FPGA-based Streaming Computation for Lattice Boltzmann Method

Sano, Kentaro; Pell, Oliver; Luk, Wayne; Yamamoto, Satoru

doi:10.1109/fpt.2007.4439254

Cited by 36 publications

(19 citation statements)

References 7 publications

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“…There are various studies using FPGAs reported so far to examine the issue. Andres et al [9] and Sano et al [11] reported the result for FPGA accelerations. However, their implementations are not for practical software packages.…”

Section: Related Workmentioning

confidence: 61%

Partial Reconfiguration of Flux Limiter Functions in MUSCL Scheme Using FPGA

Talip

Akamine

Osana

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYComputational Fluid Dynamics (CFD) is used as a common design tool in the aerospace industry. UPACS, a package for CFD, is convenient for users, since a customized simulator can be built just by selecting desired functions. The problem is its computation speed, which is difficult to enhance by using the clusters due to its complex memory access patterns. As an economical solution, accelerators using FPGAs are hopeful candidate. However, the total scale of UPACS is too large to be implemented on small numbers of FPGAs. For cost efficient implementation, partial reconfiguration which dynamically loads only required functions is proposed in this paper. Here, the MUSCL scheme, which is used frequently in UPACS, is selected as a target. Partial reconfiguration is applied to the flux limiter functions (FLF) in MUSCL. Four FLFs are implemented for Turbulence MUSCL (TMUSCL) and eight FLFs are for Convection MUSCL (CMUSCL). All FLFs are developed independently and separated from the top MUSCL module. At start-up, only required FLFs are selected and deployed in the system without interfering the other modules. This implementation has successfully reduced the resource utilization by 44% to 63%. Total power consumption also reduced by 33%. Configuration speed is improved by 34-times faster as compared to full reconfiguration method. All implemented functions achieved at least 17 times speed-up performance compared with the software implementation. key words: computational fluid dynamics (CFD), field programmable gate array (FPGA), scientific computations, reconfigurable hardware, partial reconfiguration

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Section: Related Workmentioning

confidence: 61%

Partial Reconfiguration of Flux Limiter Functions in MUSCL Scheme Using FPGA

Talip

Akamine

Osana

et al. 2012

IEICE Trans. Inf. & Syst.

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“…There are various studies using FPGAs reported so far to examine the issue. Andres the result for FPGA accelerations [2,15]. However, their implementations are not for practical software packages.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Flux Calculation Scheme in Advection Term Computation Using Partial Reconfiguration

Talip

Akamine

Hatto

et al. 2013

IJNC

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In aerospace industry, computational fluid dynamics (CFD) is used as a common design tool. Fast Aerodynamics Routines (FaSTAR) is one of the most recent CFD software package, convenient for users with various solvers and automatic generation of grid data. The problem of FaSTAR is hard to be executed in parallel machines because of its irregular and unpredictable data structure. Exploiting reconfigurable hardware with their advantages to make up for the inadequacy of the existing high performance computers had gradually become the solutions and trends. However, a single FPGA is not enough for the FaSTAR package because the whole module is very large. Instead of using a large number of chips, partially reconfigurable hardware available in recent FPGAs is explored for this application. Advection term computation module in FaSTAR is chosen as a target subroutine. We proposed a reconfigurable flux calculation scheme using partial reconfiguration technique to save hardware resources to fit in a single FPGA. We developed flux computational module and three flux calculation schemes are implemented as reconfigurable modules. This implementation has advantages of up to 42% resource saving and enhancing the configuration speed by 6.28 times. Performance evaluation also shows that 2.65 times acceleration is achieved compared to Intel Core 2 Duo at 2.4GHz.

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“…LBM is one of the methods to compute fluids based on generalized cellular automata with numerical computation [15]. In addition to the regular and coarse parallelism of LBM, it can be implemented as stream computation which is performed with data flowing through a kernel function [9]. Since these features are suitable for custom computing, there have been several reports of FPGA-based LBM computation.…”

Section: Introductionmentioning

confidence: 99%

“…These trends have motivated many researches on floating-point operations with FPGAs [5][6][7][8] and FPGA-based custom computing for scientific applications including Lattice Boltzmann method (LBM) [9][10][11][12][13][14]. LBM is one of the methods to compute fluids based on generalized cellular automata with numerical computation [15].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Scalable Custom Computing Accelerator for Computational Fluid Dynamics Based on Lattice BoltzmannMethod

Sano

2014

Sustained Simulation Performance 2014

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This paper presents a tightly-coupled FPGA cluster for custom computing of fluid dynamics simulation, and evaluates its performance with prototype implementation. For scalable and efficient computation with a lot of FPGA accelerators, we propose an accelerator-domain network (ADN) that brings lowlatency and high-speed data transfer by directly connecting FPGAs. We describe implementation of a prototype cluster node with four FPGAs, and their onchip framework for high-speed data streaming and computing. In performance evaluation, we demonstrate that our custom computing machine for fluid dynamics computation with the lattice-Boltzmann method (LBM) exploits both temporal and spatial parallelism, and scales the performance well with the number of FPGAs. As a result, we achieved 98.8 % of the peak performance of 73.0 GFlop/s with four FPGAs.

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FPGA-based Streaming Computation for Lattice Boltzmann Method

Cited by 36 publications

References 7 publications

Partial Reconfiguration of Flux Limiter Functions in MUSCL Scheme Using FPGA

Partial Reconfiguration of Flux Limiter Functions in MUSCL Scheme Using FPGA

Adaptive Flux Calculation Scheme in Advection Term Computation Using Partial Reconfiguration

FPGA-Based Scalable Custom Computing Accelerator for Computational Fluid Dynamics Based on Lattice BoltzmannMethod

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