Reducing the computational load of energy evaluations for protein folding

Santos, Eunice E.; Santos, Eugene

doi:10.1109/bibe.2004.1317328

Cited by 7 publications

(3 citation statements)

References 14 publications

(22 reference statements)

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“…When combined with benchmark HP problems, this will allows us to study a fairly realistic testbed to demonstrate the feasibility of partial fitness evaluation caching. Results in this paper were originally presented in Santos and Santos (2004). 10 …”

Section: Introductionmentioning

confidence: 94%

Effective Computational Reuse for Energy Evaluations in Protein Folding

Santos

2006

Int. J. Artif. Intell. Tools

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Predicting native conformations using computational protein models requires a large number of energy evaluations even with simplified models such as hydrophobichydrophilic (HP) models. Clearly, energy evaluations constitute a significant portion of computational time. We hypothesize that given the structured nature of algorithms that search for candidate conformations such as stochastic methods, energy evaluation computations can be cached and reused, thus saving computational time and effort. In this paper, we present a caching approach and apply it to 2D triangular HP lattice model. We provide theoretical analysis and prediction of the expected savings from caching as applied this model. We conduct experiments using a sophisticated evolutionary algorithm that contains elements of local search, memetic algorithms, diversity replacement, etc. in order to verify our hypothesis and demonstrate a significant level of savings in computational effort and time that caching can provide.

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Section: Introductionmentioning

confidence: 94%

Effective Computational Reuse for Energy Evaluations in Protein Folding

Santos

2006

Int. J. Artif. Intell. Tools

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“…The fundamental nature of GAs relies on heuristics or randomization to quickly search large numbers of candidate results in order to achieve better solutions over time. Unfortunately, the more likely a good solution can be found, the more computational re-sources are needed by GAs [16]. This leads to high runtime on sequential architectures.…”

Section: Introductionmentioning

confidence: 97%

Mapping of Genetic Algorithms for Protein Folding onto Computational Grids

Liu

Schmidt

2005

TENCON 2005 - 2005 IEEE Region 10 Conference

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Computational biology research is now faced with the burgeoning number of genome data. The rigorous postprocessing of this data requires an increased role for high performance computing. In this paper, we present a framework to map hierarchical genetic algorithms for protein folding problems onto computational grids. It has been designed to take advantage of the communication characteristics of a computational grid. By using this framework, the two level communication parts of hierarchical genetic algorithms are separated. Thus both parts of the algorithm can evolve independently. This permits users to experiment with alternative communication models on different levels conveniently. Our experiments show that it can lead to significant runtime savings on PC clusters and computational grids.

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“…In the previous section, we have pointed out that the PFP is NP-hard and heuristic optimization methods such as GAs are suitable for solving the PFP. Unfortunately, the more likely a good solution can be found, the more computational resources are needed by GAs [124]. This leads to high runtimes on sequential architectures.…”

Section: A Hierarchical Parallel Genetic Algorithm For Protein Folding Problemsmentioning

confidence: 99%

Parallel and distributed algorithms for computational biology

Liu¹

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The computational biology (CB) research area is now faced with an obstacle of everincreasing genome data. The amount, complexity and increased need for the rigorous postprocessing of this data requires an increased role for high performance computing (HPC).Dynamic programming (DP) is an important algorithm design technique in scientific computing. It has been widely applied to solve CB problems. Typical applications using this technique are compute-intensive and suffer from long runtimes on sequential architectures. In this thesis, we are proposing a tunable coarse-grained partitioning and communication scheme for DP algorithms. By introducing two performance-related parameters division and rowwidth, we can tradeoff between computation time and communication time by tuning these two parameters and thus obtain the maximum possible performance. We will demonstrate how this scheme leads to substantial performance gains for both regular and irregular DP applications.Genetic algorithms (GAs) are efficient search methods based on Darwinian evolution.They have powerful characteristics such as robustness and flexibility to capture global solutions of complex optimization problems. The fundamental nature of GAs relies on heuristics to quickly search large numbers of candidate results in order to achieve better solutions over time. Unfortunately, the more likely a good solution can be found, the more computational resources are needed by GAs. This leads to high runtimes on sequential architectures. Because of their inherent parallelism, GAs are promising candidates for efficient and scalable parallel implementations. In this thesis, we present the design of a new hierarchical parallel genetic algorithm (HPGA) for the protein folding problem (PFP) on PC clusters and computational grids. Our hierarchical approach unites the inter-cluster and intra-cluster parallelism in an efficient way by using a combination of two communication models, i.e. the stepping stone model and the island model. This i

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Reducing the computational load of energy evaluations for protein folding

Cited by 7 publications

References 14 publications

Effective Computational Reuse for Energy Evaluations in Protein Folding

Effective Computational Reuse for Energy Evaluations in Protein Folding

Mapping of Genetic Algorithms for Protein Folding onto Computational Grids

Parallel and distributed algorithms for computational biology

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