Fpgas in Bioinformatics
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Efficient Implementation of an Optimized Attack on a Reconfigurable Hardware Cluster
Breaking ecc2-113: Efficient Implementation of an Optimized Attack on a Reconfigurable Hardware Cluster Susanne Engels Master’s Thesis. February 22, 2014. Chair for Embedded Security – Prof. Dr.-Ing. Christof Paar Advisor: Ralf Zimmermann EMSEC Abstract Elliptic curves have become widespread in cryptographic applications since they offer the same cryptographic functionality as public-key cryptosystems designed over integer rings while needing a much shorter bitlength. The resulting speedup in computation as well as the smaller storage needed for the keys, are reasons to favor elliptic curves. Nowadays, elliptic curves are employed in scenarios which affect the majority of people, such as protecting sensitive data on passports or securing the network communication used, for example, in online banking applications. This works analyzes the security of elliptic curves by practically attacking the very basis of its mathematical security — the Elliptic Curve Discrete Logarithm Problem (ECDLP) — of a binary field curve with a bitlength of 113. As our implementation platform, we choose the RIVYERA hardware consisting of multiple Field Programmable Gate Arrays (FPGAs) which will be united in order to perform the strongest attack known in literature to defeat generic curves: the parallel Pollard’s rho algorithm. Each FPGA will individually perform a what is called additive random walk until two of the walks collide, enabling us to recover the solution of the ECDLP in practice. We detail on our optimized VHDL implementation of dedicated parallel Pollard’s rho processing units with which we equip the individual FPGAs of our hardware cluster. The basic design criterion is to build a compact implementation where the amount of idling units — which deplete resources of the FPGA but contribute in only a fraction of the computations — is reduced to a minimum. -
Current Status and Future Perspectives of Bioinformatics in Tanzania
CURRENT STATUS AND FUTURE PERSPECTIVES OF BIOINFORMATICS IN TANZANIA Sylvester L. Lyantagaye Department of Molecular Biology and Biotechnology, College of Natural and Applied Sciences, University of Dar es Salaam, P.O. Box 35179, Dar es Salaam, Tanzania E-mail: [email protected], [email protected] ABSTRACT The main bottleneck in advancing genomics in present times is the lack of expertise in using bioinformatics tools and approaches for data mining in raw DNA sequences generated by modern high throughput technologies such as next generation sequencing. Although bioinformatics has been making major progress and contributing to the development in the rest of the world, it has still not yet fully integrated the tertiary education and research sector in Tanzania. This review aims to introduce a summary of recent achievements, trends and success stories of application of bioinformatics in biotechnology. The applications of bioinformatics in the fields such as molecular biology, biotechnology, medicine and agriculture, the global trend of bioinformatics, accessibility bioinformatics products in Tanzania, bioinformatics training initiatives in Tanzania, the future prospects of bioinformatics use in biotechnology globally and Tanzania in particular are reviewed. The paper is of interest and importance to rouse public awareness of the new opportunities that could be brought about by bioinformatics to address many research problems relevant to Tanzania and sub-Sahara Africa. Keywords: Bioinformatics, Biotechnology, Genomics, Tanzania. INTRODUCTION analysis, and management of biological Bioinformatics is conceptualising biology in information through computational terms of molecules (in the sense of physical techniques. It uses mathematics, biology, chemistry) and applying "informatics and computer science to understand the techniques" (derived from disciplines such biological importance of an extensive as applied mathematics, computer science variety of omics data (Reportlinker 2013). -
High Performance Computing Zur Technischen Finanzmarktanalyse
