Comparative Analysis of Computationally Accelerated NGS Alignment

The Smith-Waterman algorithm is the basis of most current sequence alignment technology, which can be used to identify similarities between sequences for cancer detection and treatment because it provides researchers with potential targets for early diagnosis and personalized treatment. The growing number of DNA and RNA sequences available to analyze necessitates faster alignment processes than are possible with current iterations of the Smith-Waterman (S-W) algorithm. This project aimed to identify the most effective and efficient methods for accelerating the S-W algorithm by investigating recent advances in sequence alignment. Out of a total of 22 articles considered in this project, 17 articles had to be excluded from the study due to lack of standardization of data reporting. Only one study by Chen et al. obtained in this project contained enough information to compare accuracy and alignment speed. When accuracy was excluded from the criteria, five studies contained enough information to rank their efficiency. The study conducted by Rucci et al. was the fastest at 268.83 Giga Cell Updates Per Second (GCUPS), and the method by Pérez-Serrano et al. came close at 229.93 GCUPS while testing larger sequences. It was determined that reporting standards in this field are not sufficient, and the study by Chen et al. should set a benchmark for future reporting

OSWALD: OpenCL Smith–Waterman on Altera’s FPGA for Large Protein Databases

The well-known Smith–Waterman algorithm is a high-sensitivity method for local sequence alignment. Unfortunately, the Smith–Waterman algorithm has quadratic time complexity, which makes it computationally demanding for large protein databases. In this paper, we present OSWALD, a portable, fully functional and general implementation to accelerate Smith–Waterman database searches in heterogeneous platforms based on Altera’s FPGA. OSWALD exploits OpenMP multithreading and SIMD computing through SSE and AVX2 extensions on the host while taking advantage of pipeline and vectorial parallelism by way of OpenCL on the FPGAs. Performance evaluations on two different heterogeneous architectures with real amino acid datasets show that OSWALD is competitive in comparison with other top-performing Smith–Waterman implementations, attaining up to 442 GCUPS peak with the best GCUPS/watts ratio.First published June 30, 2016. Article available in: Vol. 32, Issue 3, 2018.Facultad de Informátic

OSWALD: OpenCL Smith–Waterman on Altera’s FPGA for Large Protein Databases

The well-known Smith–Waterman algorithm is a high-sensitivity method for local sequence alignment. Unfortunately, the Smith–Waterman algorithm has quadratic time complexity, which makes it computationally demanding for large protein databases. In this paper, we present OSWALD, a portable, fully functional and general implementation to accelerate Smith–Waterman database searches in heterogeneous platforms based on Altera’s FPGA. OSWALD exploits OpenMP multithreading and SIMD computing through SSE and AVX2 extensions on the host while taking advantage of pipeline and vectorial parallelism by way of OpenCL on the FPGAs. Performance evaluations on two different heterogeneous architectures with real amino acid datasets show that OSWALD is competitive in comparison with other top-performing Smith–Waterman implementations, attaining up to 442 GCUPS peak with the best GCUPS/watts ratio.First published June 30, 2016. Article available in: Vol. 32, Issue 3, 2018.Facultad de Informátic

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics

An energy‐aware performance analysis of SWIMM: Smith–Waterman implementation on Intel's Multicore and Manycore architectures

Alignment is essential in many areas such as biological, chemical and criminal forensics. The well‐known Smith–Waterman (SW) algorithm is able to retrieve the optimal local alignment with quadratic time and space complexity. There are several implementations that take advantage of computing parallelization, such as manycores, FPGAs or GPUs, in order to reduce the alignment effort. In this research, we adapt, develop and tune the SW algorithm named SWIMM on a heterogeneous platform based on Intel's Xeon and Xeon Phi coprocessor. SWIMM is a free tool available in a public git repository https://github.com/enzorucci/SWIMM. We efficiently exploit data and thread‐level parallelism, reaching up to 380 GCUPS on heterogeneous architecture, 350 GCUPS for the isolated Xeon and 50 GCUPS on Xeon Phi. Despite the heterogeneous implementation obtaining the best performance, it is also the most energy‐demanding. In fact, we also present a trade‐off analysis between performance and power consumption. The greenest configuration is based on an isolated multicore system that exploits AVX2 instruction set architecture reaching 1.5 GCUPS/Watts.Facultad de Informátic

High Performance Computing Algorithms for Accelerating Peptide Identification from Mass-Spectrometry Data Using Heterogeneous Supercomputers

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances due to their inefficient parallelization models, low resource utilization and high overhead costs

Smith-Waterman algorithm on heterogeneous systems: A case study

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. However, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many applications. Some heuristics are possible but at the expense of losing sensitivity. Fortunately, previous research have shown that new computing platforms such as GPUs and FPGAs are able to accelerate SW and achieve impressive speedups. In this paper we have explored SW acceleration on a heterogeneous platform equipped with an Intel Xeon Phi coprocessor. Our evaluation, using the well-known Swiss-Prot database as a benchmark, has shown that a hybrid CPU-Phi heterogeneous system is able to achieve competitive performance (62.6 GCUPS), even with moderate low-level optimisations.Facultad de Informátic

Accelerating the pace of protein functional annotation with intel xeon phi coprocessors

© 2002-2011 IEEE. Intel Xeon Phi is a new addition to the family of powerful parallel accelerators. The range of its potential applications in computationally driven research is broad; however, at present, the repository of scientific codes is still relatively limited. In this study, we describe the development and benchmarking of a parallel version of {\mmb e}FindSite, a structural bioinformatics algorithm for the prediction of ligand-binding sites in proteins. Implemented for the Intel Xeon Phi platform, the parallelization of the structure alignment portion of {\mmb e}FindSite using pragma-based OpenMP brings about the desired performance improvements, which scale well with the number of computing cores. Compared to a serial version, the parallel code runs 11.8 and 10.1 times faster on the CPU and the coprocessor, respectively; when both resources are utilized simultaneously, the speedup is 17.6. For example, ligand-binding predictions for 501 benchmarking proteins are completed in 2.1 hours on a single Stampede node equipped with the Intel Xeon Phi card compared to 3.1 hours without the accelerator and 36.8 hours required by a serial version. In addition to the satisfactory parallel performance, porting existing scientific codes to the Intel Xeon Phi architecture is relatively straightforward with a short development time due to the support of common parallel programming models by the coprocessor. The parallel version of {\mmb e}FindSite is freely available to the academic community at www.brylinski.org/efindsite

Smith-Waterman Protein Search with OpenCL on an FPGA

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. Unfortunately, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many scenarios. Previous research has shown that massively parallel architectures such as GPUs and FPGAs are able to mitigate the computational problems and achieve impressive speedups. In this paper we explore SW acceleration on an FPGA with OpenCL. We efficiently exploit data and thread-level parallelism on an Altera Stratix V FPGA, obtaining up to 39 GCUPS with less than 25 watt of power consumption.Facultad de Informátic