Speeding up ecological and evolutionary computations in R; essentials of high performance computing for biologists

Area of Science:

• Biological research
• Computational biology
• Ecological and evolutionary research

Background:

• Computational challenges can limit the scope and quality of biological research.
• Existing solutions for computational efficiency are scattered across various sources.
• There is a need for consolidated, practical guidance on optimizing code for biological applications.

Purpose of the Study:

• To review and synthesize solutions for common computational efficiency problems in ecological and evolutionary research.
• To provide practical techniques and tools for improving code performance in biological studies.
• To demonstrate the significant speed improvements achievable through computational optimization.

Main Methods:

• Review of existing literature and techniques for computational efficiency.
• Emphasis on methods applicable to ecological and environmental problems.
• Development and utilization of a new R package (aprof) for bottleneck identification.
• Inclusion of practical examples and supporting information.

Main Results:

• Demonstrated straightforward methods for writing efficient code.
• Showcased the effectiveness of profiling and parallel computing.
• The 'aprof' R package aids in identifying and resolving computational bottlenecks.
• Achieved substantial speed improvements, ranging from 10.5x to 14,000x faster.

Conclusions:

• Improving computational efficiency is feasible and essential for modern biological research.
• Optimized code allows biologists to tackle more complex tasks and sophisticated analyses.
• Enhanced computational power enables more ambitious research questions in ecology and evolution.