The Needleman-Wunsch (NW) is a dynamic programming algorithm used in the pairwise global alignment of two biological sequences. In this paper, three sets of parallel implementations of the NW algorithm are presented using a mixture of specialized software and hardware solutions: POSIX Threads-based, SIMD Extensions-based and a GPU-based implementations. The three implementations aim at improving the performance of the NW algorithm on large scale input without affecting its accuracy. Our experiments show that the GPU-based implementation is the best implementation as it achieves performance 72.5X faster than the sequential implementation, whereas the best performance achieved by the POSIX threads and the SIMD techniques are 2X and 18.2X faster than the sequential implementation, respectively.

Improving the performance of the needleman-wunsch algorithm using parallelization and vectorization techniques

We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method. Our study includes acceleration factors, performance, scalability, power efficiency and energy costs. We also quantify the influence of the contents of the compared sequences, identify potential scenarios for energy savings on speculative executions, and calculate performance and energy usage differences among distinct GPU generations and models. For a sequence alignment on chromosome-wide scale (around 2 Petacells), we are able to reduce execution times from 9.5 h on a Kepler GPU to just 2.5 h on a Pascal counterpart, with energy costs cut by 60%. We find GPUs to be an order of magnitude ahead in performance per watt compared to Xeon Phis. Finally, versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.

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DNA sequences alignment in multi-GPUs: acceleration and energy payoff

The experience of the COVID-19 pandemic, which has accelerated many chaotic processes in modern society, has highlighted in a very serious and urgent way the need to understand complex processes in order to achieve the common well-being. Modern High performance computing technologies, Quantum Computing, Computational Intelligence are shown to be extremely efficient and useful in safeguarding the fate of mankind. These technologies are the state of the art of IT evolution and are fundamental to be competitive and efficient today. If a company is familiar with these techniques and technologies, will be able to deal with any unexpected and complicated scenario more efficiently and effectively. The main contribution of our work is a set of best practices and case studies that can help the researcher address computationally complex problems. We offer a range of software technologies, from high performance computing to machine learning and quantum computing, which represent today the state of the art to deal with extremely complex computational issues, driven by chaotic events and not easily predictable. In this chapter we analyse the different technologies and applications that will lead mankind to overcome this difficult moment, as well as to understand more and more deeply the profound aspects of very complex phenomena. In this environment of rising complexity, both in terms of technology, algorithms, and changing lifestyles, it is critical to emphasize the importance of achieving maximum efficiency and outcomes while protecting the integrity of everyone's personal data and respecting the human being as a whole. 

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High-performance computing and computational intelligence applications with a multi-chaos perspective

Bioinformatics is a growing field that attracts many researchers and continues to prove its value and significance. Since the early days of discovering genomic martial and using it to identify new life forms, sequence alignment applications have become important in enabling discoveries of important biological or medical benefits. Finding similarities, or even relations between sequences, is a demanding process that requires time and high cost. However, nowadays there are plenty of algorithms that are used to find similarity and/or differences between sequences. Many of these algorithms still suffer from performance issues, such as slow performance and poor scalability. Therefore, parallelization is widely used to address these issues. In this paper, we utilize a multi-threading parallelism technique coupled with a block alignment idea in order to improve the sequence alignment performance. The experiments show that the proposed implementation outperforms the sequential implementation by 4.9 times for sequences of lengths ranging between 1024 and 8192.

Fast exact sequence alignment using parallel computing

We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method. Speed-up factors and energy consumption are monitored on different stages of the algorithm with the goal of identifying advantageous scenarios to maximize acceleration and minimize power consumption. Experimental results using CUDA on a set of GeForce GTX 980 GPUs illustrate their capabilities as high-performance and low-power devices, with a energy cost to be more attractive when increasing the number of GPUs. Overall, our results demonstrate a good correlation between the performance attained and the extra energy required, even in scenarios where multi-GPUs do not show great scalability.

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Jesús Pérez Serrano

Papers

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis