Contact Info

Product

Resources

About