A survey on reconfigurable accelerators for cloud computing

Kachris, Christoforos; Soudris, Dimitrios

doi:10.1109/fpl.2016.7577381

Cited by 77 publications

(36 citation statements)

References 35 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because the power increased significantly for long operations owing to cooling requirements of much larger chips on Intel platform while remained almost constant for FPGA based system. This is in line with major concerns laid by scientific community about the end of Dennard scaling; transistors count keep increasing while keeping the voltage constant resulting in higher power density in newer technology nodes [37].…”

Section: System Evaluation Across Different Technologiessupporting

confidence: 76%

NanoStreams: A Microserver Architecture for Real-Time Analytics on Fast Data Streams

Minhas

Russell

Kaloutsakis

et al. 2018

IEEE Trans. Multi-Scale Comp. Syst.

View full text Add to dashboard Cite

Abstract-Ever increasing power consumption has created great interest in energy-efficient microserver architectures but they lack the computational, networking and storage power necessary to cope with real-time data analytics. We propose NanoStreams, an integrated architecture comprising an ARM-based microserver, coupled via a novel, low latency network interface, Nanowire, to a Analytics-on-Chip architecture implemented on Field Programmable Gate Array (FPGA) technology; the architecture comprises ARM cores for performing low latency transactional processing, integrated with programmable, energy efficient Nanocore processors for high-throughput streaming analytics. The paper outlines the complete system architecture, hardware level detail, compiler, network protocol, and programming environment. We present experiments with an industrial workload from the financial services sector, comparing a state-of-the-art server based on Intel Sandy Bridge processors, an ARM based Calxeda ECS-1000 microserver and ODROID XU3 node, with the NanoStreams microserver architecture. For end-to-end workload, the NanoStreams microserver achieves energy savings up to 10.7×, 5.87× and 5× compared to the Intel server, Calxeda microserver and ODROID node respectively.

show abstract

Section: System Evaluation Across Different Technologiessupporting

confidence: 76%

NanoStreams: A Microserver Architecture for Real-Time Analytics on Fast Data Streams

Minhas

Russell

Kaloutsakis

et al. 2018

IEEE Trans. Multi-Scale Comp. Syst.

View full text Add to dashboard Cite

show abstract

“…Moreover, fast and low latency networks will become more common 67 [120,124,125] and may reshape the current offerings found in the cloud. New accelerators, such as GPUs [57,74,82], FPGAs [72,76], TPUs [75], and frameworks will become more pervasive and may make viable applications that currently are not present in the cloud environment.…”

Section: Discussionmentioning

confidence: 99%

HPC Cloud for Scientific and Business Applications

et al. 2018

View full text Add to dashboard Cite

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources-steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence. users have no visibility or concerns on costs of running jobs. However, large clusters do incur expenses and, when not properly managed, can generate resource wastage and poor quality of service.Motivated by the different utilization levels of clusters around the globe and by the need to run even larger parallel programs, in the early 2000s, Grid Computing became relevant for the HPC community. Grids offer users access to powerful resources managed by autonomous administrative domains [50,51]. The notion of monetary costs for running applications was soft, favoring a more collaborative model of resource sharing. Therefore, quality of service was not strict in Grids, having users relying on best-effort policies to run applications.In the late 2000s, cloud computing [8,26,91] was quickly increasing its maturity level and popularity, and studies started to emerge on the viability of executing HPC applications on remote cloud resources. These applications, which consume more resources than traditional cloud applications and usually are executed in batches rather than 24x7 services, range from parallel applications written in Message Passing Interface (MPI) [58,59] to the newest big data [11,14,39,101] and artificial intelligence applications-the latter mostly relying on deep learning [34,80]. Cloud then came up as an evolution of a series of technologies, mainly on virtualization and computer networks, which facilitated both workload management and interaction with remote resources respectively. Apart from software and hardware, cloud offers a business model where users pay for resources on demand. Compared to traditional HPC environments, in clouds users can quickly adjust their resource pools, via a mechanism known as elast...

show abstract

“…Work in this area is gaining significant attention. [Kachris and Soudris 2016] provides a comprehensive survey on FPGA based hardware accelerators for cloud computing.…”

Section: Computing Systemsmentioning

confidence: 99%

FPGA Dynamic and Partial Reconfiguration

2018

View full text Add to dashboard Cite

Dynamic and partial reconfiguration are key differentiating capabilities of field programmable gate arrays (FPGAs). While they have been studied extensively in academic literature, they find limited use in deployed systems. We review FPGA reconfiguration, looking at architectures built for the purpose, and the properties of modern commercial architectures. We then investigate design flows, and identify the key challenges in making reconfigurable FPGA systems easier to design. Finally, we look at applications where reconfiguration has found use, as well as proposing new areas where this capability places FPGAs in a unique position for adoption.

show abstract

A survey on reconfigurable accelerators for cloud computing

Cited by 77 publications

References 35 publications

NanoStreams: A Microserver Architecture for Real-Time Analytics on Fast Data Streams

NanoStreams: A Microserver Architecture for Real-Time Analytics on Fast Data Streams

HPC Cloud for Scientific and Business Applications

FPGA Dynamic and Partial Reconfiguration

Contact Info

Product

Resources

About