Disaggregated FPGAs: Network Performance Comparison against Bare-Metal Servers, Virtual Machines and Linux Containers

Weerasinghe, Jagath; Audigier, François; Hagleitner, Christoph; Herkersdorf, Andreas

doi:10.1109/cloudcom.2016.0018

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…The shell contains the common components required by all applications such as communication controllers (PCI end point, network controller, off-chip memory controller), clock management, routing (if multiple vFPGAs are allowed) and reconfiguration management (if dynamic reconfiguration is supported). Researches refer to the static region using different names such as static logic [6], [8], RC2F [5], service layer [9], vendor logic [13], [14], Network Service Layer [28], FPGA hypervisor [7], [12] and shell [10], [11]. The role region is usually a dynamically reconfigurable region [8], [9], [13], [5].…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based FPGA Custom Computing Machines for Streaming Applications

Al-Aghbari

Elrabaa

2019

IEEE Access

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A novel platform for launching and using field-programmable gate arrays (FPFA) custom computing machines (CCMs) in clouds and data centers is proposed. Based on a developed FPGA virtualization scheme, it allows users to create independent computing services on network-attached standalone FPGAs. The interface of the virtual FPGA (vFPGA)-based CCM is automatically generated by a virtualization layer and based on the user's specifications. An FPGA hypervisor has been developed that can be easily integrated with any cloud management tool. It allows the users to launch/use/tear down vFPGA-based CCMs in a similar manner to conventional virtual machines (VMs). A complete prototype of the proposed platform has been realized and tested with a streamed image processing application. Its performance was 3-4x and ∼1.4-2.4x times better than an SW implementation on a VM and a powerful server, respectively. Compared with other platforms for FPGA attachment to a cloud or datacenter, the proposed platform has relatively low overhead in terms of FPGA resources while providing the highest level of abstraction and virtualization. INDEX TERMS Reconfigurable computing, FPGAs, custom computing machines, cloud computing, streamed applications.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based FPGA Custom Computing Machines for Streaming Applications

Al-Aghbari

Elrabaa

2019

IEEE Access

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“…ese tightly coupled servers enable an acceleration of local applications to meet performance demands. e second approach considers FPGA as a standalone disaggregated component [36] independent from the CPU. FPGA is then directly connected to the network [29].…”

Section: Fpga For Cloudmentioning

confidence: 99%

From FPGA to Support Cloud to Cloud of FPGA: State of the Art

Skhiri

Fresse

Jamont

et al. 2019

International Journal of Reconfigurable Computing

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Field Programmable Gate Array (FPGA) draws a significant attention from both industry and academia by accelerating computationally expensive applications and achieving low power consumption. FPGAs are interesting due to the flexibility and reconfigurabiltiy of their device. Cloud computing becomes a major trend towards infrastructure and computing resources dematerialization. It provides “unlimited” storage capacities and a large number of data and applications that make collaboration easier between multiple (not domain specific) designers. Many papers in the literature have surveyed Cloud and FPGA separately and, more precisely, their services and challenges. The acceleration of applications by FPGA and the unlimited capacities of the cloud are expected to be more and more pervasive. As more and more FPGA are being deployed in traditional cloud, it is appropriate to clarify what is the cloud FPGA and which drawbacks of using FPGA in local are resolved. We present a survey of the cloud FPGA works that have been proposed to exploit the advantages of using FPGA in the cloud. We classify these studies in three services to highlight their benefits and limitations. This survey aims at motivating further researches in cloud FPGA.

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“…[11] implements NVMe disaggregation, but unlike the work presented in this paper, the authors focus on a custom software layer to expose devices instead of using the NVMeOF standard. On the other hand, [18] evaluates the impact of FPGA disaggregation.…”

Section: Related Workmentioning

confidence: 99%

Disaggregating Non-Volatile Memory for Throughput-Oriented Genomics Workloads

Call

Polo

Carrera

et al. 2018

Lecture Notes in Computer Science

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Massive exploitation of next-generation sequencing technologies requires dealing with both: huge amounts of data and complex bioinformatics pipelines. Computing architectures have evolved to deal with these problems, enabling approaches that were unfeasible years ago: accelerators and Non-Volatile Memories (NVM) are becoming widely used to enhance the most demanding workloads. However, bioinformatics workloads are usually part of bigger pipelines with different and dynamic needs in terms of resources. The introduction of Software Defined Infrastructures (SDI) for data centers provides roots to dramatically increase the efficiency in the management of infrastructures. SDI enables new ways to structure hardware resources through disaggregation, and provides new hardware composability and sharing mechanisms to deploy workloads in more flexible ways. In this paper we study a state-of-theart genomics application, SMUFIN, aiming to address the challenges of future HPC facilities.

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Disaggregated FPGAs: Network Performance Comparison against Bare-Metal Servers, Virtual Machines and Linux Containers

Cited by 22 publications

References 11 publications

Cloud-Based FPGA Custom Computing Machines for Streaming Applications

Cloud-Based FPGA Custom Computing Machines for Streaming Applications

From FPGA to Support Cloud to Cloud of FPGA: State of the Art

Disaggregating Non-Volatile Memory for Throughput-Oriented Genomics Workloads

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