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][1] "on the radar" today were not feasible on the supercomputers of a just decade ago. Generally, each successive generation of Grand Challenge problems require more memory and faster processing capabilities. As Moore's law appears to be reaching its end, those requirements are being realized in the aggregate - by utilizing multiple processors, multiple computers, 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

According the the U.S. Army Research Laboratory (http://www.arl.army.mil/www/default.cfm?Action=20&Page=272), there have been five generations of architectures in the realm of scientific computing. They are serial processors (1947-1986), vector processors (1986-2002), shared memory (1993-present), distributed memory (2000-present), and commodity clusters (2001-present).

Indeed, the trend in recent years has been towards cluster and grid computing, which seek to take advantage of multiple, low cost (commodity) computer systems and treat them as a logical entity. Perhaps the strongest driving force behind this trend is the economies of scale. Specialized hardware is, by definition, in less demand then general purpose hardware, making it much more difficult to recover design and production costs. Hence, there is a strong economic incentive for consumers to use more general purpose solutions.

Several categories of clusters exists, ranging from high performance computers linked together via high speed interconnects, to geographically separated systems linked together over the Internet.

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)

Also towards grid computing.

Tony Hey - "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

Software Trends

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.

Grand Challenge Problems

// 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

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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