High Performance Grid Computing Research Articles

Spatial specification of hypertorus interconnect by infinite and reenterable coloured Petri nets

Multidimensional torus interconnect finds wide application in modern exascale computing. For models design in high-performance computing, grid and cloud computing, and also systems biology, two basic ways of specifying spatial structures with Petri nets are considered – an infinite Petri net specified by a parametric expression (PE) and a reenterable coloured Petri net (CPN). The paper studies a composition of hypertorus grid models in the form of a PE and a reenterable CPN, their mutual transformations, and unfolding into a place/transition net; the parameters are the number of dimensions and the size of grid. A grid is composed via connection of neighbouring cells by dedicated transitions modelling channels. Reenterable model peculiarities are explained on step-by-step simulation examples. The rules of mutual transformations of Petri net spatial specifications are specified. Comparative investigation of two mentioned forms of spatial specifications is implemented, including analysis techniques and tools. CPNs are convenient for the state space analysis. The main advantage of PEs is the ability to obtain linear invariants and other structural constructs of Petri nets, for instance, siphons and traps, in parametric form that allows us to draw conclusions on Petri net properties for any values of parameters.

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.

Strain engineering of electronic and magnetic properties of Ga2S2 nanoribbons**Project supported by the National Natural Science Foundation of China (Grant Nos. 11174220 and 11374226), the Key Scientific Research Project of the Henan Institutions of Higher Learning, China (Grant No. 16A140009), the Program for Innovative Research Team of Henan Polytechnic University, China (Grant Nos. T2015-3 and T2016-2), the Doctoral Foundation of

Using first-principles calculations, we study the tailoring of the electronic and magnetic properties of gallium sulfide nanoribbons (Ga2S2NRs) by mechanical strain. Hydrogen-passivated armchair- and zigzag-edged NRs (ANRs and ZNRs) with different widths are investigated. Significant effects in band gap and magnetic properties are found and analyzed. First, the band gaps and their nature of ANRs can be largely tailored by a strain. The band gaps can be markedly reduced, and show an indirect-direct (I-D) transition under a tensile strain. While under an increasing compressive strain, they undergo a series transitions of I-D-I-D. Five strain zones with distinct band structures and their boundaries are identified. In addition, the carrier effective masses of ANRs are also tunable by the strain, showing jumps at the boundaries. Second, the magnetic moments of (ferromagnetic) ZNRs show jumps under an increasing compressive strain due to spin density redistribution, but are unresponsive to tensile strains. The rich tunable properties by stain suggest potential applications of Ga2S2NRs in nanoelectronics and optoelectronics.

Parallel high-performance grid computing: capabilities and opportunities of a novel demanding service and business class allowing highest resource efficiency.

Especially in the life-science and the health-care sectors the huge IT requirements are imminent due to the large and complex systems to be analysed and simulated. Grid infrastructures play here a rapidly increasing role for research, diagnostics, and treatment, since they provide the necessary large-scale resources efficiently. Whereas grids were first used for huge number crunching of trivially parallelizable problems, increasingly parallel high-performance computing is required. Here, we show for the prime example of molecular dynamic simulations how the presence of large grid clusters including very fast network interconnects within grid infrastructures allows now parallel high-performance grid computing efficiently and thus combines the benefits of dedicated super-computing centres and grid infrastructures. The demands for this service class are the highest since the user group has very heterogeneous requirements: i) two to many thousands of CPUs, ii) different memory architectures, iii) huge storage capabilities, and iv) fast communication via network interconnects, are all needed in different combinations and must be considered in a highly dedicated manner to reach highest performance efficiency. Beyond, advanced and dedicated i) interaction with users, ii) the management of jobs, iii) accounting, and iv) billing, not only combines classic with parallel high-performance grid usage, but more importantly is also able to increase the efficiency of IT resource providers. Consequently, the mere yes-we- can becomes a huge opportunity like e.g. the life-science and health-care sectors as well as grid infrastructures by reaching higher level of resource efficiency.

