Process of Efficiently Parallelizing a Protein Structure Determination Algorithm

TL;DR

This paper presents a parallelization process for REDcRAFT, a protein structure determination algorithm that reduces computational complexity and achieves significant scalability improvements by optimizing system overhead and bottlenecks.

Contribution

The paper introduces a parallelization approach for REDcRAFT, significantly enhancing its scalability and efficiency in protein structure determination.

Findings

Parallel execution reduces execution time by nearly two orders of magnitude.

Most of the original execution time was spent in system overhead.

Identifying and addressing bottlenecks improved scalability of REDcRAFT.

Abstract

Computational protein structure determination involves optimization in a problem space much too large to exhaustively search. Existing approaches include optimization algorithms such as gradient descent and simulated annealing, but these typically only find local minima. One novel approach implemented in REDcRAFT is to instead of folding a protein all at the same time, fold it residue by residue. This simulates a protein folding as each residue exits from the generating ribosome. While REDcRAFT exponentially reduces the problem space so it can be explored in polynomial time, it is still extremely computationally demanding. This algorithm does have the advantage that most of the execution time is spent in inherently parallelizable code. However, preliminary results from parallel execution indicate that approximately two-thirds of execution time is dedicated to system overhead. Additionally, by carefully analyzing and timing the structure of the program the major bottlenecks can be identified. After addressing these issues, REDcRAFT becomes a scalable parallel application with nearly two orders of magnitude improvement.