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High performance in silico virtual drug screening on many-core processors.
Simon McIntosh-Smith1, James Price1, Richard B Sessions2
1Department of Computer Science, University of Bristol, Bristol, UK.
The International Journal of High Performance Computing Applications
|May 15, 2015
Summary
Bristol University Docking Engine (BUDE) is now optimized for parallel programming using OpenCL. This computational drug screening tool achieves high performance on modern GPUs and offers portability across diverse hardware architectures.
Area of Science:
- Computational chemistry
- Drug discovery and development
- High-performance computing
Background:
- Drug screening is crucial for pharmaceutical development.
- Computational methods like molecular docking supplement traditional lab-based screening.
- Molecular docking simulates drug-target interactions, typically with proteins.
Purpose of the Study:
- To optimize the Bristol University Docking Engine (BUDE) for modern parallel processing.
- To leverage the OpenCL standard for enhanced computational performance and portability.
- To accelerate drug discovery through efficient molecular docking simulations.
Main Methods:
- Porting the BUDE software to the OpenCL parallel programming language.
- Implementing highly optimized code for many-core processors, including GPUs.
- Benchmarking performance on various hardware architectures (Nvidia GPUs, AMD GPUs, Intel Xeon Phi, multi-core CPUs).
Main Results:
- The OpenCL implementation of BUDE achieves 1.43 TFLOP/s on a single Nvidia GTX 680 GPU (46% of peak performance).
- Demonstrated effective performance portability across diverse computing platforms.
- Validated high-performance computing capabilities for drug screening applications.
Conclusions:
- OpenCL enables BUDE to harness the power of modern many-core processors for drug screening.
- BUDE's performance and portability facilitate efficient computational drug discovery.
- The optimized engine accelerates the simulation of drug-target interactions.
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