Search

The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized‐Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large‐scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-throughput computing (HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller, independent components. However, it requires a large amount of computing power for a long time. Most existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level execution. Also, most of the frameworks reach a limited scale, and their usability needs further improvement. Herein, we present HTDcr, a job execution framework for the HTC on supercomputers. This study aims to improve the throughput, task dispatching, and usability of the framework. In detail, the throughput optimizations include a sophisticated designed task management system, a hierarchical scheduler, and the co-optimization of the task-scheduling strategy with the application and hardware characteristics. The optimizations for usability include a programable execution workflow, mechanisms for more robust and reliable service qualities, and a fine-grained resource allocation system for the colocation of multiple jobs. According to our evaluations, HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC workload. We evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design mechanisms. For instance, the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy. [ABSTRACT FROM AUTHOR]

In 2003 the first testing version of supercomputer in V.M. Glushkov Institute of cybernetics the NAS of Ukraine was created. Since then computing system was constantly developed and turned into supercomputing complex SCIT “Supercomputer for information technologies”. During 20 years in operation complex has involved in numerous big and small projects, and now it’s time to summarize that period. The data presented in the article will be useful primarily for developers of computer clusters who are actively improving the operation of their systems and developing new cluster management algorithms, and scientists who use the SCIT complex for their research.

The increase in performance and power of computing systems requires the wider use of program optimizations. The goal of performing optimizations is not only to reduce program runtime, but also to reduce other computer resources including power consumption. The goal of the study was to evaluate the impact of different optimization levels and various optimization strategies on power consumption. In a series of experiments, it was established that the average power consumption tends to peak for the programs with optimized source code. The articles also describes the impact of changing computer architecture on power consumption graphs. The relationships between the average and median values of power consumption by example programs are considered. The possibility of creating program energy consumption profile for a parallel program is shown. [ABSTRACT FROM AUTHOR]

Public research, which requires large computational resources, utilizes the supercomputers of the National Supercomputing Center in the Republic of Korea. The average utilization rate of resources over the past three years reached 80%. Therefore, to ensure the operational stability of this national infrastructure, specialized centers have been established to distribute the computational demand concentrated in the national centers. It is necessary to predict the computational demand accurately to build an appropriate resource scale. Therefore, it is important to estimate the inflow and outflow of computational demand between the national and specialized centers to size the resources required to construct specialized centers. We conducted a logit model analysis using the probabilistic utility theory to derive the preferences of individual users for future supercomputer resources. This analysis shows that the computational demand share of specialized centers is 59.5%, which exceeds the resource utilization plan of existing specialized centers. [ABSTRACT FROM AUTHOR]

During the initialization of a supercomputer job, no useful calculations are performed. A high proportion of initialization time results in idle computing resources and less computational efficiency. Certain methods and algorithms combining jobs into groups are used to optimize scheduling of jobs with high initialization proportion. The article considers the influence of the scale ratio setting in algorithm for the job groups formation, on the efficiency metrics of the job management system. The study was carried out on the developed by authors Alea-based job management system model. The model makes it possible to conduct a large number of experiments in reasonable time without losing the accuracy of the simulation. We performed a series of experiments involving various characteristics of the workload. The article represents the results of a study of the scale ratio influence on efficiency metrics for different initialization time proportions and input workflows with varying intensity and homogeneity. The presented results allow the job management system administrators to set a scale ratio that provides an appropriate balance with contradictory efficiency metrics. [ABSTRACT FROM AUTHOR]