The parallelization of Smith-Waterman (SW) sequence comparison tools for long DNA sequences has been a big challenge over the years, requesting the use of several devices and sophisticated optimizations. Pruning is one of these optimizations, which can reduce considerably the amount of computation. This article proposes MultiBP, a sequence comparison solution in multiple GPUs with block pruning. Two MultiBP strategies are proposed. In static score-sharing, workload is statically distributed to the GPUs, and the best score is sent to neighbor GPUs to simulate a global view. In the dynamic strategy, execution is divided into cycles and workload is dynamically assigned, according to the GPUs processing rate. MultiBP was integrated to MASA-CUDAlign and tested in homogeneous and heterogeneous platforms, with different NVidia GPU architectures. The best results in our homogeneous and heterogeneous platforms were mostly obtained by the static and dynamic approaches, respectively. We also show that our decision module is able to select the best strategy in most cases. Finally, the comparison of the human and chimpanzee chromosomes 1 in a cluster with 512 V100 NVidia GPUs took 11 minutes and obtained the impressive rate of 82,822 GCUPS (Billions of Cells Updated per Second) which is, to our knowledge, the best performance for SW tools in GPUs.

Sequence comparison is a task performed in several Bioinformatics applications daily all over the world. Algorithms that retrieve the optimal result have quadratic time complexity, requiring a huge amount of computing power when the sequences compared are long. In order to reduce the execution time, many parallel solutions have been proposed in the literature. Nevertheless, depending on the sizes of the sequences, even those parallel solutions take hours or days to complete. Pruning techniques can significantly improve the performance of the parallel solutions and a few approaches have been proposed to provide pruning capabilities for sequence comparison applications. This paper proposes and evaluates a variant of the block pruning approach that runs in multiple GPUs, in homogeneous or heterogeneous environments. Experimental results obtained with DNA sequences in two testbeds show that significant performance gains are obtained with pruning, compared to its non-pruning counterpart, achieving the impressive performance of 694.8 GCUPS (Billions of Cells Updated per Second) for four GPUs.

Summary Biological sequence comparison is often used as an auxiliary task in the analysis of genetic material. Pairwise comparison algorithms like Smith‐Waterman evaluate two strings representing sequences of proteins, DNA or RNA to obtain optimal alignment between them. Many applications have been proposed to address the sequence comparison problem, prioritizing the use of graphics cards and proprietary languages such as CUDA. In this paper, we propose and evaluate MASA‐OpenCL, an OpenCL solution for comparing long DNA sequences that is based on the MASA sequence alignment framework, with pruning capability proportional to the similarity of the sequences compared. The results of MASA‐OpenCL were compared to its CUDA counterpart (MASA‐CUDAlign) and, in most cases, MASA‐OpenCL achieved better performance. In order to better understand the behavior of MASA‐OpenCL, we performed a statistical analysis considering 11 comparisons of sequences with high, medium and low similarity in 4 GPUs. As a result, we obtained a multiple linear regression model that considers (a) the sizes of the sequences, (b) the similarity between them, (c) the computational power of the GPU, and (d) the GPU memory bandwidth. We used this model to predict the performance in two other GPUs, with low error rates.

BACKGROUND: 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. RESULTS: 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%. CONCLUSIONS: 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.

This is a pre-copyedited, author-produced version of an article accepted for publication in The Computer Journal following peer review. The version of record Edans F O Sandes, George L M Teodoro, Maria Emilia M T Walter, Xavier Martorell, Eduard Ayguade, Alba C M A Melo; Formalization of Block Pruning: Reducing the Number of Cells Computed in Exact Biological Sequence Comparison Algorithms, The Computer Journal, Volume 61, Issue 5, 1 May 2018, Pages 687–713 is available online at: The Computer Journal https://academic.oup.com/comjnl/article-abstract/61/5/687/4539903 and https://doi.org/10.1093/comjnl/bxx090.

This paper proposes and evaluates CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA sequences in multi-GPU platforms, using the exact Smith–Waterman (SW) algorithm. In the first phase of CUDAlign 4.0, a huge Dynamic Programming (DP) matrix is computed by multiple GPUs, which asynchronously communicate border elements to the right neighbor in order to find the optimal score. After that, the traceback phase of SW is executed. The efficient parallelization of the traceback phase is very challenging because of the high amount of data dependency, which particularly impacts the performance and limits the application scalability. In order to obtain a multi-GPU highly parallel traceback phase, we propose and evaluate a new parallel traceback algorithm called Incremental Speculative Traceback (IST), which pipelines the traceback phase, speculating incrementally over the values calculated so far, producing results in advance. With CUDAlign 4.0, we were able to calculate SW matrices with up to 60 Peta cells, obtaining the optimal local alignments of all Human and Chimpanzee homologous chromosomes, whose sizes range from 26 Millions of Base Pairs (MBP) up to 249 MBP. As far as we know, this is the first time such comparison was made with the SW exact method. We also show that the IST algorithm is able to reduce the traceback time from 2.15$\times$up to 21.03$\times$, when compared with the baseline traceback algorithm. The human$\times$chimpanzee chromosome 5 comparison (180 MBP$\times$183 MBP) attained 10,370.00 GCUPS (Billions of Cells Updated per Second) using 384 GPUs, with a speculation hit ratio of 98.2 percent.

