Abstract

BackgroundPrediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications.ResultsIn this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture.ConclusionsSpeedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

Highlights

  • ribonucleic acid (RNA) is an important molecule in biological systems

  • The main idea is that the secondary structure of an RNA sequence consists of four fundamental substructures: stack, hairpin, internal loop, and multi-branched loop

  • We only evaluated the hybrid accelerating method in the testbed with one CPU and one Graphics Processing Units (GPU), the proposed method can be employed to hardware platforms with multiple CPUs and GPUs

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Summary

Introduction

RNA is an important molecule in biological systems. The function of RNA can be derived generally from its secondary structure. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Overview of the Zuker algorithm The Zuker algorithm predicts the most stable secondary structure for a single RNA sequence by computing its minimal free energy (MFE). It uses a “nearest neighbor” model and empirical estimates of thermodynamic parameters for neighboring interactions and loop entropies to score all possible structures [20]. It calculates the minimal free energy of the input RNA sequence on a group of recurrence relations, as shown in Formula (1) to (5) It performs a trace-back to recover the secondary structure with the base pairs. Computing energy matrices as quickly as possible is critical to improve the performance

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