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, each successive generation of Grand Challenge problems require more memory and faster processing capabilities. The architectures used to deliver those memory and processing speed requirements are changing, and along with them the algorithms and techniques used to make efficient use of them.
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 in multiprocessing has been away from vector architectures and towards low cost, multi-threaded single address spaced systems. (http://www.nus.edu.sg/comcen/svu/publications/SVULink/vol_1_iss_1/hpc-trends.html)
There have also been strong trends towards cluster and grid computing, which seek to take advantage of multiple, low cost (commodity) computer systems and treat them as a logical entity. 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. (http://en.wikipedia.org/wiki/Cluster_computing) Grid computing is a form of the latter, generally composed of multiple "collections" of computers (grid elements) that do not necessarily trust each other.
Putting these together, we see a clear movement towards commodity processors in both the areas of parallel computing (processors in a single computer system) and distributed computing (using multiple computer systems). Perhaps the strongest driving force behind these trends is one of supply and demand, or 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 and manufacturers alike to favor more general purpose solutions.
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.
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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
Links: http://en.wikipedia.org/wiki/High_Performance_Computing