An enormous challenge when the Internet matured into a mainstream technology was meeting the information technology workforce needs in a competitive business environment. In anticipation of a similar scenario for upcoming grid technology we are in the process of implementing a comprehensive multitier NSF-supported adaptation of grid technology in education. The project addresses the above mentioned challenge at three important levels of our educational system: the undergraduate, the graduate and the industrial training. Our grid technology-based curriculum has been developed for a sequence of two new courses for senior level undergraduates. The same courses would be taught at the graduate level with emphasis on research. Additionally, seminars are planned for spreading grid awareness to the local businesses and industries by using domain-dependent grid applications. This paper presents the details of the model we call GridForce (Grid For Research, Collaboration and Education) and our experiences with its implementation, with the objective of improving the technical preparedness of the workforce for the grid.

There is a growing awareness that high-end performance evaluation and tuning requires holistic program analysis. In addition to CPU performance characterization, observation of memory, network, and I/O performance can help to identify execution bottlenecks related to these factors. Correctness of memory and communication operations is also an issue and can affect performance indirectly. This paper describes extensions to the TAU performance system to incorporate direct source-level code instrumentation for tracking dynamic memory management in Fortran codes that use allocate and deallocate statements. TAU's lightweight profiling can then generate a detailed report of memory usage including the sizes of memory blocks allocated and deallocated with precise program attribution: variable name, source line number, and file name. We report on results and experiences in applying TAU to the PTURBO application.

With the increasing functionality and complexity of distributed systems, resource failures are inevitable. While numerous models and algorithms for dealing with failures exist, the lack of public trace data sets and tools has prevented meaningful comparisons. To facilitate the design, validation, and comparison of fault-tolerant models and algorithms, we have created the Failure Trace Archive (FTA) as an online public repository of availability traces taken from diverse parallel and distributed systems. Our main contributions in this study are the following. First, we describe the design of the archive, in particular the rationale of the standard FTA format, and the design of a toolbox that facilitates automated analysis of trace data sets. Second, applying the toolbox, we present a uniform comparative analysis with statistics and models of failures in nine distributed systems. Third, we show how different interpretations of these data sets can result in different conclusions. This emphasizes the critical need for the public availability of trace data and methods for their analysis.

Turning large volumes of data into actionable knowledge is a top challenge in high performance computing. Our previous work in this area demonstrated algorithmic techniques for massively parallel graph analysis on multithreaded systems. This work led to the development of GraphCT, the first endto-end graph analytics platform for the Cray XMT and x86class systems with OpenMP, and STINGER, a high performance, multithreaded, dynamic graph data structure and algorithms. Both of these packages are freely available as open source software. This dissertation research culminates in experimental and analytical techniques to study the marriage of disk-based systems, such as Hadoop, with shared memory-based systems, such as the Cray XMT, for data-intensive applications. David Ediger is a fifth year PhD candidate in Electrical and Computer Engineering.

As the gap between memory and processor speeds continues to widen, cache efficiency is an increasingly important component of processor performance. Compiler techniques have been used to improve instruction and data cache performance for virtually indexed caches by mapping code and data with temporal locality to different cache blocks. In this

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Porting well known computer vision algorithms to low power, high performance computing devices such as SIMD linear processor arrays can be a challenging task. One especially useful such algorithm is the color-based particle filter, which has been applied successfully by many research groups to the problem of tracking non-rigid objects. In this paper, we propose an implementation of the color-based particle filter suitable for SIMD processors. The main focus of our work is on the parallel computation of the particle weights. This step is the major bottleneck of standard implementations of the color-based particle filter since it requires the knowledge of the histograms of the regions surrounding each hypothesized target position. We expect this approach to perform faster in an SIMD processor than an implementation in a standard desktop computer even running at much lower clock speeds.