High Performance Computing zur technischen Finanzmarktanalyse Christoph Starke Dissertation zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften (Dr.-Ing.) der Technischen Fakultät der Christian-Albrechts-Universität zu Kiel eingereicht im Jahr 2012 1. Gutachter: Prof. Dr. Manfred Schimmler Christian-Albrechts-Universität zu Kiel 2. Gutachter: Prof. Dr. Andreas Speck Christian-Albrechts-Universität zu Kiel Datum der mündlichen Prüfung: 24.9.2012 ii Zusammenfassung Auf Grundlagen der technischen Finanzmarktanalyse wird ein Algorith- mus für eine sicherheitsorientierte Wertpapierhandelsstrategie entwickelt. Maßgeblich für den Erfolg der Handelsstrategie ist dabei eine mög- lichst optimale Gewichtung mehrerer Indikatoren. Die Ermittlung dieser Gewichte erfolgt in einer sogenannten Kalibrierungsphase, die extrem rechenintensiv ist. Bei einer direkten Implementierung auf einem herkömmlichen High Performance PC würde diese Kalibrierungsphase zigtausend Jahre dauern. Deshalb wird eine parallele Version des Algorithmus entwickelt, die hervorragend für die massiv parallele, FPGA-basierte Rechnerarchitektur der RIVYERA geeignet ist, die am Lehrstuhl für technische Infor- matik der Christian-Albrechts-Universität zu Kiel entwickelt wurde. Durch mathematisch äquivalente Transformationen und Optimierungs- schritte aus verschiedenen Bereichen der Informatik gelingt eine FPGA- Implementierung mit einer im Vergleich zu dem PC mehr als 22.600-fach höheren Performance. Darauf aufbauend wird durch die zusätzliche Ent- wicklung eines -
High-Performance Reconfigurable Computing
International Journal of Reconfigurable Computing High-Performance Reconfigurable Computing Guest Editors: Khaled Benkrid, Esam El-Araby, Miaoqing Huang, Kentaro Sano, and Thomas Steinke High-Performance Reconfigurable Computing International Journal of Reconfigurable Computing High-Performance Reconfigurable Computing Guest Editors: Khaled Benkrid, Esam El-Araby, Miaoqing Huang, Kentaro Sano, and Thomas Steinke Copyright © 2012 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “International Journal of Reconfigurable Computing.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro- vided the original work is properly cited. Editorial Board Cristinel Ababei, USA Paris Kitsos, Greece Mario Porrmann, Germany Neil Bergmann, Australia Chidamber Kulkarni, USA Viktor K. Prasanna, USA K. L. M. Bertels, The Netherlands Miriam Leeser, USA Leonardo Reyneri, Italy Christophe Bobda, Germany Guy Lemieux, Canada Teresa Riesgo, Spain Miodrag Bolic, Canada Heitor Silverio Lopes, Brazil Marco D. Santambrogio, USA Joao˜ Cardoso, Portugal Martin Margala, USA Ron Sass, USA Paul Chow, Canada Liam Marnane, Ireland Patrick R. Schaumont, USA Rene´ Cumplido, Mexico Eduardo Marques, Brazil Andrzej Sluzek, Singapore Aravind Dasu, USA Maire´ McLoone, UK Walter Stechele, Germany Claudia Feregrino, Mexico Seda Ogrenci Memik, USA Todor Stefanov, The Netherlands Andres D. Garcia, Mexico Gokhan Memik, USA Gregory Steffan, Canada Soheil Ghiasi, USA Daniel Mozos, Spain Gustavo Sutter, Spain Diana Gohringer,¨ Germany Nadia Nedjah, Brazil Lionel Torres, France Reiner Hartenstein, Germany Nik Rumzi Nik Idris, Malaysia Jim Torresen, Norway Scott Hauck, USA JoseNu´ nez-Ya˜ nez,˜ UK W. Vanderbauwhede, UK Michael Hubner,¨ Germany Fernando Pardo, Spain Mus¨¸tak E. -
White Paper on CLC Read Mapper
White Paper White paper on CLC read mapper October 10, 2012 Sample to Insight QIAGEN Aarhus Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone: +45 70 22 32 44 www.qiagenbioinformatics.com [email protected] White paper: White paper on CLC read mapper 2 Contents 1 Abstract 3 2 Introduction 3 3 Performance Benchmark of CLC Genomics Workbench/Server 5 4 Computational Resource Requirements of CLC Assembly Cell 9 5 Accuracy Benchmark of CLC Assembly Cell 12 6 Algorithmic Details 18 7 Conclusions 19 8 Materials & Methods 19 8.1 Performance Benchmark of CLC Genomics Workbench/Server ........... 19 8.2 Computational Resource Requirements of CLC Assembly Cell ............ 20 8.3 Accuracy Benchmark of CLC Assembly Cell ...................... 20 8.4 Read Mappers ..................................... 