Brze paralelne molekularne simulacije

We have developed and built several clusters of Personal Computers (PCs) that we use to perform parallel molecular simulations of chemically, physically, and biologically relevant systems. We describe the distinguishing networking topology of our clusters that enable them to perform classical and quantum mechanical computer simulations faster than standard PC clusters. Several techniques that we have used in parallelizing simulation programs are described. We employed these clusters for simulations of several different molecular systems. Also the computational performance of these simulations on our PC clusters is presented.Razvijeno je i izgrađeno par grozdova osobnih računala (PC) za izvođenje paralelnih molekularnih simulacija raznih kemijskih, fizikalnih i biološki relevantnih sustava. Osebujna topologija umrežavanja ovih grozdova je, u odnosu na standardne PC grozdove, u stanju brže izvoditi klasične i kvantnomehaničke simulacije. Opisano je više tehnika za paraleliziranje simulacijskih programa koji su zatim primjenjeni na niz molekularnih sustava. Diskutirana je tako|er računalna učinkovitost simulacija na predlo`enim PC grozdovima

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension

Parallel Computer Simulations on Clusters of Personal Computers

Parallel computer programs are used to speed up the calculation of computationally-demanding scientific problems such as molecular dynamics (MD) simulations. Parallel MD methods distribute calculations to the processors of a parallel computer but the efficiency of parallel computation decreases due to interprocessor communication. Calculating the interactions among all atoms of the simulated system is the most computationally demanding part of an MD simulation. Parallel methods differ in their distribution of these calculations among the processors, while the distribution dictates the method's communication requirements. I have developed a parallel method for MD simulation, the distributed-diagonal force decomposition method. Compared to other methods its communication requirements are lower and it features dynamic load balancing, which increase the parallel efficiency. I have designed a cluster of personal computers featuring a topology based on the new method. Its lower communication time in comparison to standard topologies enables an even greater parallel efficiency

Parallel Computer Simulations on Clusters of Personal Computers

Parallel computer programs are used to speed up the calculation of computationally-demanding scientific problems such as molecular dynamics (MD) simulations. Parallel MD methods distribute calculations to the processors of a parallel computer but the efficiency of parallel computation decreases due to interprocessor communication. Calculating the interactions among all atoms of the simulated system is the most computationally demanding part of an MD simulation. Parallel methods differ in their distribution of these calculations among the processors, while the distribution dictates the method's communication requirements. I have developed a parallel method for MD simulation, the distributed-diagonal force decomposition method. Compared to other methods its communication requirements are lower and it features dynamic load balancing, which increase the parallel efficiency. I have designed a cluster of personal computers featuring a topology based on the new method. Its lower communication time in comparison to standard topologies enables an even greater parallel efficiency

ANALIZA GENETSKIH PODATKOV IN NAČRTOVANJE GENETSKIH POSKUSOV Z METODAMI UMETNE INTELIGENCE

Mutations are a key tool that geneticists use in exploring biological processes. By using them, they can determine which genes play a role in some biological process and observe their mutual influence. They most often summarize their observations and conclusions about gene relations and influences in a graph called a genetic network. To build a genetic network biologists use the gentic data that includes information on mutants (changes in genotypes) and data on the resulting change in biological processes due to mutations induced (changes in phenotypes). To infer relatons between genes, biologist use reasoning (inference) patterns of the type ''IF a certain combination of experiments is present THEN we may propose a certain relation between genes and biological processes.'' Although such an analytical process is established among geneticists, it has only recently been formalized and implemented in the GenePath program. The main contribution of the project reported in this work is the development of an algorithm for proposing additional genetic experiments and an algorithm for generating hypothetical genetic networks that are consistent with genetic data. For this this, we have extended GenePath, which originally generated only one genetic network and did not suggest any additional experiments. The algorithm that proposes additional genetic experiments first detemines which pairs of genes are unrelated due to the lack of experiments. Zhe suggestions, which are determined on the basis of background knowledge in the form of inference patterns, include these missing experiments. The algorithm for generating hypothetical networks is recursive and generates all possible networks consistent with the experimental data. Constraints derived from known gene relations limit the number of generated networks, and the algorithm discontinues generating the networks that are inconsistent with the constraints. We tested both algorithms on experimental data on Dictyostelium discoideum maoebas and Caenorhabditis elegans nematodes. Both algorithms have proven to perform well in the analysis, planning of additional experiments with mutants, and gradual building of relatively large genetic networks. We therefore conclude that the methods we have developed and the tools we have implemented in this work are adequate and useful for experts in the field of functional genomics, and csn be used as ''intelligent assistants'' that aid in the analysis of genetic data and the hypothsizing on genetic networks

ANALIZA GENETSKIH PODATKOV IN NAČRTOVANJE GENETSKIH POSKUSOV Z METODAMI UMETNE INTELIGENCE

Mutations are a key tool that geneticists use in exploring biological processes. By using them, they can determine which genes play a role in some biological process and observe their mutual influence. They most often summarize their observations and conclusions about gene relations and influences in a graph called a genetic network. To build a genetic network biologists use the gentic data that includes information on mutants (changes in genotypes) and data on the resulting change in biological processes due to mutations induced (changes in phenotypes). To infer relatons between genes, biologist use reasoning (inference) patterns of the type ''IF a certain combination of experiments is present THEN we may propose a certain relation between genes and biological processes.'' Although such an analytical process is established among geneticists, it has only recently been formalized and implemented in the GenePath program. The main contribution of the project reported in this work is the development of an algorithm for proposing additional genetic experiments and an algorithm for generating hypothetical genetic networks that are consistent with genetic data. For this this, we have extended GenePath, which originally generated only one genetic network and did not suggest any additional experiments. The algorithm that proposes additional genetic experiments first detemines which pairs of genes are unrelated due to the lack of experiments. Zhe suggestions, which are determined on the basis of background knowledge in the form of inference patterns, include these missing experiments. The algorithm for generating hypothetical networks is recursive and generates all possible networks consistent with the experimental data. Constraints derived from known gene relations limit the number of generated networks, and the algorithm discontinues generating the networks that are inconsistent with the constraints. We tested both algorithms on experimental data on Dictyostelium discoideum maoebas and Caenorhabditis elegans nematodes. Both algorithms have proven to perform well in the analysis, planning of additional experiments with mutants, and gradual building of relatively large genetic networks. We therefore conclude that the methods we have developed and the tools we have implemented in this work are adequate and useful for experts in the field of functional genomics, and csn be used as ''intelligent assistants'' that aid in the analysis of genetic data and the hypothsizing on genetic networks