Granularity is the extent to which a system is broken down into small parts, either the system itself or its description or observation. It is the "extent to which a larger entity is subdivided. For example, a yard broken into inches has finer granularity than a yard broken into feet."

Coarse-grained systems consist of fewer, larger components than fine-grained systems; a coarse-grained description of a system regards large subcomponents while a fine-grained description regards smaller components of which the larger ones are composed.

The terms granularity, coarse, and fine are relative, used when comparing systems or descriptions of systems. An example of increasingly fine granularity: a list of nations in the United Nations, a list of all states/provinces in those nations, a list of all counties in those states, etc.

The terms "fine" and "coarse" are used consistently across fields, but the term "granularity" itself is not. For example, in investing, "more granularity" refers to more positions of smaller size, while photographic film that is "more granular" has fewer and larger chemical "grains".

Physics

A fine-grained description of a system is a detailed, low-level model of it. A coarse-grained description is a model where some of this fine detail has been smoothed over or averaged out. The replacement of a fine-grained description with a lower-resolution coarse-grained model is called coarse graining. (See for example the second law of thermodynamics)

Molecular dynamics

In molecular dynamics, coarse graining consists in replacing an atomistic description of a biological molecule with a lower-resolution coarse-grained model that averages or smooths away fine details. Coarse-grained models have been developed for investigating the longer time- and length-scale dynamics that are critical to many biological processes, such as lipid membranes and proteins.

Computing

In parallel computing, granularity means the amount of computation in relation to communication, i.e., the ratio of computation to the amount of communication.

Fine-grained parallelism means individual tasks are relatively small in terms of code size and execution time. The data is transferred among processors frequently in amounts of one or a few memory words. Coarse-grained is the opposite: data are communicated infrequently, after larger amounts of computation.

The finer the granularity, the greater the potential for parallelism and hence speed-up, but the greater the overheads of synchronization and communication.

In order to attain the best parallel performance, the best balance between load and communication overhead needs to be found. If the granularity is too fine, the performance can suffer from the increased communication overhead. On the other side, if the granularity is too coarse, the performance can suffer from load imbalance.

Reconfigurable computing and supercomputing

In reconfigurable computing and in supercomputing these terms refer to the data path width. The use of about one bit wide processing elements like the configurable logic blocks (CLBs) in an FPGA is called fine-grained computing or fine-grained reconfigurability, whereas using wide data paths, such as, for instance 32 bits wide resources, like microprocessor CPUs or data-stream-driven data path units (DPUs) like in a reconfigurable datapath array (rDPA) is called coarse-grained computing coarse-grained reconfigurability.

Data granularity

The granularity of data refers to the fineness with which data fields are sub-divided. For example, a postal address can be recorded, with low granularity, as a single field:

# address = 200 2nd Ave. South #358, St. Petersburg, FL 33701-4313 USA

or with high granularity, as multiple fields:

# street address = 200 2nd Ave. South #358 # city = St. Petersburg # postal code = FL 33701-4313 # country = USA

or even higher granularity:

# street = 2nd Ave. South # address number = 200 # suite/apartment number = #358 # city = St. Petersburg # state = FL # postal-code = 33701 # postal-code-add-on = 4313 # country = USA

Higher granularity has overheads for data input and storage. This manifests itself in a higher number of objects and methods in the object-oriented programming paradigm or more subroutine calls for procedural programming and parallel computing environments. It does however offer benefits in flexibility of data processing in treating each data field in isolation if required. A performance problem caused by excessive granularity may not reveal itself until scalability becomes an issue.

Credit portfolio risk management

In credit portfolio risk modeling, granularity refers to the number of the exposures in the portfolio. The higher the granularity, the more positions are in a credit portfolio, providing a higher degree of size diversification, which in turn reduces concentration risk. This is colloquially known as "not putting all your eggs in one basket".

Photographic film

In photography, granularity is a measure of film grain. It is measured using a particular standard procedure but in general a larger number means the grains of silver are larger and there are fewer grains in a given area.

Business

In modern U.S. business lingo, granularity refers to "specificity" or "hierarchical ranking". For example, granularity has been written about in the book, The Granularity of Growth: Making choices that drive enduring company performance. Its authors, Patrick Viguerie, Sven Smit, and Mehrdad Baghai say that there’s a problem with the broad-brush way that many companies describe their business opportunities. They argue that growth opportunities best emerge from a finer-than-usual understanding of market segments, their needs, and the capabilities required to serve them well.

References

See also

Granular computing

Multiple granularity locking

Particle size (geology)

Category:statistical mechanics

Curtis Roads is a composer of electronic and electroacoustic music specializing in granular and pulsar synthesis, author, and computer programmer.

He studied composition at the California Institute of the Arts and the University of California, San Diego and is Associate Professor at the University of California, Santa Barbara. He has previously taught at the University of Naples

Publications

Roads, Curtis (2009). Composing Electronic Music. Oxford University Press. (forthcoming)

Roads, Curtis (2001). Microsound. Cambridge: MIT Press. ISBN 0-262-18215-7

Roads, Curtis (1996). The Computer Music Tutorial. MIT Press. ISBN 0-262-68082-3

Roads, Curtis (1997). Musical Signal Processing. Routledge. ISBN 90-265-1483-2

Roads, Curtis and Strawn, John, eds (1987). Foundations of Computer Music. MIT Press. ISBN 0-262-68051-3

Compositions

POINT LINE CLOUD (2005) @Asphodel (Excerpt @ youtube)

Half-life (1998–1999)

Clang-Tint (1991–1994)

References

Wired.com article by Eliot Van Buskirk Hear Curtis Roads’ Subatomic Pop Symphonies (May 5 2008), accessed 16 February 2010

External links

moderecords.com profile

Asphodel artist page

delllounge.com Motherboard/Curtis Roads (3 videos)

Category:20th-century classical composers Category:American composers Category:21st-century classical composers Category:Living people Category:University of California, San Diego alumni Category:Year of birth missing (living people) Category:Harvard University faculty Category:University of California, Santa Barbara faculty Category:Oberlin College faculty