In object-oriented (OO) languages, the ability to encapsulate software concerns of the dominant decomposition in objects is the key to reaching high modularity and loss of complexity in large scale designs. However, distributed memory parallelism tends to break modularity and encapsulation of concerns in OO languages, since a parallel computation cannot be encapsulated in an individual object. For reconciling object-orientation and distributed memory parallelism, we propose PObC++, a OO language that introduces innovative ideas for object-oriented parallel programming (OOPP).

The study of material culture generated by military engagements has created an emergent sub-discipline of archaeological studies centred on battlefields. This approach has developed a particular and sophisticated methodology that is able to deal with the fact that archaeologists will often not find either structures or a useful stratigraphical record on the site, as the material remains of the battle

We present two designs (I and II) for IEEE 754 double precision floating point matrix multiplication, optimized for implementation on high-end FPGAs. It forms the kernel in many important tile-based BLAS algorithms, making an excellent candidate for acceleration. The designs, both based on the rank-1 update scheme, can handle arbitrary matrix sizes, and are able to sustain their peak performance

Based on an active transmission line concept and twodimensional (2-D) device simulations, an accurate and computationally efficient simulation technique for high frequency noise performance of MOSFETs is demonstrated. Using a Langevin stochastic source term model and small-signal equivalent circuit of the MOSFET, three intrinsic noise parameters ( , , and ) for the drain noise and induced gate noise are calculated. Validity and error analysis for the simulation are discussed by comparing the simulation results with theoretical results as well as measured data.

Data Stream Processing (DaSP) is a recent and highly active research field, applied in various real world scenarios. Differently than traditional applications, input data is seen as transient continuous streams that must be processed “on the fly”, with critical requirements on throughput, latency and memory occupancy. A parallel solution is often advocated, but the problem of designing and implementing high throughput and low latency DaSP applications is complex per se and because of the presence of multiple streams characterized by high volume, high velocity and high variability. Moreover, parallel DaSP applications must be able to adapt themselves to data dynamics in order to satisfy desired QoS levels. The aim of our work is to study these problems in an integrated way, providing to the programmers a methodological framework for the parallelization of DaSP applications.

Iterative applications are known to run as slow as their slowest computational component. This paper introduces malleability, a new dynamic reconfiguration strategy to overcome this limitation. Malleability is the ability to dynamically change the data size and number of computational entities in an application. Malleability can be used by middleware to autonomously reconfigure an application in response to dynamic changes in resource availability in an architecture-aware manner, allowing applications to optimize the use of multiple processors and diverse memory hierarchies in heterogeneous environments. The modular Internet Operating System (IOS) was extended to reconfigure applications autonomously using malleability. Two different iterative applications were made malleable. The first is used in astronomical modeling, and representative of maximum-likelihood applications was made malleable in the SALSA programming language. The second models the diffusion of heat over a two dimensional object, and is representative of applications such as partial differential equations and some types of distributed simulations. Versions of the heat application were made malleable both in SALSA and MPI. Algorithms for concurrent data redistribution are given for each type of application. Results show that using malleability for reconfiguration is 10 to 100 times faster on the tested environments. The algorithms are

Deep Neural Networks (DNNs) have recently been shown to significantly outperform existing machine learning techniques in several pattern recognition tasks. DNNs are the state-of-the-art models used in image recognition, object detection, classification and tracking, and speech and language processing applications. The biggest drawback to DNNs has been the enormous cost in computation and time taken to train the parameters of the networks -often a tenfold increase relative to conventional technologies. Such training time costs can be mitigated by the application of parallel computing algorithms and architectures. However, these algorithms often run into difficulties because of the cost of inter-processor communication bottlenecks. In this paper, we describe how to enable Parallel Deep Neural Network Training on the IBM Blue Gene/Q (BG/Q) computer system. Specifically, we explore DNN training using the dataparallel Hessian-free 2nd order optimization algorithm. Such an algorithm is particularly well-suited to parallelization across a large set of loosely coupled processors. BG/Q, with its excellent inter-processor communication characteristics, is an ideal match for this type of algorithm. The paper discusses how issues regarding programming model and data-dependent imbalances are addressed. Results on large-scale speech tasks show that the performance on BG/Q scales linearly up to 4096 processes with no loss in accuracy. This allows us to train neural networks using billions of training examples in a few hours.

