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

Simple scalable nucleotic FPGA based short read aligner for exhaustive search of substitution errors

With the advent of the new and continuously improving technologies, in a couple of years DNA sequencing can be as commonplace as a simple blood test. The growth of sequencing efficiency has a larger exponent than the Moore’s law of standard processors, hence alignment and further processing of sequenced data is the bottleneck. The usage of FPGA (Field Programmable Gate Arrays) technology may provide an efficient alternative. We propose a simple algorithm for DNA sequence alignment, which can be realized efficiently by nucleotic principal agents of Non.Neumann nature. The prototype FPGA implementation runs on a small Terasic DE1-SoC demo board with a Cyclone V chip. We present test results and furthermore analyse the theoretical scalability of this system, showing that the execution time is independent of the length of reference genome sequences. A special advantage of this parallel algorithm is that it performs exhaustive search producing all match variants up to a predetermined number of point (mutation) errors

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists

GPU cards as a low cost solution for efficient and fast classification of high dimensional gene expression datasets

The days when bioinformatics tools will be so reliable to become a standard aid in routine clinical diagnostics are getting very close. However, it is important to remember that the more complex and advanced bioinformatics tools become, the more performances are required by the computing platforms. Unfortunately, the cost of High Performance Computing (HPC) platforms is still prohibitive for both public and private medical practices. Therefore, to promote and facilitate the use of bioinformatics tools it is important to identify low-cost parallel computing solutions. This paper presents a successful experience in using the parallel processing capabilities of Graphical Processing Units (GPU) to speed up classification of gene expression profiles. Results show that using open source CUDA programming libraries allows to obtain a significant increase in performances and therefore to shorten the gap between advanced bioinformatics tools and real medical practic

Аппаратное распознавание строк в интеллектуальных системах защиты информации

При создании интеллектуальных систем противодействия таким угрозам информационной безопасности, как сетевые вторжения, вирусы и спам, необходимо анализировать интенсивный поток данных на наличие одновременно нескольких тысяч эталонных последовательностей символов. Для достижения требуемой производительности часто используют аппаратные решения на базе программируемых интегральных схем. В настоящей работе исследован зарубежный опыт подобных разработок, предложено применение унифицированных изделий.При створенні інтелектуальних систем протидії таким загрозам інформаційній безпеці, як мережні вторгнення, віруси та спам, необхідно аналізувати інтенсивний потік даних на наявність одночасно декількох тисяч еталонних послідовностей символів. Для досягнення необхідної продуктивності часто використовують апаратні рішення на базі програмованих інтегральних схем. У даній роботі досліджений зарубіжний досвід подібних розробок, запропоновано застосування уніфікованих виробів.In order to protect information systems from security threats such as intrusion, virus and spam it is necessary to match all occurrences of a predefined set of string-based patterns containing several thousands of strings. To provide required throughput, the hardware solutions based on programmable logic are widely used. In this paper, the world experiences of such works are investigated and unified solution is proposed