22 8.5 Data Sets ....................................... 22 8.6 Benchmarking Hardware ............................... 23 8.7 bash related ...................................... 23 References 25 White paper: White paper on CLC read mapper 3 1 Abstract After decades of biological research relying on Sanger sequencing, massively parallel high throughput sequencing (HTS) techniques have created a broad range of new and exciting research applications by increasing the output sequencing data dramatically. Although allowing for hitherto unseen DNA and RNA sequencing and binding study designs, HTS technologies have created remarkable bioinformatic challenges. In typical resequencing studies read mappers are used to align sequence reads to reference genomes. Read mapping, while not too time consuming in the Sanger-century, quickly evolved into one of the most insistent bottlenecks. Nowadays, researchers can resort to a broad range of read mapping solutions and it becomes increasingly challenging to choose software that optimally meets given requirements. -
A Hybrid-Parallel Architecture for Applications in Bioinformatics
A Hybrid-parallel Architecture for Applications in Bioinformatics M.Sc. Jan Christian Kässens Dissertation zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften (Dr.-Ing.) der Technischen Fakultät der Christian-Albrechts-Universität zu Kiel eingereicht im Jahr 2017 Kiel Computer Science Series (KCSS) 2017/4 dated 2017-11-08 URN:NBN urn:nbn:de:gbv:8:1-zs-00000335-a3 ISSN 2193-6781 (print version) ISSN 2194-6639 (electronic version) Electronic version, updates, errata available via https://www.informatik.uni-kiel.de/kcss The author can be contacted via [email protected] Published by the Department of Computer Science, Kiel University Computer Engineering Group Please cite as: Ź Jan Christian Kässens. A Hybrid-parallel Architecture for Applications in Bioinformatics Num- ber 2017/4 in Kiel Computer Science Series. Department of Computer Science, 2017. Dissertation, Faculty of Engineering, Kiel University. @book{Kaessens17, author = {Jan Christian K\"assens}, title = {A Hybrid-parallel Architecture for Applications in Bioinformatics}, publisher = {Department of Computer Science, CAU Kiel}, year = {2017}, number = {2017/4}, doi = {10.21941/kcss/2017/4}, series = {Kiel Computer Science Series}, note = {Dissertation, Faculty of Engineering, Kiel University.} } © 2017 by Jan Christian Kässens ii About this Series The Kiel Computer Science Series (KCSS) covers dissertations, habilitation theses, lecture notes, textbooks, surveys, collections, handbooks, etc. written at the Department of Computer Science at Kiel University. It was initiated in 2011 to support authors in the dissemination of their work in electronic and printed form, without restricting their rights to their work. The series provides a unified appearance and aims at high-quality typography. The KCSS is an open access series; all series titles are electronically available free of charge at the department’s website. -
An Efficient VHDL Description and Hardware Implementation of The
An Efficient VHDL Description and Hardware Implementation of the Triple DES Algorithm A thesis submitted to the Graduate School of the University of Cincinnati In partial fulfillment of the requirements for the degree of Master of Science In the Department of Electrical and Computer Engineering Of the College of Engineering and Applied Sciences June 2014 By Lathika SriDatha Namburi B.Tech, Electronics and Communications Engineering, Jawaharlal Nehru Technological University, Hyderabad, India, 2011 Thesis Advisor and Committee Chair: Dr. Carla Purdy ABSTRACT Data transfer is becoming more and more essential these days with applications ranging from everyday social networking to important banking transactions. The data that is being sent or received shouldn’t be in its original form but must be coded to avoid the risk of eavesdropping. A number of algorithms to encrypt and decrypt the data are available depending on the level of security to be achieved. Many of these algorithms require special hardware which makes them expensive for applications which