A Massively Parallel Architecture for Bioinformatics

1. A Massively Parallel Architecture for Bioinformatics Presented by MdJamiulJahid

2. Introduction • Bioinformatics algorithms are demanding in scientific computing • In general most of the bioinformatics algorithms are fairly simple • Dealing with huge amount of data • The size of DNA sequence database doubles every year

3. Introduction • A typical DNA contains 3.4 billion base pairs • Maximum algorithms use only simple operations with input data like • Arithmetic operation • String matching • String comparison

4. Introduction • Standard CPUs are designed for providing a good instruction mix for almost all commonly used algorithm • For a target class of algorithm they are not effective • Results • High runtime • Energy • Money

5. Contribution • Present a massively parallel architecture • Using low cost FPGA(Field Programmable Gate Array) • They called it COPACOBANA 5000 • Meaning Cost-Optimized Parallel Code BrakerANdAnalyzer

6. COPACOBANA 1000 • This machine is for cryptanalysis: fast code breaking • 120 low cost FPGAs • 20 subunits • Each has Xilinx Spartan -3 XC3S1000 FPGAs

7. COPACOBANA 1000 • Assumptions • Programs are parallelizable • Demand of data transfer is low • All node needed very little local memory which can be served from on-chip RAM of FPGAs

8. COPACOBANA 5000 • Bus Concepts • Point to point connection two neighboring FPGA-cards • Point to point connection contain 8 pairs of wire • Each 250MHz, total 2Gbit/s

10. COPACOBANA 5000 • Controller • Root entity of control is running on a remote host computer • Connected to COPACOBANA5000 by LAN • Two scenario • Data on remote host • Data on COPACOBANA5000

11. COPACOBANA 5000 • FPGA-Card • Xilinx Spartan-3 5000 is used • Contains 8 FPGAs • All FPGAs are globally clocked

12. Performance Estimation • Between • PC • COPACOBANA1000 • COPACOBANA5000

13. Performance Estimation

14. Conclusion • In this paper a new hardware for running bioinformatics algorithm is proposed • The hardware are • Cheap • Low power consumption • Efficient

15. Questions ?

16. Thank You

17. Reference • Gerd Pfeiffer, Stefan Baumgart, Jan Schröder, and Manfred Schimmler, A Massively Parallel Architecture for Bioinformatics, 9th International Conference on Computational Science (ICCS 2009).