Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations

Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%

Cell Cycle Modeling for Budding Yeast with Stochastic Simulation Algorithms

For biochemical systems, where some chemical species are represented by small numbers of molecules, discrete and stochastic approaches are more appropriate than continuous and deterministic approaches. The continuous deterministic approach using ordinary differential equations is adequate for understanding the average behavior of cells, while the discrete stochastic approach accurately captures noisy events in the growth-division cycle. Since the emergence of the stochastic simulation algorithm (SSA) by Gillespie, alternative algorithms have been developed whose goal is to improve the computational efficiency of the SSA. This paper explains and empirically compares the performance of some of these SSA alternatives on a realistic model. The budding yeast cell cycle provides an excellent example of the need for modeling stochastic effects in mathematical modeling of biochemical reactions. This paper presents a stochastic approximation of the cell cycle for budding yeast using Gillespie’s stochastic simulation algorithm. To compare the stochastic results with the average behavior, the simulation must be run thousands of times. Many of the proposed techniques to accelerate the SSA are not effective on the budding yeast problem, because of the scale of the problem or because underlying assumptions are not satisfied. A load balancing algorithm improved overall performance on a parallel supercomputer

Implementasi Program Wirausaha Baru Oleh Dinas Tenaga Kerja Dan Transmigrasi Dalam Mendukung Gerdu Kempling Kota Semarang Tahun 2014

The Government of Semarang through Local Regulation Number 4 of 2008 about poverty reduction in Semarang City which is an acceleration in poverty reduction efforts. The strategy called Gerdu Kempling (Integrated Health, Economy, Education, Infrastructure, and Environment ) and one of the program that is New Entrepreneur Program by Dinas Tenaga Kerja dan Transmigrasi Kota Semarang. This research was meant to find out how the implementation of New Entrepreneur Program by Dinas Tenaga Kerja Dan Transmigrasi that supports Gerdu Kempling Kota Semarang in 2014 and knowing the influence factors of this implementation. New Entrepreneur Program has been part of Gerdu Kempling starting in 2011. There are three locations in this research: Village of Bulusan, Ngadirgo and Padangsari. This research using qualitative descriptive research methods. The subject in this study consisted of eight (8) informants. The results showed that the implementation of New Entrepreneur Program are still less effective that is seen from the precision implementation aspects. The factors that influence the implementation such as the goals and basic of policy, resource policy, communication and implementation activities, the implementing agency characteristics, external conditions as well as the disposition of the implementor are still less optimal too. Based on these conclusions, the researcher recommend to the implementation agency and target of this program need high commitment and take maximal advantages for sustainable in order to achieve the purpose of this program

1 Pengaruh Mengkonsumsi Rebusan Daun Sirsak Terhadap Penurunan Nyeri Pada Penderita Gout Artritis Di Wilayah Kerja Puskesmas Pineleng

Gout artritis merupakan penyakit yang ditandai dengan nyeri yang terjadi berulang-ulang yang disebabkan adanya endapan kristal monosodium urat yang tertumpuk di dalam sendi sebagai akibat tingginya kadar asam urat di dalam darah. Mengkonsumsi rebusan daun sirsak (Anonna muricata) adalah salah satu jenis terapi nonfamakologi yang bertujuan untuk menurunkan tingkat nyeri pada penderita gout artritis karena senyawa yang terkandung dalam daun sirsak berfungsi sebagai analgetik yang mempu mengurangi nyeri gout.Tujuan penelitan ini adalah untuk menganalisis pengaruh mengkonsumsi rebusan daun sirsak terhadap penurunan nyeri pada penderita gout artritis di wilayah kerja Puskesmas Pineleng.Sampel diambil dengan menggunakan total sampling yaitu 34 orang yang memenuhi kriteria inklusi.Desain penelitian yang digunakan adalah Time Series Design dan data yang dikumpulkan dari responden menggunakan lembar observasi.Hasil penelitian uji Wilcoxon sign rank test pada hasil akhir didapatkan nilai p = 0,004 < α = 0,005 sehingga dapat diambil Kesimpulan bahwa hipotesis penelitian diterima, hal ini menunjukan bahwa ada pengaruh mengkonsumsi rebusan daun sirsak terhadap penurunan nyeri pada penderita gout artritis di wilayah kerja Puskesmas Pineleng.Saran untuk penelitian selanjutnya dapat menggunakan populasi yang lebih besar untuk hasil yang lebih akurat serta dapat mengembangkan penelitian tentang pengaruh mengkonsumsi rebusan daun sirsak terhadap variabel yang lain seperti penurunan tekanan darah pada penderita hipertensi

Neutron knockout of 12Be populating neutron-unbound states in 11Be

Neutron-unbound resonant states of 11Be were populated in neutron knock-out reactions from 12Be and identified by 10Be-n coincidence measurements. A resonance in the decay-energy spectrum at 80(2) keV was attributed to a highly excited unbound state in 11Be at 3.949(2) MeV decaying to the 2+ excited state in 10Be. A knockout cross section of 15(3) mb was inferred for this 3.949(2) MeV state suggesting a spectroscopic factor near unity for this 0p3/2- level, consistent with the detailed shell model calculations.Comment: 5 pages, 2 figures \pacs{29.38.Db, 29.30.Hs, 24.50.+g, 21.10.Pc, 21.10.Hw, 27.20.+n} \keywords{neutron decay spectroscopy, neutron-unbound states in 11Be

Radio Astronomy

Contains reports on seven research projects and research objectives.National Science Foundation (Grant AST77-06052)Joint Services Electronics Program (Contract DAAG29-78-C-0020)National Aeronautics and Space Administration (Contract NAS5-21980)U.S. Department of Commerce - National Oceanic and Atmospheric Administration (Grant 04-8-M01-1)National Aeronautics and Space Administration (Contract NAS5-22929)National Aeronautics and Space Administration (Contract NAS5-25091)National Science Foundation (Grant AST77-12960)National Science Foundation (Grant AST77-26896

Radio Astronomy

Contains reports on research objectives and eight research projects.National Science Foundation (Grant AST79-25075)National Science Foundation (Grant AST79-20984)National Science Foundation (Grant AST79-19553)U.S. Navy - Office of Naval Research (Contract N00014-80-C-0348)National Aeronautics and Space Administration (Grant NAG2-50)M.I.T. Sloan Fund for Basic ResearchJoint Services Electronics Program (Contract DAAG29-78-C-0020)Joint Services Electronics Program (Contract DAAG29-80-C-0104)National Aeronautics and Space Administration (Grant NAG5-10)National Aeronautics and Space Administration (Contract NAS5-25091)National Aeronautics and Space Administration (Contract NAS5-22929)U.S. Department of Commerce - National Oceanic and Atmospheric Administration (Grant 04-8-MOl-1