require a low to medium level of data security. FPGAs are a cost effective way to implement such algorithms. We briefly survey several encryption/decryption algorithms and then focus on one of these, the Triple DES. This algorithm is currently used in the electronic payment industry as well as in applications such as Microsoft OneNote, Microsoft Outlook and Microsoft system center configuration manager to password protect user content and data. We implement the algorithm in a Hardware Description Language, specifically VHDL and deploy it on an Altera DE1 board which uses a NIOS II soft core processor. The algorithm takes input encoded using a software based Huffman encoding to reduce its redundancy and compress the data. -
Secure Volunteer Computing for Distributed Cryptanalysis
ysis SecureVolunteer Computing for Distributed Cryptanal Nils Kopal Secure Volunteer Computing for Distributed Cryptanalysis ISBN 978-3-7376-0426-0 kassel university 9 783737 604260 Nils Kopal press kassel university press ! "# $ %& & &'& #( )&&*+ , #()&- ( ./0 12.3 - 4 # 5 (!!&& & 6&( 7"#&7&12./ 5 -839,:,3:3/,2;1/,2% ' 5 -839,:,3:3/,2;13,3% ,' 05( (!!<& &!.2&.81..!")839:3:3/2;133 "=( (!!, #& !(( (2221,;2;13/ '12.97 # ?@7 & &, & ) ? “With magic, you can turn a frog into a prince. With science, you can turn a frog into a Ph.D. and you still have the frog you started with.” Terry Pratchett Abstract Volunteer computing offers researchers and developers the possibility to distribute their huge computational jobs to the computers of volunteers. So, most of the overall cost for computational power and maintenance is spread across the volunteers. This makes it possible to gain computing resources that otherwise only expensive grids, clusters, or supercomputers offer. Most volunteer computing solutions are based on a client-server model. The server manages the distribution of subjobs to the computers of volunteers, the clients, which in turn compute the subjobs and return the results to the server. The Berkeley Open Infrastructure for Network Computing (BOINC) is the most used middleware for volunteer computing. A drawback of any client-server architecture is the server being the single point of control and failure. To get rid of the single point of failure, we developed different distribution algorithms (epoch distribution algorithm, sliding-window distribution algorithm, and extended epoch distribution algorithm) based on unstructured peer-to-peer networks. These algorithms enable the researchers and developers to create volunteer computing networks without any central server. -
Solving the Discrete Logarithm of a 113-Bit Koblitz Curve with an FPGA Cluster
Solving the Discrete Logarithm of a 113-bit Koblitz Curve with an FPGA Cluster Erich Wenger and Paul Wolfger Graz University of Technology Institute for Applied Information Processing and Communications Inffeldgasse 16a, 8010 Graz, Austria [email protected], [email protected] Abstract. Using FPGAs to compute the discrete logarithms of elliptic curves is a well-known method. However, until to date only CPU clus- ters succeeded in computing new elliptic curve discrete logarithm records. This work presents a high-speed FPGA implementation that was used to compute the discrete logarithm of a 113-bit Koblitz curve. The core of the design is a fully unrolled, highly pipelined, self-sufficient Pollard's rho iteration function. An 18-core Virtex-6 FPGA cluster computed the discrete logarithm of a 113-bit Koblitz curve in extrapolated 24 days. Until to date, no attack on such a large Koblitz curve succeeded using as little resources or in such a short time frame. Keywords: elliptic curve cryptography, discrete logarithm problem, Koblitz curve, hardware design, FPGA, discrete logarithm record. 1 Introduction It is possible to repeatedly fold a standard letter-sized sheet of paper at the midway point about six to seven times. In 2012, some MIT students [28] were able to fold an 1.2 kilometer long toilet paper 13 times. And every time the paper was folded, the number of layers on top of each other doubled. Therefore, the MIT students ended up with 213 = 8192 layers of paper on top of each other. And poor Eve's job was to manually count all layers one by one. -