Biological sequence comparison is a very common task in Bioinformatics applications. Many parallel solutions have been proposed for this problem, using different HPC platforms, programmed usually with platform-specific languages and frameworks. With this approach, it is difficult to port solutions among different platforms such as CPUs and GPUs, for instance. To tackle this problem, this paper proposes and evaluates an OpenCL parallel solution for Biological Sequence Comparison, which was integrated to the CUDAlign Megabase Sequence Comparison tool. The evaluation of our solution shows we were able to obtain a program for CPUs and GPUs (NVidia and AMD) with basically the same OpenCL code. In addition, in the comparison with SW# and CUDAlign optimized CUDA codes, we show that the performance of our OpenCL version has comparable and, many times, superior performance.

This paper proposes and evaluates a parallel strategy to execute the exact Smith-Waterman (SW) biological sequence comparison algorithm for huge DNA sequences in multi-GPU platforms. In our strategy, the computation of a single huge SW matrix is spread over multiple GPUs, which communicate border elements to the neighbour, using a circular buffer mechanism. We also provide a method to predict the execution time and speedup of a comparison, given the number of the GPUs and the sizes of the sequences. The results obtained with a large multi-GPU environment show that our solution is scalable when varying the sizes of the sequences and/or the number of GPUs and that our prediction method is accurate. With our proposal, we were able to compare the largest human chromosome with its homologous chimpanzee chromosome (249 Millions of Base Pairs (MBP) x 228 MBP) using 64 GPUs, achieving 1.7 TCUPS (Tera Cells Updated per Second). As far as we know, this is the largest comparison ever done using the Smith-Waterman algorithm.

This paper proposes and evaluates a parallel strategy to execute the exact Smith-Waterman (SW) algorithm for megabase DNA sequences in heterogeneous multi-GPU platforms. In our strategy, the computation of a single huge SW matrix is spread over multiple GPUs, which communicate border elements to the neighbour, using a circular buffer mechanism that hides the communication overhead. We compared 4 pairs of human-chimpanzee homologous chromosomes using 2 different GPU environments, obtaining a performance of up to 140.36 GCUPS (Billion of cells processed per second) with 3 heterogeneous GPUS.

Online social networks (OSNs) offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities: #musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.

In Genome Projects, biological sequences are aligned thousands of times, in a daily basis. The Smith-Waterman algorithm is able to retrieve the optimal local alignment with quadratic time and space complexity. So far, aligning huge sequences, such as whole chromosomes, with the Smith-Waterman algorithm has been regarded as unfeasible, due to huge computing and memory requirements. However, high-performance computing platforms such as GPUs are making it possible to obtain the optimal result for huge sequences in reasonable time. In this paper, we propose and evaluate CUDAlign 2.1, a parallel algorithm that uses GPU to align huge sequences, executing the Smith-Waterman algorithm combined with Myers-Miller, with linear space complexity. In order to achieve that, we propose optimizations which are able to reduce significantly the amount of data processed, while enforcing full parallelism most of the time. Using the NVIDIA GTX 560 Ti board and comparing real DNA sequences that range from 162 KBP (Thousand Base Pairs) to 59 MBP (Million Base Pairs), we show that CUDAlign 2.1 is scalable. Also, we show that CUDAlign 2.1 is able to produce the optimal alignment between the chimpanzee chromosome 22 (33 MBP) and the human chromosome 21 (47 MBP) in 8.4 hours and the optimal alignment between the chimpanzee chromosome Y (24 MBP) and the human chromosome Y (59 MBP) in 13.1 hours.

Cross-species chromosome alignments can reveal ancestral relationships and may be used to identify the peculiarities of the species. It is thus an important problem in Bioinformatics. So far, aligning huge sequences, such as whole chromosomes, with exact methods has been regarded as unfeasible, due to huge computing and memory requirements. However, high performance computing platforms such as GPUs are being able to change this scenario, making it possible to obtain the exact result for huge sequences in reasonable time. In this paper, we propose and evaluate a parallel algorithm that uses GPU to align huge sequences, executing the Smith-Waterman algorithm combined with Myers-Miller, with linear space complexity. In order to achieve that, we propose optimizations that are able to reduce significantly the amount of data processed and that enforce full parallelism most of the time. Using the GTX 285 Board, our algorithm was able to produce the optimal alignment between sequences composed of 33 Millions of Base Pairs (MBP) and 47 MBP in 18.5 hours.

Biological sequence comparison is a very important operation in Bioinformatics. Even though there do exist exact methods to compare biological sequences, these methods are often neglected due to their quadratic time and space complexity. In order to accelerate these methods, many GPU algorithms were proposed in the literature. Nevertheless, all of them restrict the size of the smallest sequence in such a way that Megabase genome comparison is prevented. In this paper, we propose and evaluate CUDAlign, a GPU algorithm that is able to compare Megabase biological sequences with an exact Smith-Waterman affine gap variant. CUDAlign was implemented in CUDA and tested in two GPU boards, separately. For real sequences whose size range from 1MBP (Megabase Pairs) to 47MBP, a close to uniform GCUPS (Giga Cells Updates per Second) was obtained, showing the potential scalability of our approach. Also, CUDAlign was able to compare the human chromosome 21 and the chimpanzee chromosome 22. This operation took 21 hours on GeForce GTX 280, resulting in a peak performance of 20.375 GCUPS. As far as we know, this is the first time such huge chromosomes are compared with an exact method.