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    Area of Science:

    • Computational biology
    • Drug discovery
    • Biophysics

    Background:

    • Molecular docking is vital for understanding biological processes and designing new therapeutics.
    • Previous GPU-accelerated docking code (PIPER) offered a 5x speed-up, but advancements reduced its relative performance.
    • The need for efficient computational methods in drug design is ever-increasing.

    Purpose of the Study:

    • To upgrade the GPU-accelerated PIPER code to regain and surpass previous performance benchmarks.
    • To investigate the impact of algorithm changes and GPU code migration on docking speed.
    • To enhance the computational efficiency of molecular docking for broader applications.

    Main Methods:

    • Complete rewrite of the GPU-accelerated PIPER code.
    • Algorithm modifications to optimize performance.
    • Migration of remaining CPU-bound code to the GPU.
    • Utilized GPU-accelerated 3D Fast Fourier Transform (FFT) libraries (cuFFT).

    Main Results:

    • Achieved a 7x improvement in GPU performance for the upgraded PIPER code.
    • Obtained a 3.3x speed-up compared to the CPU-only version.
    • Identified the 3D FFT library as the primary factor influencing computational time differences between CPU and GPU.

    Conclusions:

    • The upgraded GPU PIPER code offers significant performance enhancements for molecular docking.
    • The integration into the ClusPro docking server provides a powerful tool for over 4000 users.
    • This advancement contributes to more efficient drug design and biological process modeling.