As técnicas de processamento digital de imagens tendem a ser extremamente custosas computacionalmente. Isso leva os cientistas da área a procurar soluções com desempenho otimizado. Sendo assim, neste trabalho foi implementado um hardware otimizado (comumente tem-se soluções baseadas em software) para os filtros Sharpen e Smooth de pré-processamento de imagens, os quais tem por objetivo melhorar a qualidade da imagem, cujo destino, nesse caso, é passar pelo filtro Prewitt de detecção de bordas.

Continuously increasing energy standards have driven the need for increasing the efficiency of buildings. Most enhancements to building efficiency have been a result of changes to the heating/cooling systems, improvements in construction materials, or building design code improvements. These approaches neglect the way in which air is dispersed into individual rooms or in a building -i.e., the ducting system. This opens up the possibility of significant energy savings by making ductwork systems lighter and better insulating while ensuring cost effectiveness.

This contribution provides a definition of High Performance Computing, and then considers the importance of the computing paradigm for the humanities.  It identifies potential applications of HPC for the humanities, and factors in Canada that currently constrain effective humanist exploitation of HPC.

With increasing interest among mainstream users to run HPC applications, Infrastructure-as-a-Service (IaaS) cloud computing platforms represent a viable alternative to the acquisition and maintenance of expensive hardware, often out of the financial capabilities of such users. Also, one of the critical needs of HPC applications is an efficient, scalable and persistent storage. Unfortunately, storage options proposed by cloud providers are not standardized and typically use a different access model. In this context, the local disks on the compute nodes can be used to save large data sets such as the data generated by Checkpoint-Restart (CR). This local storage offers high throughput and scalability but it needs to be combined with persistency techniques, such as block replication or erasure codes. One of the main challenges that such techniques face is to minimize the overhead of performance and I/O resource utilization (i.e., storage space and bandwidth), while at the same time guar...

1 -X-ray crystallography is an important field due to its role in drug discovery and its relevance in bioinformatics experiments of comparative genomics, phylogenomics, evolutionary analysis, ortholog detection, and three-dimensional structure determination. Managing these experiments is a challenging task due to the orchestration of legacy tools and the management of several variations of the same experiment. Workflows can model a coherent flow of activities that are managed by scientific workflow management systems (SWfMS). Due to the huge amount of variations of the workflow to be explored (parameters, input data) it is often necessary to execute X-ray crystallography experiments in High Performance Computing (HPC) environments. Cloud computing is well known for its scalable and elastic HPC model. In this paper, we present a performance evaluation for the X-ray crystallography workflow defined by the PC4 (Provenance Challenge series). The workflow was executed using the SciCumulus middleware at the Amazon EC2 cloud environment. SciCumulus is a layer for SWfMS that offers support for the parallel execution of scientific workflows in cloud environments with provenance mechanisms. Our results reinforce the benefits (total execution time × monetary cost) of parallelizing the X-ray crystallography workflow using SciCumulus. The results show a consistent way to execute X-ray crystallography workflows that need HPC using cloud computing. The evaluated workflow shares features of many scientific workflows and can be applied to other experiments.

Parallelism and distribution have been considered the key features of neural processing. The term parallel distributed processing is even used as a synonym for arti cial neural networks. Nevertheless, the actual implementations are still in search of the appropriate model to "naturally represent" neural computing. And thenal judgement is always given in performance gures { keeping the parallelization issue high on the neurosimulation agenda. Two approaches have yielded the best results: parallel simulations on general-purpose computers, and specially developed neurohardware. Programming neural networks on parallel machines requires high-level techniques re ecting both inherent features of neuromodels and characteristics of the underlying computers. On the other hand, emulation of the neuroparadigm requires that the functioning of neural operations be mimicked directly by the hardware. Both approaches are presented, and their advantages and shortcomings are outlined.

