CSC/ECE 506 Fall 2007/wiki1 2 3K8i

Trends in Scientific and Engineering Computing

Scientific and engineering applications continue to demand the highest performance computers available, a trend established in the early days of supercomputers that continues today in the realm of "High Performance Computing" (HPC). As problems are solved, new, more complex problems arise to take their place. Many Grand Challenge Problems "on the radar" today were not feasible on the supercomputers of a just decade ago. Generally they require more memory, faster processors, more aggregate processing speed, and better algorithms to efficiently harness those resources.

Before looking at some of today's most challenging computational problems, it may be instructive to look at the trends in HPC over the last decade or so.

Hardware Trends

What characterizes present-day applications? How much memory, processor time, etc.? How high is the speedup? For several years the trend has been towards commodity processor based clusters. (mutithreaded, single address space) (http://www.arl.army.mil/www/default.cfm?Action=20&Page=272)

Also towards grid computing.

Suzanne Tracy - "hundreds of cores on a socket by 2015." (http://www.accessmylibrary.com/coms2/summary_0286-30177016_ITM)

http://www.nus.edu.sg/comcen/svu/publications/SVULink/vol_1_iss_1/hpc-trends.html

Off the Shelf Software Packages Mathematica 6 - does not require parallel processors, but does require significant resources (from the "average" personal computer) - at least 512mb memory. On many operating systems Mathematica is able to take advantage of multiple processors, focusing on linear algebra and machine-precision real numbers. (http://support.wolfram.com/mathematica/systems/allplatforms/multipleprocessors.html) See also: http://www.wolfram.com/products/applications/parallel/ http://www.wolfram.com/products/gridmathematica/

Some challenges today in keeping the "performance" in HPC (High Performance Computing): http://www.scientificcomputing.com/ShowPR~PUBCODE~030~ACCT~3000000100~ISSUE~0707~RELTYPE~HPCC~PRODCODE~00000000~PRODLETT~C.html All is not well in HPC. There is a tend towards virtualization, or running on virtual machines. One benefit of this approach is that the virtual machine can be seamlessly migrated off its physical host machine. However, in practice there are many challenges related to virtualization. Current designs impose some performance limitations on HPC. <examples, source>

Petascale computing - 10^15 floating point operations per second! Should be a reality by 2010 (http://www.nsf.gov/pubs/2005/nsf05625/nsf05625.htm).

Computational modeling/simulation: why, benefits. in-depth analysis can be performed cheaply on hypothetical designs. There is a direct correlation between computational performance and the problems that can be studied through simulation.

// todo: work on wording below
Some problems are so complex that to solve them would require a significant increase (in some cases by orders of magnitude) in the current computational capabilities of today's computers. These problems are loosely defined as "Grand Challenge Problems." Grand Challenge problems are problems that are solvable, but not in a reasonable period of time on today's computers. Further, a grand challenge problem is a problem of some importance, either socially or economically.

Biology - Human Genome Project (http://en.wikipedia.org/wiki/Human_genome_project) Looks like a "divide and conquer" approach. The genome was broken down into smaller pieces, approximately 150,000 base pairs in length, processed separately, then assembled to form chromosones.

Physics (nuclear technology)

Astronomy

Cognition/Strong AI - the idea that computers can become "self aware." (vs. weak AI who's goal is not so grandiose - Turing test)

Game playing - chess, checkers (Jonathon Schaefer)

Linpack benchmark

Links: http://en.wikipedia.org/wiki/High_Performance_Computing

http://www.top500.org/

http://en.wikipedia.org/wiki/Grand_Challenge_problem

http://en.wikipedia.org/wiki/Grand_Challenge