High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP

This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs

Accelerating pairwise sequence alignment on GPUs using the Wavefront Algorithm

Advances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio, and Nanopore technologies. The recently proposed Wavefront Alignment (WFA) algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. Notwithstanding the advantages of the WFA algorithm, modern high performance computing (HPC) platforms rely on accelerator-based architectures that exploit parallel computing resources to improve over classical computing CPUs. Hence, a GPU-enabled implementation of the WFA could exploit the hardware resources of modern GPUs and further accelerate sequence alignment in current genome analysis pipelines. This thesis presents two GPU-accelerated implementations based on the WFA for fast pairwise DNA sequence alignment: eWFA-GPU and WFA-GPU. Our first proposal, eWFA-GPU, computes the exact edit-distance alignment between two short sequences (up to a few thousand bases), taking full advantage of the massive parallel capabilities of modern GPUs. We propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast on-chip memory of a GPU. Our results show that eWFA-GPU outperforms by 3-9X the edit-distance WFA implementation running on a 20 core machine. Compared to other state-of-the-art tools computing the edit-distance, eWFA-GPU is up to 265X faster than CPU tools and up to 56 times faster than other GPU-enabled implementations. Our second contribution, the WFA-GPU tool, extends the work of eWFA-GPU to compute the exact gap-affine distance (i.e., a more general alignment problem) between arbitrary long sequences. In this work, we propose a CPU-GPU co-design capable of performing inter and intra-sequence parallel alignment of multiple sequences, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original WFA implementation between 1.5-7.7X times when computing the alignment path, and between 2.6-16X when computing only the alignment score. Moreover, compared to other state-of-the-art tools, the WFA-GPU is up to 26.7X faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations

High-throughput Protein Sequence Alignment on Multi-core Systems

Rapid evolution in sequencing technologies results in generating data on an enormous scale. A focal and main challenge in analyzing data at such a large scale is the alignment of the DNA/Protein sequences, whereby reads are compared to the reference sequences. To find similar sequences, alignment algorithms are used to align a query sequence with the database. Alignment algorithms can be utilized to classify the source of a sequence, to discover similarities among the organisms, or to deduce a progenitor connection. A wide range of algorithms for alignment has been developed in recent years.In this paper, an accurate method of accelerating such algorithms using GPUs has been investigated. A Swiss-Prot database has been processed using GPU implemented Smith-Waterman Sequence Alignment Algorithm. The first step in the process generates the alignment scores but not the actual alignment. Various available alignment tools like ssearch2 are then utilized to align the output file generated during the first step.The performance of GPU-accelerated implementation as compared to other techniques is then evaluated for performance /throughput improvement. Swiss-Prot database was aligned using various alignment tools. NVIDIA TESLA K40 GPU is being utilized for generating the results for this research. This implementation achieves the performance of 44.3 Giga cell updates per second (GCUPS), which is 22.9 times better than its implementation on GTX 275. Performance is improved as the workload of sequences of equal length is equally distributed among all the threads on Multiprocessors of GPU

libgapmis: extending short-read alignments

Background: A wide variety of short-read alignment programmes have been published recently to tackle the problem of mapping millions of short reads to a reference genome, focusing on different aspects of the procedure such as time and memory efficiency, sensitivity, and accuracy. These tools allow for a small number of mismatches in the alignment; however, their ability to allow for gaps varies greatly, with many performing poorly or not allowing them at all. The seed-and-extend strategy is applied in most short-read alignment programmes. After aligning a substring of the reference sequence against the high-quality prefix of a short read–the seed–an important problem is to find the best possible alignment between a substring of the reference sequence succeeding and the remaining suffix of low quality of the read–extend. The fact that the reads are rather short and that the gap occurrence frequency observed in various studies is rather low suggest that aligning (parts of) those reads with a single gap is in fact desirable. Results: In this article, we present libgapmis, a library for extending pairwise short-read alignments. Apart from the standard CPU version, it includes ultrafast SSE- and GPU-based implementations. libgapmis is based on an algorithm computing a modified version of the traditional dynamic-programming matrix for sequence alignment. Extensive experimental results demonstrate that the functions of the CPU version provided in this library accelerate the computations by a factor of 20 compared to other programmes. The analogous SSE- and GPU-based implementations accelerate the computations by a factor of 6 and 11, respectively, compared to the CPU version. The library also provides the user the flexibility to split the read into fragments, based on the observed gap occurrence frequency and the length of the read, thereby allowing for a variable, but bounded, number of gaps in the alignment. Conclusions: We present libgapmis, a library for extending pairwise short-read alignments. We show that libgapmis is better-suited and more efficient than existing algorithms for this task. The importance of our contribution is underlined by the fact that the provided functions may be seamlessly integrated into any shortread alignment pipeline. The open-source code of libgapmis is available at http://www.exelixis-lab.org/gapmis

