As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.

https://dl.acm.org/doi/pdf/10.1145/2788396

A Survey of CPU-GPU Heterogeneous Computing Techniques

Molecular Biologists frequently compute multiple sequence alignments (MSAs) to identify similar regions in protein families. However, aligning hundreds of sequences by popular MSA tools such as ClustalW requires several hours on sequential computers. Due to the rapid growth of biological sequence databases biologists have to compute MSAs in a far shorter time. In this paper we present a new approach to reduce this runtime using graphics processing units (GPUs). To derive an efficient mapping onto this type of architecture, we have reformulated the computationally most expensive part of ClustalW in terms of computer graphics primitives. This results in a high-speed implementation with significant runtime savings on a commodity graphics card.

GPU-Clustalw : Using graphics hardware to accelerate multiple-sequence alignment

The rapid advances in sequencing technology have led to an explosion of sequence data. Sequence alignment is the central and fundamental problem in many sequence analysis procedure, while local alignment is often the kernel of these algorithms. Usually, Smith–Waterman algorithm is used to find the best subsequence match between given sequences. However, the high time complexity makes the algorithm time-consuming. A lot of approaches have been developed to accelerate and parallelize it, such as vector-level parallelization, thread-level parallelization, process-level parallelization, and heterogeneous acceleration, but the current researches seem unsystematic, which hinders the further research of parallelizing the algorithm. In this paper, we summarize the current research status of parallel local alignments and describe the data layout in these work. Based on the research status, we emphasize large-scale genomic comparisons. By surveying some typical alignment tools’ performance, we discuss some possible directions in the future. We hope our work will provide the developers of the alignment tool with technical principle support, and help researchers choose proper alignment tools.

/pdf/a-review-of-parallel-implementations-for-the-smith-waterman-1209954783.pdf

A Review of Parallel Implementations for the Smith-Waterman Algorithm.

Biological pairwise sequence alignment can be used as a method for arranging two biological sequence characters to identify regions of similarity. This operation has elicited considerable interest due to its significant influence on various critical aspects of life (e.g., identifying mutations in coronaviruses). Sequence alignment over large databases cannot yield results within a reasonable time, power, and cost. heuristic methods, such as FASTA, the BLAST family have been demonstrated to perform 40 times faster than DP-based (e.g., Needleman–Wunsch) techniques they cannot guarantee an optimum alignment result An optimized software platform of a widely used DNA sequence alignment algorithm called the Needleman–Wunsch (NW) algorithm based on a lookup table, is described in this study. This global alignment algorithm is the best approach for identifying similar regions between sequences. This study presents a new application of classical machine learning (ML) to global sequence alignment. Customized ML models are used to implement NW global alignment. An accuracy of 99.7% is achieved when using a multilayer perceptron with the ADAM optimizer, and up to 2912 Giga cell updates per second are realized on two real DNA sequences with a length of 4.1 M nucleotides. Our implementation is valid for RNA/DNA sequences. This study aims to parallelize the computation steps involved in the algorithm to accelerate its performance by using ML algorithms. All datasets used in this study are available from https://ieee-dataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm .

/pdf/sequence-alignment-using-machine-learning-based-needleman-3zr4x76eb8.pdf

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

present a novel implementation of the pairwise DNA sequence alignment problem other than the Dynamic programming solution presented by Smith Waterman Algorithm. The proposed implementation uses CUDA; the parallel computing platform and programming model invented by NVIDIA. The main idea of the proposed implementation is assigning different nucleotide weights then merging the sub- sequences of match using the GPU Architecture according to predefined rules to get the optimum local alignment. We parallelize the whole solution for the pairwise DNA sequence alignment using CUDA and compare the results against a similar semi-parallelized solution and a traditional Smith- Waterman implementation on traditional processors; Experimental results demonstrate a considerable reduction in the running time.

/pdf/accelerating-pairwise-dna-sequence-alignment-using-the-cuda-uyzhdxpvyb.pdf

Accelerating Pairwise DNA Sequence Alignment using the CUDA Compatible GPU

Smith-Waterman algorithm is a classic dynamic programming algorithm to solve the problem of biological sequence alignment However, with the rapid increment of the number of DNA and protein sequences, the originally sequential algorithm is very time consuming due to there existing the same computing task computed repeatedly on large-scale data Today's GPU (graphics processor unit) consists of hundreds of processors, so it has a more powerful computation capacity than the current multicore CPU And as the programmability of GPU improved continuously, using it to do generous purpose computing is becoming very popular In order to accelerate sequence alignment, previous researchers use the parallelism of the anti-diagonal of similarity matrix to parallelize the Smith-Waterman algorithm on GPU In this paper, we design a new parallel algorithm which exploits the parallelism of the column of similarity matrix to parallelize the Smith-Waterman algorithm on a heterogeneous system based on CPU and GPU The experiment result shows that our new parallel algorithm is more efficient than that of previous, which takes full advantage of the features of both the CPU and GPU and obtains approximately 37 times speedup compared with the sequential algorithm named OSEARCH implemented on Intel dual-core E2140 processor.

A new parallel method of smith-waterman algorithm on a heterogeneous platform

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A new parallel method of smith-waterman algorithm on a heterogeneous platform