A Massively Parallel Architecture for Bioinformatics Presented by Md Jamiul Jahid.
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Transcript of A Massively Parallel Architecture for Bioinformatics Presented by Md Jamiul Jahid.
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-3 5000 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
Questions
?
Thank You
Reference• Gerd Pfeiffer, Stefan Baumgart, Jan Schröder, and Manfred Schimmler,
A Massively Parallel Architecture for Bioinformatics, 9th International Conference on Computational Science (ICCS 2009).