Evaluating standard-based self-virtualizing devices: A performance study on 10 GbE NICs with SR-IOV support

Liu, Jiuxing

doi:10.1109/ipdps.2010.5470365

Cited by 88 publications

(35 citation statements)

References 12 publications

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“…However, the access from multiple VMs to the physical device has to be multiplexed by the network interface firmware. Although this approach achieves reasonably good performance [22], a new hardware support has to be incorporated to PCI devices. Nevertheless, the direct device access cannot provide bare-metal performance, according to Gordon et al [23], due to the host involvement, as it intercepts all interrupts inducing multiple unwarranted guest/host context switches.…”

Section: I/o Virtualizationmentioning

confidence: 99%

Performance analysis of HPC applications in the cloud

Expósito

Taboada

Ramos

et al. 2013

Future Generation Computer Systems

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The scalability of High Performance Computing (HPC) applications depends heavily on the efficient support of network communications in virtualized environments. However, Infrastructure as a Service (IaaS) providers are more focused on deploying systems with higher computational power interconnected via high-speed networks rather than improving the scalability of the communication middleware. This paper analyzes the main performance bottlenecks in HPC applications scalability on Amazon EC2 Cluster Compute platform: (1) evaluating the communication performance on shared memory and a virtualized 10 Gigabit Ethernet network; (2) assessing the scalability of representative HPC codes, the NAS Parallel Benchmarks, using an important number of cores, up to 512; (3) analyzing the new cluster instances (CC2), both in terms of single instance performance, scalability and costefficiency of its use; (4) suggesting techniques for reducing the impact of the virtualization overhead in the scalability of communication-intensive HPC codes, such as the direct access of the Virtual Machine to the network and reducing the number of processes per instance; and (5) proposing the combination of message-passing with multithreading as the most scalable and cost-effective option for running HPC applications on Amazon EC2 Cluster Compute platform.

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Section: I/o Virtualizationmentioning

confidence: 99%

Performance analysis of HPC applications in the cloud

Expósito

Taboada

Ramos

et al. 2013

Future Generation Computer Systems

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“…We measure with netperf the request/response latency between two processes running on different servers 5 , which is typically used to characterize communication performance in data-centers [19,26,34]. In these experiments, server CPUs and I/O buses are lightly loaded.…”

Section: Baseline: Static Workloadmentioning

confidence: 99%

VNToR

Fietz

Whitlock

Ioannidis

et al. 2016

Proceedings of the Seventh ACM Symposium on Cloud Computing

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Cloud providers typically implement abstractions for network virtualization on the server, within the operating system that hosts the tenant virtual machines or containers. Despite being flexible and convenient, this approach has fundamental problems: incompatibility with bare-metal support, unnecessary performance overhead, and susceptibility to hypervisor breakouts. To solve these, we propose to offload the implementation of network-virtualization abstractions to the top-of-rack switch (ToR). To show that this is feasible and beneficial, we present VNToR, a ToR that takes over the implementation of the security-group abstraction. Our prototype combines commodity switching hardware with a custom software stack and is integrated in OpenStack Neutron. We show that VNToR can store tens of thousands of access rules, adapts to traffic-pattern changes in less than a millisecond, and significantly outperforms the state of the art.

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“…Both specifications provide DMA remapping to enable PCIpassthrough as well as other features such as interrupt remapping, hypervisor snooping, and security control mechanisms to ensure proper and efficient hardware utilization. PCI passthrough has been studied within the context of networking [8], storage [9], and other PCI-attached devices; however, GPUs have historically lagged behind other devices in their support for virtual machine passthrough.…”

Section: B Pci Passthroughmentioning

confidence: 99%

GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications

Walters

Younge

Kang

et al. 2014

2014 IEEE 7th International Conference on Cloud Computing

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Abstract-As more scientific workloads are moved into the cloud, the need for high performance accelerators increases. Accelerators such as GPUs offer improvements in both performance and power efficiency over traditional multi-core processors; however, their use in the cloud has been limited. Today, several common hypervisors support GPU passthrough, but their performance has not been systematically characterized.In this paper we show that low overhead GPU passthrough is achievable across 4 major hypervisors and two processor microarchitectures. We compare the performance of two generations of NVIDIA GPUs within the Xen, VMWare ESXi, and KVM hypervisors, and we also compare the performance to that of Linux Containers (LXC). We show that GPU passthrough to KVM achieves 98-100% of the base system's performance across two architectures, while Xen and VMWare achieve 96-99% of the base systems performance, respectively. In addition, we describe several valuable lessons learned through our analysis and share the advantages and disadvantages of each hypervisor/GPU passthrough solution.

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Evaluating standard-based self-virtualizing devices: A performance study on 10 GbE NICs with SR-IOV support

Cited by 88 publications

References 12 publications

Performance analysis of HPC applications in the cloud

Performance analysis of HPC applications in the cloud

VNToR

GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications

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