A Massively Parallel Architecture for Bioinformatics
A Massively Parallel Architecture for Bioinformatics Gerd Pfeiffer1, Stefan Baumgart1, Jan Schr¨oder2, and Manfred Schimmler2 1 SciEngines GmbH, 24118 Kiel, Germany, WWW home page: http://www.sciengines.com 2 Christian-Albrechts Universit¨at, Department of Computer Science, Hermann-Rodewald-Str. 3, 24118 Kiel, Germany WWW home page: http://www.informatik.uni-kiel.de Abstract. Today’s general purpose computers lack in meeting the re- quirements on computing performance for standard applications in bioin- formatics like DNA sequence alignment, error correction for assembly, or TFBS finding. The size of DNA sequence databases doubles twice a year. On the other hand the advance in computing performance per unit cost only doubles every 2 years. Hence, ingenious approaches have been developed for putting this discrepancy in perspective by use of special purpose computing architectures like ASICs, GPUs, multicore CPUs or CPU Clusters. These approaches suffer either from being too applica- tion specific (ASIC and GPU) or too general (CPU-Cluster and multi- core CPUs). An alternative is the FPGA, which outperforms the solu- tions mentioned above in case of bioinformatic applications with respect to cost and power efficiency, flexibility and communication bandwidths. For making maximal use of the advantages, a new massively parallel architecture consisting of low-cost FPGAs is presented. 1 Introduction Bioinformatics algorithms are most demanding in scientific computing. Most of the times they are not NP-hard or even of high asymptotic complexity but the sheer masses of input data make their computation laborious. Furthermore, the life science thus bioinformatics research field is growing fast and so is the input data that wait to be processed. -
Buying in to Bioinformatics: an Introduction to Commercial Sequence Analysis Software David Roy Smith
BRIEFINGS IN BIOINFORMATICS. VOL 16. NO 4. 700 ^709 doi:10.1093/bib/bbu030 Advance Access published on 1 September 2014 Buying in to bioinformatics: an introduction to commercial sequence analysis software David Roy Smith Submitted: 25th June 2014; Received (in revised form) : 7th August 2014 Abstract Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bio- informatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right soft- ware for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, fea- tures and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software -
CLC Sequence Viewer
CLC Sequence Viewer USER MANUAL Manual for CLC Sequence Viewer 8.0.0 Windows, macOS and Linux June 1, 2018 This software is for research purposes only. QIAGEN Aarhus Silkeborgvej 2 Prismet DK-8000 Aarhus C Denmark Contents I Introduction7 1 Introduction to CLC Sequence Viewer 8 1.1 Contact information.................................9 1.2 Download and installation..............................9 1.3 System requirements................................ 11 1.4 When the program is installed: Getting started................... 12 1.5 Plugins........................................ 13 1.6 Network configuration................................ 16 1.7 Latest improvements................................ 16 II Core Functionalities 18 2 User interface 19 2.1 Navigation Area................................... 20 2.2 View Area....................................... 27 2.3 Zoom and selection in View Area.......................... 35 2.4 Toolbox and Status Bar............................... 37 2.5 Workspace...................................... 40 2.6 List of shortcuts................................... 41 3 User preferences and settings 44 3.1 General preferences................................. 44 3.2 View preferences................................... 46 3.3 Advanced preferences................................ 49 3.4 Export/import of preferences............................ 49 3 CONTENTS 4 3.5 View settings for the Side Panel.......................... 50 4 Printing 52 4.1 Selecting which part of the view to print...................... 53 4.2 Page setup.....................................