A Massively Parallel Architecture for Bioinformatics Presented by Md Jamiul Jahid

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

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

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

Contribution Present a massively parallel architecture Using low cost FPGA(Field Programmable Gate Array) They called it COPACOBANA 5000 – Meaning Cost-Optimized Parallel Code Braker ANd Analyzer

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

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

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

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

COPACOBANA 5000 FPGA-Card – Xilinx Spartan is used – Contains 8 FPGAs – All FPGAs are globally clocked

Performance Estimation Between – PC – COPACOBANA1000 – COPACOBANA5000

Performance Estimation

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

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