The steady increase of computing power at lower and lower cost enables molecular dynamics simulations to investigate the process of protein folding with an explicit treatment of water molecules. Such simulations are t...
详细信息
ISBN:
(纸本)0769519199
The steady increase of computing power at lower and lower cost enables molecular dynamics simulations to investigate the process of protein folding with an explicit treatment of water molecules. Such simulations are typically done with well known computational chemistry codes like CHARMM. Desktop grids such as the United Devices MetaProcessor are highly attractive platforms, since scavenging for unused machines on Intra- and Internet delivers compute power that is almost free. However, the predominant programming paradigm for current desktop grids is pure taskparallelism and might not fit the needs for protein folding simulations with explicit water molecules. A short overall turn-around time of a simulation remains highly important for research productivity, but the need for an accurate model and long simulation time-scales leads to tasks that are too large for optimal scheduling on a desktop grid. To address this problem, we introduce a combination of task- and dataparallelism as a well suitable computing paradigm for protein folding investigations on grid platforms. As a proof of concept, we design and implement a simple system for protein folding simulations based on the notion of combined task and dataparallelism with clustered workers. Clustered workers are machines grouped into small clusters according to network and CPU performance criteria and act as super-nodes within a desktop grid, permitting the utilization of dataparallelism in addition to the taskparallelism. We integrate our new paradigm into the existing software environment of the United Devices MetaProcessor. For a test protein, we reach a better quality of the folding calculations than we reached using just taskparallelism on distributed systems.
Attribute reduction is one of the important research issues in rough set theory. Most existing attribute reduction algorithms are now faced with two challenging problems. On one hand, they have seldom taken granular c...
详细信息
Attribute reduction is one of the important research issues in rough set theory. Most existing attribute reduction algorithms are now faced with two challenging problems. On one hand, they have seldom taken granular computing into consideration. On the other hand, they still cannot deal with big data. To address these issues, the hierarchical encoded decision table is first defined. The relationships of hierarchical decision tables are then discussed under different levels of granularity. The parallel computations of the equivalence classes and the attribute significance are further designed for attribute reduction. Finally, hierarchical attribute reduction algorithms are proposed in data and task parallel using MapReduce. Experimental results demonstrate that the proposed algorithms can scale well and efficiently process big data. (C) 2014 Elsevier B.V. All rights reserved.
Cellular automata proved to be a promising model to simulate several complex systems: the requirement is that space and time, taken into account, have to be discretizable, while the system to be simulated has to satis...
详细信息
Cellular automata proved to be a promising model to simulate several complex systems: the requirement is that space and time, taken into account, have to be discretizable, while the system to be simulated has to satisfy locality and uniformity in the evolutionary space. Often, in dealing with the simulation of real complex systems some properties of locality are lost and consequently standard CA model application is very difficult. For this reason it is useful to extend the classical CA model and introduce feasible mechanisms in order to take advantage of the parallelism source of this computational model. With this aim the Cellular Automata Network (CAN) model was conceived that includes the advantages of classical CA models and introduces a new source of parallelism, i.e. the network of cellular automata. In this paper we deal with a sort of heuristics in order to map CA applications into CANs. This mapping can also be extremely useful as a proposal of a methodology to drive the modeling and simulation activity of complex phenomena that can be easily fragmented according to local interaction and components. (C) 2002 Elsevier Science B.V. All rights reserved.
Cellular automata proved to be a promising model to simulate several complex systems: the requirement is that space and time, taken into account, have to be discretizable, while the system to be simulated has to satis...
详细信息
Cellular automata proved to be a promising model to simulate several complex systems: the requirement is that space and time, taken into account, have to be discretizable, while the system to be simulated has to satisfy locality and uniformity in the evolutionary space. Often, in dealing with the simulation of real complex systems some properties of locality are lost and consequently standard CA model application is very difficult. For this reason it is useful to extend the classical CA model and introduce feasible mechanisms in order to take advantage of the parallelism source of this computational model. With this aim the Cellular Automata Network (CAN) model was conceived that includes the advantages of classical CA models and introduces a new source of parallelism, i.e. the network of cellular automata. In this paper we deal with a sort of heuristics in order to map CA applications into CANs. This mapping can also be extremely useful as a proposal of a methodology to drive the modeling and simulation activity of complex phenomena that can be easily fragmented according to local interaction and components. (C) 2002 Elsevier Science B.V. All rights reserved.
暂无评论