Energy efficient scheduling strategies in Federated Grids

The impact of energy consumption and CO2 emissions in high-performance grid computing is no longer ignored. So far, most efforts have focused on energy efficiency measures of grid infrastructures's underlying hardware and on direct reduction of CO2 emissions. Thus, in the field of scheduling and resource allocation in computational grids some algorithms have been proposed for selecting grid infrastructures in energy- and CO2-aware manner or based on hardware power consumption. However, these hardware energy efficiency measures are not suitable at meta-scheduler level. Instead, this paper presents software energy efficiency measures that reduce computing power and communication bandwidth, and hence reduce algorithm's total communication cost and power consumption. The main idea is to restrict to the maximum the number of needed messages per job scheduled. Thus, we proposed performance based scheduling policies and self-adjusting resource sharing strategies that accomplish with applications makespan and resources performance while reducing the total amount of computation power and communication bandwidth required by the algorithm itself.

Using Hadoop File System and MapReduce in a small/medium Grid site

Data storage and data access represent the key of CPU-intensive and data-intensive high performance Grid computing. Hadoop is an open-source data processing framework that includes fault-tolerant and scalable distributed data processing model and execution environment, named MapReduce, and distributed File System, named Hadoop distributed File System (HDFS).HDFS was deployed and tested within the Open Science Grid (OSG) middleware stack. Efforts have been taken to integrate HDFS with gLite middleware. We have tested the File System thoroughly in order to understand its scalability and fault-tolerance while dealing with small/medium site environment constraints. To benefit entirely from this File System, we made it working in conjunction with Hadoop Job scheduler to optimize the executions of the local physics analysis workflows. The performance of the analysis jobs which used such architecture seems to be promising, making it useful to follow up in the future.

An Approach to Mobile Grid Platforms for the Development and Support of Complex Ubiquitous Applications

Several complex and time-critical applications require the existence of novel distributed, heterogeneous and dynamic platforms composed of a variety of fixed and mobile processing nodes and networks. Such platforms, that can be called Pervasive Mobile Grids, aim to merge the features of Pervasive Computing and Highperformance Grid Computing onto a new emerging paradigm. In this Chapter we study a methodology for the design and the development of high-performance, adaptive and context-aware applications. We describe a programming model approach, and we compare it with other existing research works in the field of Pervasive Mobile Computing, discussing the rationales of the requirements and the features of a novel programming model for the target platforms and applications. In order to exemplify the proposed methodology we introduce our programming framework ASSISTANT, and we provide some interesting future directions in this research field.

Parallel high-performance grid computing: capabilities and opportunities of a novel demanding service and business class allowing highest resource efficiency.

Especially in the life-science and the health-care sectors the huge IT requirements are imminent due to the large and complex systems to be analysed and simulated. Grid infrastructures play here a rapidly increasing role for research, diagnostics, and treatment, since they provide the necessary large-scale resources efficiently. Whereas grids were first used for huge number crunching of trivially parallelizable problems, increasingly parallel high-performance computing is required. Here, we show for the prime example of molecular dynamic simulations how the presence of large grid clusters including very fast network interconnects within grid infrastructures allows now parallel high-performance grid computing efficiently and thus combines the benefits of dedicated super-computing centres and grid infrastructures. The demands for this service class are the highest since the user group has very heterogeneous requirements: i) two to many thousands of CPUs, ii) different memory architectures, iii) huge storage capabilities, and iv) fast communication via network interconnects, are all needed in different combinations and must be considered in a highly dedicated manner to reach highest performance efficiency. Beyond, advanced and dedicated i) interaction with users, ii) the management of jobs, iii) accounting, and iv) billing, not only combines classic with parallel high-performance grid usage, but more importantly is also able to increase the efficiency of IT resource providers. Consequently, the mere "yes-we-can" becomes a huge opportunity like e.g. the life-science and health-care sectors as well as grid infrastructures by reaching higher level of resource efficiency.