Contact

Improving Performance of the Smith-Waterman Algorithm

Azati improved the performance of the Smith-Waterman algorithm by applying computing acceleration technologies, reducing the time required to run queries by 30-50 times, while maintaining the accuracy of the results.

Discuss an idea

All Technologies Used

C
C
C++
C++
NVIDIA CUDA
NVIDIA CUDA

Motivation

The client approached Azati with a request to optimize the performance of the Smith-Waterman algorithm, which is crucial for sequence alignment in bioinformatics. The goal was to significantly reduce the time required to perform sequence searches, especially those involving large datasets, without compromising the accuracy or completeness of the results. By improving the algorithm's efficiency, the client aimed to enhance the speed of their research processes while maintaining the high accuracy that is essential in biotechnology research.

Main Challenges

Challenge 1
Smith-Waterman's Speed Problem

The Smith-Waterman algorithm is highly accurate but extremely slow when processing long query sequences, which could take hours to complete, impacting productivity. Azati proposed optimizing the algorithm with GPU and cloud computing to significantly reduce the processing time while maintaining accuracy.

Challenge 2
Efficient Sequencing

Maintaining the balance between speed and accuracy was crucial, as the algorithm needed to deliver precise results without excessive delays. Azati focused on enhancing computational efficiency to achieve faster results while ensuring the precision of the sequence matches.

Key Features

  • Improved Processing Speed: The Smith-Waterman algorithm’s query execution time was reduced by 30-50 times, significantly improving the overall performance.
  • Maintained Accuracy: Despite the accelerated processing, the accuracy of the results remained intact, ensuring reliable and precise sequence matching.
  • Cloud and GPU Utilization: The solution took advantage of cloud computing and GPU acceleration to optimize the algorithm's performance.

Our Approach

Bottleneck Analysis
We started by analyzing the existing Smith-Waterman algorithm and identifying its performance bottlenecks, particularly in the handling of large query sequences.
GPU Acceleration Exploration
We explored GPU acceleration using NVIDIA® CUDA® technology to offload computationally intensive tasks to the GPU, reducing the overall processing time.
Algorithm Optimization and Cloud Integration
Our team implemented the algorithm improvements and integrated cloud computing resources to ensure scalability and handle larger datasets efficiently.
Accuracy and Performance Testing
We thoroughly tested the updated algorithm to ensure it maintained the accuracy of the sequence alignment while significantly reducing the query execution time.
Deployment and Impact
We deployed the improved algorithm, delivering a 30–50 times speedup in processing time, enabling the client to complete sequence searches much faster.

Project Impact

Increased Efficiency: The client now experiences much faster sequence search results, saving time and improving productivity.

Enhanced User Experience: Researchers and bioinformaticians can now process queries much more quickly without sacrificing accuracy, making the tool more practical for daily use.

Scalability: The new implementation is scalable, supporting more complex and larger datasets with improved speed.

Ready To Get Started

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.