The purpose of this paper is to investigate the e-commerce credibility factors affecting the perception of
users in Saudi Arabia and, moreover, to investigate whether the variation of credibility factors in Saudi
Arabian e-commerce websites influence users' performance. Website credibility, which refers to the
believability of the website and its content, plays an important role in consumers’ successful online
shopping experience and satisfaction. This investigation is conducted by employing two credibility
evaluation methods: heuristic evaluation and performance measurement. This study adopts Fogg's 10
Stanford credibility guidelines as a starting point for the heuristic evaluation. In the performance
measurement method, two measurements are used: the amount of time needed to finish the task and the
total number of clicks taken to finish the task. A frequency analysis of the comments and a one-way ANOVA
test are used to establish the results. Three e-commerce websites in Saudi Arabia are selected. The findings
show that Fogg’s 10 Stanford credibility guidelines can be implemented in the Saudi Arabian e-commerce
context with minor modifications and expansions by adding reputation, endorsement, security, and service
diversity guidelines. Another important finding is that professional website design plays a vital role in
users' first impression of websites, while usability is the most important credibility factor investigated used
to evaluate the credulity of an e-commerce website. Lastly, the results of this study indicate a relationship
between the e-commerce credibility level and users’ performance. This paper contributes to the literature
by providing a set of credibility guidelines associated with specific criteria, which can be assessed to
improve the future of e-commerce in Saudi Arabia.

Solving systems of linear equations is central in scientific computation. In this paper, we focus on using Intel's Pentium Streaming SIMD Extensions (SSE) for parallel implementation of LU-decomposition algorithm. Two implementations (non-SSE and SSE) of LUdecomposition are compared. Moreover, two different variants of the algorithm for the SSE version are also compared. Our results demonstrate an average performance of 2.25 times faster than the non-SSE version. This speedup is higher than 1.74 times the speedup of Intel's SSE implementation. The source of the speedup is highly reusing of loaded data by efficiently organizing SSE instructions.

Traditionally, bridge bearings supporting steel trapezoidal box girder (a.k.a. tub girder) superstructures consist of fabricator designed mechanical devices, such as pot or disk bearings (a.k.a. high load, multi-rotational bearings), that may be guided or unguided and are typically expensive to fabricate and place. The performance of these bearings has been mixed, with some being maintenance free and others requiring significant maintenance, repair and/or replacement. In Texas, bridge bearings supporting concrete and steel superstructure types are typically steellaminated elastomeric bearings that are tapered or have a constant thickness. Recently the Texas Department of Transportation has moved toward the use of these structurally engineered elastomeric bearings for steel trapezoidal box girders in place of mechanically engineered pot or disk bearings. This paper presents a discussion of the rationale for the use of elastomeric bearings for steel trapezoidal box girders, a historical overview of bridge bearing systems in Texas, and the design procedure for the elastomeric bearings of the steel trapezoidal box girders of the US 290 & IH 35 Interchange built in Austin in the late 1990s and place in service in 2001.

В последнее время нефтегазовые компании, а также научно-исследовательские и сервисные центры проявляют большой интерес к использованию неразрушающих методов определения петрофизических характеристик пласта-коллектора на основе микрокомпьютерной томографии образцов керна. Методы исследования начинаются с создания качественной цифровой модели образа керна, которая затем используется для моделирования воздействий различной природы на насыщенную пористую среду. Например, это может быть численное моделирование течения флюидов, вызванного приложенным перепадом давления, что дает возможность вычислить тензоры коэффициентов абсолютной и относительной проницаемостей, а также эффективную динамическую пористость. Подготовка цифровой модели состоит их трех этапов: построение внутренней структуры объекта по синограмме, фильтрация и сегментация трехмерного цифрового изображения. Поскольку каждая из этих операций может входить в контур решения обратной задачи-адаптации результатов моделирования к лабораторным измерениям, необходимо обеспечить высокое быстродействие и качество вычислительной модели. Объем оперативной памяти, необходимый для построения 3D-изображений как единого целого, может достигать сотен гигабайт даже для простой бит-кодировки в градациях серого. В статье предложен новый алгоритм неявной анизотропной фильтрации, получены условия его остановки, а также осуществлен выбор инструментальных средств параллельного программирования, которые можно использовать без существенных изменений на гетерогенных высокопроизводительных вычислительных системах различной архитектуры. Ключевые слова: микрокомпьютерная томография; фильтры, сохраняющие границы; неявный фильтр анизотропной диффузии; полудискретный метод прямых; квазилинейные уравнения в частных производных; безматричный предобусловленный метод сопряженных градиентов.

