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GPU computing for systems biology.

Lorenzo Dematté¹, Davide Prandi

¹CoSBi Centre, Italy.

Briefings in Bioinformatics

|March 10, 2010

Summary

Graphics processing units (GPUs) offer a cost-effective solution for simulating complex biological systems. This review explores recent advancements in leveraging GPU computing for biological modeling and in-silico simulations.

Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Integrating vast experimental data requires detailed models of complex biological systems.
In-silico simulation aids in understanding biological system dynamics and testing experimental conditions.
High-performance computing is often necessary but expensive for these simulations.

Purpose of the Study:

To review recent efforts in utilizing Graphics Processing Units (GPUs) for biological system simulations.
To highlight GPGPU as an accessible alternative to traditional high-performance computing.

Main Methods:

Exploration of GPGPU (general-purpose computing on GPUs) for scientific computation.
Review of algorithms and programming paradigms specific to GPU computing.

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Analysis of existing research on GPU acceleration for biological simulations.

Main Results:

GPGPU provides high computational power at a significantly lower cost compared to traditional clusters.
GPU programming requires specialized algorithms due to its distinct paradigm from CPU computing.
Emerging research demonstrates the potential of GPUs in accelerating biological simulations.

Conclusions:

GPGPU presents a viable and cost-effective approach for simulating complex biological systems.
Further development in GPU-specific algorithms is crucial for widespread adoption in computational biology.
Leveraging GPUs can democratize access to powerful simulation capabilities for biological research.