Using Parallel Computing Methods in Business Processes

Ondřej MACHEKDepartment of Business EconomicsFaculty of Business AdministrationUniversity of Economics in PragueCzech Republic

Jan HEJDADepartment of Biomedical Technology Faculty of Biomedical EngineeringCzech Technical University in PragueCzech Republic

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Parallel computing methods

• Single core processors will soon reach their physical limits

• Parallel computing as a possibility of further increasing the computer power

• Examples of challenging computing tasks:

• Numerical mathematics and physics

• Statistics – large sets of statistical data

• Theory of numbers – cryptography

• Computer science – visualization, large databases

• A great number of models and algorithms which allow optimal or nearly optimal use of parallel structures have been designed for parallel computers

• Nowadays, parallelism is used e.g. in current PCs, mobile phones or game consoles, but the real massive parallelism is used by supercomputers for special applications, but also in Internet search engines or social networks

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Business networks

• In the economic world, the motivation is not very different

• Efficient parallel cooperation of business unitshelp organizations reduce costs and time and increase consumer satisfaction

• Mutual relations of business entities have received the attention of many economic scientists

• Business units form large networks with various mutual connections and relations

• Graph theory is used to formalize these structures

• Multiple business units have to cooperate on the solution of a problem

• Sequential vs. parallel work

Ondřej MACHEK 25-26th May 2012

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Basic assumptions

• Multiple units cooperating on the solution of a problem are able to finish the work faster than if the operations were carried out sequentially

• Ould (1995): If two parts of a sequential process A and B take time ta + tb, parallel redesign of the process can lead to time max(ta,tb)

• Hammer (1990): Parallel work design and management cause more complex coordination of activities which negatively affects the performance gains

• Zapf (2007): „Most of the listed publications state the effect of paralleling as positive regarding the reduction of development time. But the achievable gains are valued extremely different: Starting from 2% gains go up to 53%. Some authors do not value the paralleling gains and Handfield mentions even a performance loss of 76% regarding the time to delivery for the enhancement of already developed products“.

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Theoratical constraints

• The business network is composed of identical business units

• Business units are subordinated to a central authority

• Each business unit has an executive system, a memory and communication system

• The communication can take place between two BUs (one-to-one communication) or between multiple BUs (one-to-all, all-to-all communication etc.)

• The communication system can be either one-directional or bi-directional

• The communication system can have one input-output interface (port), or multiple interfaces.

• A message can be of tangible or intangible nature

• The operations are carried out synchronously stepwise

• Each business unit can be either active or passive (idle) in a certain step

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Requierements for network topologies

• Low degree of nodes (additional links are more expensive)

• Low diameter (requires less communication links, less messages and reduces errors and congestion)

• Low fault diameter (a measure of a network’s stability and ability to bypass overloaded or congested nodes or edges)

• The networks should be regular, reproducible and understandable. Complexity increases the coordination effort and reduces performance

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Parallel computing methods

• Suitable networks - examples

• Measures of efficiency

• Optimal and fast algorithms

• Parallel state space search, collective communication algorithms…

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Practical issues

• Redesign of existing sequential processes and definition of responsibilities

• Requirements on collective work capabilities

• Comparable knowledge and skills raise the need of training

• Requirement on subordinated position imposes requirements on discipline

• One of the main problems is keeping the BUs working synchronously. This requires some synchronization system (imperative timestamps, barriers) which further requires some signaling system

• The need of efficient communication system to reduce the coordination efforts (information systems are suitable to automate the parallel tasks)

• The coordination efforts are greater for dense networks => use sparse networks

• Clear and unambiguous communication protocol

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Conclusion

• The intention of the paper was to consider the use of parallel computing in business process management

• The theory of parallel algorithms is still being developed, but some of its results could be used in the field of business networks

• The main issue is the overall complexity and additional coordination efforts

• Efficient algorithms for common business tasks have yet to be developed

• Empirical evidence is needed to verify the applicability of advanced parallelism in business process management

Ondřej MACHEK 25-26th May 2012

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Thank you for your attention

Ondřej MACHEK 25-26th May 2012