libgapmis: extending short-read alignments

BACKGROUND: A wide variety of short-read alignment programmes have been published recently to tackle the problem of mapping millions of short reads to a reference genome, focusing on different aspects of the procedure such as time and memory efficiency, sensitivity, and accuracy. These tools allow for a small number of mismatches in the alignment; however, their ability to allow for gaps varies greatly, with many performing poorly or not allowing them at all. The seed-and-extend strategy is applied in most short-read alignment programmes. After aligning a substring of the reference sequence against the high-quality prefix of a short read--the seed--an important problem is to find the best possible alignment between a substring of the reference sequence succeeding and the remaining suffix of low quality of the read--extend. The fact that the reads are rather short and that the gap occurrence frequency observed in various studies is rather low suggest that aligning (parts of) those reads with a single gap is in fact desirable. RESULTS: In this article, we present libgapmis, a library for extending pairwise short-read alignments. Apart from the standard CPU version, it includes ultrafast SSE- and GPU-based implementations. libgapmis is based on an algorithm computing a modified version of the traditional dynamic-programming matrix for sequence alignment. Extensive experimental results demonstrate that the functions of the CPU version provided in this library accelerate the computations by a factor of 20 compared to other programmes. The analogous SSE- and GPU-based implementations accelerate the computations by a factor of 6 and 11, respectively, compared to the CPU version. The library also provides the user the flexibility to split the read into fragments, based on the observed gap occurrence frequency and the length of the read, thereby allowing for a variable, but bounded, number of gaps in the alignment. CONCLUSIONS: We present libgapmis, a library for extending pairwise short-read alignments. We show that libgapmis is better-suited and more efficient than existing algorithms for this task. The importance of our contribution is underlined by the fact that the provided functions may be seamlessly integrated into any short-read alignment pipeline. The open-source code of libgapmis is available at http://www.exelixis-lab.org/gapmis

MC64-ClustalWP2: A Highly-Parallel Hybrid Strategy to Align Multiple Sequences in Many-Core Architectures

We have developed the MC64-ClustalWP2 as a new implementation of the Clustal W algorithm, integrating a novel parallelization strategy and significantly increasing the performance when aligning long sequences in architectures with many cores. It must be stressed that in such a process, the detailed analysis of both the software and hardware features and peculiarities is of paramount importance to reveal key points to exploit and optimize the full potential of parallelism in many-core CPU systems. The new parallelization approach has focused into the most time-consuming stages of this algorithm. In particular, the so-called progressive alignment has drastically improved the performance, due to a fine-grained approach where the forward and backward loops were unrolled and parallelized. Another key approach has been the implementation of the new algorithm in a hybrid-computing system, integrating both an Intel Xeon multi-core CPU and a Tilera Tile64 many-core card. A comparison with other Clustal W implementations reveals the high-performance of the new algorithm and strategy in many-core CPU architectures, in a scenario where the sequences to align are relatively long (more than 10 kb) and, hence, a many-core GPU hardware cannot be used. Thus, the MC64-ClustalWP2 runs multiple alignments more than 18x than the original Clustal W algorithm, and more than 7x than the best x86 parallel implementation to date, being publicly available through a web service. Besides, these developments have been deployed in cost-effective personal computers and should be useful for life-science researchers, including the identification of identities and differences for mutation/polymorphism analyses, biodiversity and evolutionary studies and for the development of molecular markers for paternity testing, germplasm management and protection, to assist breeding, illegal traffic control, fraud prevention and for the protection of the intellectual property (identification/traceability), including the protected designation of origin, among other applications

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Background and objectives. The computational biology area is growing up over the years. The interest in researching and developing computational tools for the acquisition, storage, organization, analysis, and visualization of biological data generates the need to create new hardware architectures and new software tools that allow processing big data in acceptable times. In this sense, heterogeneous computing takes an important role in providing solutions but at the same time generates new challenges for developers in relation to the impossibility of porting source code between different architectures. Methods. Intel has recently introduced oneAPI, a new unified programming environment that allows code developed in the SYCL-based Data Parallel C++ (DPC++) language to be run on different devices such as CPUs, GPUs, and FPGAs, among others. Due to the large amount of CUDA software in the field of bioinformatics, this paper presents the migration process of the SW\# suite, a biological sequence alignment tool developed in CUDA, to DPC++ through the oneAPI compatibility tool dpc (recently renowned as SYCLomatic). Results. SW\# has been completely migrated with a small programmer intervention in terms of hand-coding. Moreover, it has been possible to port the migrated code between different architectures (considering different target platforms and vendors), with no noticeable performance degradation. Conclusions. The SYCLomatic tool presented a great performance-portability rate. SYCL and Intel oneAPI can offer attractive opportunities for the Bioinformatics community, especially considering the vast existence of CUDA-based legacy codes