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A study on management system and parallel application library for stand-alone FPGA clusters (本文)

弘中, 和衛 慶應義塾大学

2022.07.27

概要

In recent years, Mobile Edge Computing (MEC) has been gaining attention due to the progress of the expansion of the 5th Generation mobile network (5G), which offers high capacity and low latency wireless communication. MEC is an edge computing method that uses computing resources located in the edge environment of a wireless base station to application task offloading requiring computing power near client devices. Utilizing the 5G network and MEC enables the implementation of various applications that have been difficult to achieve with cloud computing and mobile devices due to network latency.

However, MEC is often installed on power and space restricted environments such as wireless base stations; hence, it is difficult to employ a conventional computing platform used in the data center. Therefore, this thesis focuses on Field-Programable-Gate-Array (FPGA) as a computing platform for MEC, which has high flexibility with programmable logic circuits and achieves high performance with hardware-based data processing. Traditionally, FPGAs have been utilized as hardware devices to realize logic circuits for specific applications. However, the latest FPGAs are multifunctional and utilized for highly efficient general computing acceleration platforms.

As a proof-of-concept to validate the application of FPGA systems to MEC, we have developed a scale-flexible stand-alone multi-FPGA system called FiC and its successor M-KUBOS. A stand-alone multi-FPGA system is an FPGA system consisting of multiple stand-alone FPGA nodes with a high-speed inter-FPGA network, and each FPGA node can operate in both stand-alone and clustered mode. The system scale is flexibly adjustable according to the application and performance requirements by changing the number of FPGA nodes. Utilizing FPGA’s high energy efficiency and flexibility, the stand-alone multi-FPGA system is expected to be an ideal computing platform for MEC, achieving space-saving and low power consumption.

On the other hand, FPGA is still a low-layer device compared to CPUs and GPUs. There are challenges in improving platform accessibility, manageability, and application programmability to adopt a stand-alone multi-FPGA system for MEC.
For instance, FPGAs are versatile devices and can implement various dedicated hardware logic, but in order to use them from applications, they generally require dedicated device drivers and middleware. Therefore, to adopt FPGA as a computing platform on MEC, it is necessary to provide a general-purpose platform management interface and protocol that various application platforms can support. In addition, application programmability for stand-alone multi-FPGA systems is a fundamental problem because no standard method has been provided. Hence, providing an application development environment for a stand-alone multi-FPGA system is also required.

To facilitate the use and management of stand-alone multi-FPGA systems from applications, this thesis proposes a platform management system called FiC-RFC for stand-alone multi-FPGA systems. With FiC-RFC, MEC applications can access stand-alone multi-FPGA systems directly via simple HTTP access by RESTful APIs, which is a programming fashion widely used in cloud applications. Using RESTful APIs allows direct use of stand-alone multi-FPGA systems from various application platforms and improves platform accessibility and manageability. In a scenario that provisioning FPGA applications by FiC-RFC to 12 nodes FiC system over HTTP access, FiC-RFC achieved scalable management performance and provisioned an application below 20 seconds. The result shows that FiC-RFC improves platform utilization efficiency compared to conventional application provisioning methods such as JTAG, and it supports enough manageability as a computing platform on MEC.

To improve the programmability of applications on stand-alone multi-FPGA systems, we also focus on Message Passing Interface (MPI), a parallel programming environment commonly used in distributed memory architectures, and propose FiC-MPI, an MPI-compatible library to facilitate parallel programming in stand-alone multi-FPGA systems. This MPI-compatible library can be directly used in the standard C/C++ HLS FPGA application development flow. It is expected to enable application designers to design parallel programming applications with MPI interface for multi-FPGA nodes easily and improve the programmability of stand-alone multi-FPGA systems. Also, the FiC-MPI provides an application test and debug environment called FiC-MPI Simulator for the MPI applications. It eliminates FPGA implementation of the debugging on the real FPGA platform and improves application productivity dramatically. In order to show the feasibility and usability of FiC-MPI, the thesis ported an MPI-based general numerical benchmark Himeno Benchmark (Himeno-BMT) to an application for six nodes M-KUBOS system as a case study. The ported Himeno-BMT implemented with FiC-MPI achieved 178.7 MFLOPS with a single node; scaled to 643.7 MFLOPS with four nodes; and 896.9 MFLOPS with six nodes. The result showed that the FiC-MPI could achieve scalable performance along with the number of nodes, and the case study demonstrated the easiness of developing parallel programs with FiC-MPI on multi-FPGA systems.

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