Lately, oil and gas companies as well as scientific-research and service centers have been paying much interest to the use of non-destructive methods that are applied to determine a reservoir's petrophysical characteristics, based on microcomputer tomography of core samples. The methods of researching start with development of a high quality digital model of a core sample, which is later used for simulation of different natural impacts on the saturated porous medium. For example, it can be numerical modeling of a fluid flow, caused by the pressure drop, that provides calculation of tensors of absolute and relative permeability as well as effective dynamic po-rosity. Development of the digital model includes three stages: construction of an object internal structure based on the sinogram, filtration and segmentation of a 3D digital image. Since any of these operations can be included in the contour of the inverse problem solution , namely, simulation results adaptation to laboratory studies, it is necessary to provide fast response time and quality of the computing model. RAM capacity required for constructing 3D-images can reach several hundred gigabytes even for an ordinary grayscale image coding. The paper proposes a new algorithm of implicit anisotropic filtration, submits conditions if its stoppage as well as chooses the instrumental tools of parallel programming, which can be used without essential alterations on heterogeneous high-performance computing systems of various architecture. Keywords: microcomputer tomography; filters, preserving the boundaries; implicit filter of anisotropic diffusion; quasi-discrete method of lines; quasi-linear PDE (partial differential equation/s); matrix-free preconditioned conjugate gradient method.

Molecular dynamics (MD) simulations are able to provide a wealth of detailed information about biological systems enabling studies impossible to perform in a laboratory. As such, MD simulations provide insight into important biological processes like protein folding, the mechanism of viral infections, as well as aid modern drug design.

Typical MD simulations require billions of time-steps, at each step evaluating forces between tens to hundreds of thousands of particles. This represents a floating-point bottleneck and suggests that the problem is well-suited for HPC. However, the iterative nature of MD algorithms means that speeding up simulations requires reducing the calculation time of a time-step. As the amount of parallelism is limited by the range of relevant problem sizes, improving absolute performance requires strong scaling. Highly optimized MD codes like GROMACS reach iteration rates in the millisecond range which renders MD strongly latency sensitive and represents unique challenges for efficient parallelization.

The HPC landscape is rapidly changing toward increasing on-chip parallelism as well as heterogeneity through emerging accelerator platforms like GPUs or Intel MIC.
The GROMACS MD package is known for its high performance thanks to hand-tuned SIMD kernels and state-of-the art parallel algorithms. However, making efficient use of modern architectures required a substantial redesign of the previously MPI-only parallelization as well as
entirely new algorithms for wide SIMD units and massive accelerator parallelism.

Here we present our recent work on multi-level heterogeneous parallelization of MD implemented in GROMACS. We have developed new, highly efficient SIMD algorithms for pair force calculation targeting current and future architectures ranging from CPUs to GPUs to FPGAs. Thanks to recent efforts, all compute-intensive code in GROMACS uses SIMD acceleration to maximize single-core CPU performance. OpenMP multithreading allows combining a set of cores with GPUs for node-level heterogeneous parallelization. On the top level, MPI-based neutral-territory domain-decomposition ensures scaling across compute nodes. To maximize the utilization of all compute resources, we employ multi-level, inter- and intra-node dynamic load balancing. The latest version of GROMACS not only provides a dramatic improvement in strong scaling to tens of particles per core at peak, but is able to efficiently utilize all compute units in heterogeneous HPC hardware with an absolute simulation performance between hundreds of nanoseconds to a microsecond a day.

