FPGA Implementation of a Data-Driven Stochastic Biochemical Simulator with the Next Reaction Method
ABSTRACT This paper introduces a scalable FPGA implementation of a stochastic simulation algorithm (SSA) called the next reaction method. There are some hardware approaches of SSAs that obtained high-throughput on reconfigurable devices such as FPGAs, but these works lacked in scalability. The design of this work can accommodate to the increasing size of target biochemical models, or to make use of increasing capacity of FPGAs. Interconnection network between arithmetic circuits and multiple simulation circuits aims to perform a data-driven multi-threading simulation. Approximately 8 times speedup was obtained compared to an execution on Xeon 2.80 GHz.
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ABSTRACT: In medical research it is of great importance to be able to quickly obtain answers to inquiries about system response to different stimuli. Modeling the dynamics of biological regulatory networks is a promising approach to achieve this goal, but existing modeling approaches suffer from complexity issues and become inefficient with large networks. In order to improve the efficiency, we propose the implementation of models of regulatory networks in hardware, which allows for highly parallel simulation of these networks. We find that our FPGA implementation of an example model of peripheral naïve T cell differentiation provides five orders of magnitude speedup when compared to software simulation.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:149-52. DOI:10.1109/IEMBS.2011.6089916
Conference Paper: Credit Risk Modelling using Hardware Accelerated Monte-Carlo Simulation[Show abstract] [Hide abstract]
ABSTRACT: The recent turmoil in global credit markets has demonstrated the need for advanced modeling of credit risk, which can take into account the effects of changing economic conditions on portfolios of loans. Such models are most easily described as Monte-Carlo simulations, but take too long to converge in software based simulators. This paper describes a hardware implementation of a loan portfolio simulator, which uses an event based model to describe changes both in prevailing economic conditions, and the behaviour of individual loans within the portfolio. Three distinct variants of the simulator are developed using transformations of the simulation algorithm, with each variant trading off area utilisation against the efficiency with which different event types can be processed. As the distribution of event types is highly dependent on the input data, each of the three variants provides the highest overall performance per FPGA for some set of input data characteristics. The hardware simulators are implemented using a Virtex-4 xc4vsx55 device running at 233 MHz in an RC2000 PCI card, and compared to four parallel software simulation threads running in a quad-core Pentium-4 Core2 at 2.4GHz, providing a speed-up of between 60 and 100 times.16th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2008, 14-15 April 2008, Stanford, Palo Alto, California, USA; 01/2008