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Vina-FPGA-Cluster: Multi-FPGA Based Molecular Docking Tool With High-Accuracy and Multi-Level Parallelism.

Ming Ling, Zhihao Feng, Ruiqi Chen

IEEE Transactions on Biomedical Circuits and Systems

|April 15, 2024

Summary

This study introduces Vina-FPGA-cluster, a novel multi-FPGA system for accelerating molecular docking (AutoDock Vina). It achieves high accuracy and significant speedup with reduced power consumption compared to existing methods.

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Area of Science:

Computational chemistry
Bioinformatics
Hardware acceleration

Background:

AutoDock Vina is crucial for drug discovery but computationally intensive.
Existing FPGA accelerators for Vina face accuracy, design, parallelization, and scalability limitations.

Purpose of the Study:

To develop a high-accuracy, multi-level parallel Vina acceleration tool on a multi-FPGA platform.
To overcome limitations of previous FPGA-based Vina accelerators.

Main Methods:

Hybrid fixed-point quantization for accuracy preservation.
SystemC modeling for accelerated hardware design evaluation.
Bidirectional AG module for data-level parallelism.
Optimized architecture for task-level parallelism across multiple FPGAs.

Main Results:

Vina-FPGA-cluster achieves a minimal 0.2% accuracy loss (RMSD < 2Å).
It provides 27.33x speedup over CPU and 7.26x over Vina-FPGA.
Achieves 1.38x speedup over Vina-GPU with only 28.99% of its power consumption.

Conclusions:

Vina-FPGA-cluster offers an energy-efficient, high-accuracy, and scalable solution for molecular docking acceleration.
The proposed methods effectively address previous limitations in FPGA-based Vina acceleration.