An Investigation into Implementations of DNA Sequence Pattern Matching Algorithms Peden Nichols Computer Systems Research April,

Abstract Purpose:  To investigate the relative practicality of the three main computational methods (supercomputing, clusters, and grid computing) as applications to the problem of DNA sequence pattern matching  To establish data for local unused processor time and the degree of invasiveness of various backgrounding methods, for possible applications to grid computing implementations of BLAST  To quantify the cost to efficiency when adding processors for various BLAST algorithms.

Abstract Methods  Grid Computing MPI (Message Passing Interface) implementations of BLAST algorithms Systems Lab Resources: 40+ networked desktop machines, 16 machines currently mpi-enabled  Backgrounding Algorithms run in the background while computer is in use To what extent do users notice performance change?  Dynamic load balancing Reallocate work as new processors become available/current processors become unavailable

Background Huge amounts of genetic data  Human Genome Project  The Institute for Genomic Research (TIGR) Current computational resources overwhelmed Decoding genome not profitable (yet) Independent/Government organizations don't have the money

Background Two approaches to solution Make the algorithms run faster - mpiBLAST with dynamic load balancing - Port algorithm implementations to clusters, supercomputers Increase utilization of current computational resources - Schools, labs across the country with lots of idle time - Most processors are nowhere near 100% load - Applications for other computationally intensive problems

Procedure 1 – Demonstrate potential for greater utilization of local computational resources - CPU Load Perl Script - Records one-minute load averages every second - Produces graphable results - Sample output:

Procedure As the graphs show, CPU usage on most processors is close to zero before I start running tests on other students' computers. These graphs show CPU load averages (a measure of processor use) of computers being used by students during a systems lab class.

Procedure But did students even notice when the processor use on their computers spiked over 100% above normal levels? No! Seven out of seven students tested reported no noticeable change in performance.

Procedure Conclusions: - Average Systems Lab students use nowhere near their processor's full capabilities, during class time - That processor time/power is effectively going to waste - We haven't even mentioned the time when there is no user logged in to a given computer! Think how much time is just wasted at the login screen...

Where it goes from here... Taking advantage of unused time -Develop automated backgrounding of BLAST algorithms -Long-term tests of CPU usage -Attempt to realize increase in long-term usage with new applications Optimizing the algorithms -Test BLAST algorithms against one another -Test algorithms on different machines -Investigate supercomputer, cluster implementations