Accelerating All-Atom Normal Mode Analysis with Graphics Processing Unit.

Area of Science:

• Computational biology
• Biophysics
• Structural biology

Background:

• All-atom normal mode analysis (NMA) is crucial for understanding protein dynamics and aiding drug design.
• Current NMA methods face computational limitations due to the time-intensive Hessian matrix diagonalization.
• Householder-QR transformation is a key bottleneck in traditional NMA calculations.

Purpose of the Study:

• To demonstrate the application of graphics processing units (GPUs) for accelerating all-atom NMA.
• To evaluate the performance and accuracy of GPU-accelerated NMA using Compute Unified Device Architecture (CUDA).
• To analyze the impact of numerical precision (single vs. double) on GPU performance and results.

Main Methods:

• Implementation of Householder-QR transformation for Hessian matrix diagonalization on GPUs using CUDA.
• Comparison of runtime and speedup between GPU-accelerated NMA and traditional CPU-based NMA.
• Assessment of accuracy and performance trade-offs using single and double precision calculations on GPUs.

Main Results:

• GPU-accelerated all-atom NMA achieved a significant reduction in diagonalization runtime.
• Over 20x speedup was observed compared to CPU-based NMA.
• Double precision on GPU yielded accurate results with acceptable speedup by minimizing data transfer.

Conclusions:

• GPU acceleration offers a substantial performance improvement for all-atom NMA.
• The developed CUDA-based approach effectively overcomes computational bottlenecks in NMA.
• Further strategies are discussed to address inherent GPU limitations and scale up computations.