Granularity refers to the level of detail in a system, whether it be in parallel processing, data division, scientific models, or finance. Fine-grained systems have more smaller parts, while coarse-grained systems have fewer larger parts. Higher granularity allows for more specific processing but requires more resources.
Granularity refers to how divisible a system is. Fine-grained systems, which have high granularity, are broken up into a greater number of smaller parts, while a coarse-grained system has fewer larger parts. For example, a measurement of the weight of an object in ounces is more granular than a measurement of the weight of the same object in pounds, which is in turn more granular than a measurement in tons. The concept is important in a number of areas, including science, information technology and business.
In parallel computer processing, the term refers to how tasks are divided. Fine-grained parallel processing divides a task into a large number of smaller, usually short-lived tasks, while coarse-grained parallel processing has larger, longer-lasting tasks. Finer granularity increases the amount of work that can be done simultaneously and is therefore potentially faster, but at the cost of requiring more resources for communication between processors.
Granularity is also used to describe the division of data. Data with low granularity is split into a small number of fields, while data with high granularity is split into a larger number of more specific fields. For example, a high data record of a person’s physical characteristics might have separate fields for height, weight, age, gender, hair color, eye color, and so on, while a low data record would record the same information in a fewer more general fields and an even fewer record will list all the information in one field. Higher granularity makes data more flexible by allowing more specific parts of the data to be processed separately, but requires more computational resources.
In the physical sciences, the term refers to the level of detail in scientific models. A fine-grain model is highly detailed, while a coarse-grain model averages low-level details rather than portraying them individually. For example, a fine-grained computer model of the interactions between atoms will model them at the subatomic level according to the laws of quantum mechanics, while somewhat coarser models may treat the entire nucleus of an atom as a single-point particle which is then modeled according to classical physics, and even grosser models treat entire groups of atoms as a single unit. Coarse-grained models are less accurate, but require less computing power to model a given system than fine-grained models. They also allow for the modeling of large-scale systems that would be impractical or impossible to represent with finer-grained models.
This concept is also used in business and finance. In banking, granularity in loan portfolio risk management refers to portfolio diversity. Highly granular portfolios have a higher number of exposures spread across a variety of economic areas, which protects the bank from facing large and sudden losses following a single large debtor default or a recession in a single sector. The term can also refer to a similar principle for reducing risk for investments in stocks, bonds or currencies.
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