FPGA-Accelerated Molecular Dynamics Simulations System

Guo, He; Su, Lili; Wang, Yuxin; Zhu, Liehuang

doi:10.1109/embeddedcom-scalcom.2009.71

Cited by 4 publications

(5 citation statements)

References 14 publications

(18 reference statements)

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“…As an initial effort, we mainly focus on the acceleration of the short-range, non-bonded force computation that is the critical path of the MD algorithm, similar to prior studies [1,13,14,15,16]. Therefore, we will only report the performance of one simulation iteration for the short-range, non-bonded force computation throughout this paper.…”

Section: Algorithmmentioning

confidence: 99%

“…Compared to ASIC-based and GPU-based acceleration approaches, FPGA-based acceleration emerges as an attractive alternative because FPGAs provide better programmability than ASICs, and better power and energy efficiency than GPUs. In prior studies, there have been several attempts (e.g., [1,13,14,15,16]) to accelerate MD using FPGAs. Most of these prior studies mainly focus on the acceleration for the critical path of the MD algorithm, which is the computation of short-range, non-bonded forces for each atom in every simulation iteration.…”

Section: Introductionmentioning

confidence: 99%

“…Our goal is achieve a comparable performance of prior reference RTL acceleration of MD simulation [13,14], but with more affordable programming cost in HLS. Similar to prior studies [1,13,14,15,16], we focus on the acceleration of the critical path of the MD algorithm, that is, the computation of short-range, non-bonded forces for each atom, as explained in Section 2. To provide more insights into the community, we start with a naive HLS design and optimize the HLS design all the way to generate the reference architecture written in RTL [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis

Cong,

Fang,

Kianinejad

et al. 2016

Preprint

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Molecular dynamics (MD) simulation is one of the past decade's most important tools for enabling biology scientists and researchers to explore human health and diseases. However, due to the computation complexity of the MD algorithm, it takes weeks or even months to simulate a comparatively simple biology entity on conventional multicore processors. The critical path in molecular dynamics simulations is the force calculation between particles inside the simulated environment, which has abundant parallelism. Among various acceleration platforms, FPGA is an attractive alternative because of its low power and high energy efficiency. However, due to its high programming cost using RTL, none of the mainstream MD software packages has yet adopted FPGA for acceleration.In this paper we revisit the FPGA acceleration of MD in high-level synthesis (HLS) so as to provide affordable programming cost. Our experience with the MD acceleration demonstrates that HLS optimizations such as loop pipelining, module duplication and memory partitioning are essential to improve the performance, achieving a speedup of 9.5X compared to a 12-core CPU. More importantly, we observe that even the fully optimized HLS design can still be 2X slower than the reference RTL architecture due to the common dynamic (conditional) data flow behavior that is not yet supported by current HLS tools. To support such behavior, we further customize an array of processing elements together with a datadriven streaming network through a common RTL template, and fully automate the design flow. Our final experimental results demonstrate a 19.4X performance speedup and 39X energy efficiency for the widely used ApoA1 MD benchmark on the Convey HC1ex FPGA compared to a 12-core Intel Xeon server.

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Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis

Cong,

Fang,

Kianinejad

et al. 2016

Preprint

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show abstract

“…An advantage of FGPA over the conventional computing clusters is the customization of hardware components, which leads to the decreased cost and electric power. 172 The general process of MD calculation on an FGPA can be summarized in few steps, 173 similar to the steps implemented in CPU. First, the cell list is loaded to the FGPA memory, whereas the particle position data are stored in a memory location external to the FGPA.…”

Section: Recon¯gurable Computingmentioning

confidence: 99%

Molecular Dynamics Simulation of Iron — A Review

Chui

Liu

et al. 2015

SPIN

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Molecular dynamics (MD) is a technique of atomistic simulation which has facilitated scienti¯c discovery of interactions among particles since its advent in the late 1950s. Its merit lies in incorporating statistical mechanics to allow for examination of varying atomic con¯gurations at nite temperatures. Its contributions to materials science from modeling pure metal properties to designing nanowires is also remarkable. This review paper focuses on the progress of MD in understanding the behavior of iron -in pure metal form, in alloys, and in composite nanomaterials. It also discusses the interatomic potentials and the integration algorithms used for simulating iron in the literature. Furthermore, it reveals the current progress of MD in simulating iron by exhibiting some results in the literature. Finally, the review paper brie°y mentions the development of the hardware and software tools for such large-scale computations.

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“…Differentiating this potential model with respect to the distance gives the motion model φ' of the molecule [13], which has a balanced distance r 0 where the momentum is φ' (r 0 ) = 0. Brownian motion is random oscillatory molecular motion produced by the internal energy resulting from heat input from outside the system.…”

Section: Thermodynamics Modelmentioning

confidence: 99%

Collective Movement Method for Swarm Robot based on a Thermodynamic Model

Yamagishi¹,

Suzuki²

2017

ijacsa

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Abstract-In this paper, a distributed collective movement control method is proposed for a swarm robotics system based on an internal energy thermodynamic model. The system can move between obstacles with a changing aggregation suitable for confronting obstacle arrangements in an environment. The swarm robot shape is a fixed aggregation formed by virtual attraction and repulsion forces based on the proposed method. It follows a leader agent while retaining its shape. When the swarm robot aggregation shape cannot be maintained during movement though narrow spaces with obstacles, the swarm robot flexibly changes shape according to that of the local environment. To this end, it employs virtual thermal motion, which is made possible with directives and enables continuous movement. A simulation confirmed the capability of the proposed method in enabling the solidity and flexibility collective movement of swarm robots. The results furthermore showed that the parameter setting range is important for applying the proposed method to collective movement.

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FPGA-Accelerated Molecular Dynamics Simulations System

Cited by 4 publications

References 14 publications

Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis

Revisiting FPGA Acceleration of Molecular Dynamics Simulation with Dynamic Data Flow Behavior in High-Level Synthesis

Molecular Dynamics Simulation of Iron — A Review

Collective Movement Method for Swarm Robot based on a Thermodynamic Model

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