FPGA-Accelerated Particle-Grid Mapping

Sanaullah, Ahmed; Khoshparvar, Arash; Herbordt, Martin C.

doi:10.1109/fccm.2016.53

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…Figure 8 shows the process of a single particle's influence on 64 neighborcells and their mapping to the grid memory structure. Parallel particle-to-grid mapping occurs with the use of accumulators before entering grid memory due to restrictions in using BRAMs [40,41].…”

Section: Lr Architecturementioning

confidence: 99%

Fully integrated FPGA molecular dynamics simulations

Yang

Geng

Wang

et al. 2019

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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The implementation of Molecular Dynamics (MD) on FPGAs has received substantial attention. Previous work, however, has consisted of either proof-of-concept implementations of components, usually the range-limited force; full systems, but with much of the work shared by the host CPU; or prototype demonstrations, e.g., using OpenCL, that neither implement a whole system nor have competitive performance. In this paper, we present what we believe to be the first full-scale FPGA-based simulation engine, and show that its performance is competitive with a GPU (running Amber in an industrial production environment). The system features on-chip particle data storage and management, short-and long-range force evaluation, as well as bonded forces, motion update, and particle migration. Other contributions of this work include exploring numerous architectural trade-offs and analysis on various mappings schemes among particles/cells and the various on-chip compute units. The potential impact is that this system promises to be the basis for long timescale Molecular Dynamics with a commodity cluster.

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Section: Lr Architecturementioning

confidence: 99%

Fully integrated FPGA molecular dynamics simulations

Yang

Geng

Wang

et al. 2019

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

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“…Most studies on FPGA accelerated MD target the PP calculations [10], [11], [13], [14], [20][21][22], since they make up for over 90% of the sequential runtime in typical simulations. Apart from these studies, only relatively few papers cover PME and other aspects [16], [18], [19], [45].…”

Section: MD Acceleration Using Fpgasmentioning

confidence: 99%

“…In [18], [19], it is shown that commonly used 3D FFTs in the order of 64 3 can be conveniently solved on single FPGAs at competitive speeds in the range of a few 100 µs. In [16] they present an interpolator design able to carry out the charge spreading and force gathering stems within PME, and in [45] a preliminary analysis of bonded-force computation on FPGAs is performed.…”

Section: MD Acceleration Using Fpgasmentioning

confidence: 99%

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On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations

Schaffner¹,

Benini²

2018

Preprint

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Classical molecular dynamics (MD) simulations are important tools in life and material sciences since they allow studying chemical and biological processes in detail. However, the inherent scalability problem of particle-particle interactions and the sequential dependency of subsequent time steps render MD computationally intensive and difficult to scale. To this end, specialized FPGA-based accelerators have been repeatedly proposed to ameliorate this problem. However, to date none of the leading MD simulation packages fully support FPGA acceleration and a direct comparison of GPU versus FPGA accelerated codes has remained elusive so far.With this report, we aim at clarifying this issue by comparing measured application performance on GPU-dense compute nodes with performance and cost estimates of a FPGA-based singlenode system. Our results show that an FPGA-based system can indeed outperform a similarly configured GPU-based system, but the overall application-level speedup remains in the order of 2× due to software overheads on the host. Considering the price for GPU and FPGA solutions, we observe that GPU-based solutions provide the better cost/performance tradeoff, and hence pure FPGA-based solutions are likely not going to be commercially viable. However, we also note that scaled multi-node systems could potentially benefit from a hybrid composition, where GPUs are used for compute intensive parts and FPGAs for latency and communication sensitive tasks.

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“…Reconfigurable architectures, such as FPGAs, are becoming increasingly popular since their logic can be configured to construct application-specific architectures [20–25]. For MLPs in particular, arbitrary sized models can be easily implemented, scaled, and even transferred to newer generation technologies by recompiling the design.…”

Section: Introductionmentioning

confidence: 99%

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

et al. 2018

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BackgroundReal-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection.ResultsWe demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively.ConclusionIn our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

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FPGA-Accelerated Particle-Grid Mapping

Cited by 14 publications

References 9 publications

Fully integrated FPGA molecular dynamics simulations

Fully integrated FPGA molecular dynamics simulations

On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

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