Inputloutput speed continues to present a performance challenge for high-performance computing systems. This is because technology improves processor speed, memory speed and capacity, and disk capacity at a much higher-rate than mass storage latency. Developments in 1 1 0 architecture have been attempting to reduce this performance gap. The MasPar 110 architecture includes many interesting features. This work presents an experimental study of the dynamic characteristics of the MasPar parallel 110. Performance measurements were collected and compared for the MasPar MP-1 and MP-2 testbeds at NASA GSFC. The results have revealed strengths as well as areas for potential improvements and are helpful to software developers, systems managers, and system designers.

This paper covers the main subjects that have been discussed during the very interesting lecture of Change Management by Dr. Foster. This course consisted of severallectures where we discussed the theory in the first part and the second part of the lecture we could benefit of a guest lecture from a practitioner from the field. The variation of the lectures in combination with the presence of the professionals from the field gave us the perfect example of implementing the theory in practice. We have been familiarized with different theories and they possible outcomes of them. Every guest lecturers gave us their perception of change and change management and how they implemented change in their organizations. This made it possible for us to spot the similarities, differencesand best practices according to these organizations.

As the trends of process scaling make memory system even more crucial bottleneck, the importance of latency hiding techniques such as prefetching grows further. However, naively using prefetching can harm performance and energy efficiency and hence, several factors and parameters need to be taken into account to fully realize its potential. In this paper, we survey several recent techniques that aim to improve implementation and effectiveness of prefetching. We characterize the techniques on several parameters to highlight their similarities and differences. The aim of this survey is to provide insights to researchers into working of prefetching techniques and spark interesting future work for improving the performance advantages of prefetching even further.

Many of the basic image processing tasks suffer from processing overhead to operate over the whole image. In real time applications the processing time is considered as a big obstacle for its implementations. A High Performance Computing (HPC) platform is necessary in order to solve this problem. The usage of hardware accelerator make the processing time low. In recent developments, the Graphics Processing Unit (GPU) is being used in many applications. Along with the hardware accelerator a proper choice of the computing algorithm makes it an added advantage for fast processing of images. The Cellular Neural Network (CNN) is a large-scale nonlinear analog circuit able to process signals in real time . In this paper, we develop a new design in evaluation of image processing algorithms on the massively parallel GPUs with CNN implementation using Open Computing Language (OpenCL) programming model. This implementation uses the Discrete Time CNN (DT-CNN) model which is derived from originally proposed CNN model. The inherent massive parallelism of CNN along with GPUs makes it an advantage for high performance computing platform [2]. The advantage of OpenCL makes the design to be portable on all the available graphics processing devices and multi core processors. Performance evaluation is done in terms of execution time with both device (i.e. GPU) and host (i.e. CPU).

Abstract–In Cellular networks, number of users increase in an exponential manner. As a result of this exponential growth of users, overloading of the system takes place. Besides this, unacceptable delays and high computational cost create a strong prerequisite for efficient location management techniques. Motivated by this fact, a novel user profile based (UPB) scheme is proposed in this paper to track the location of the mobile users.

With the ubiquity and pervasiveness of mobile computing, together with the increasing number of social networks, end-users have learned to live and share all kinds of information about themselves. As an example, Facebook reports that it has currently 500 million active users, 200 million of which access its services on mobile systems; moreover, users that access Facebook through mobile applications are twice as active as non-mobile users, and it is used by 200 mobile operators in 60 countries [1]. More specific mobile platforms such as Foursquare, which unlike Facebook only collects location information, reports 6.5 million users worldwide, and also has a mobile presence (both with a web application and iPhone / Android applications) [2]. Contextaware architectures intend to explore this increasing number of context information sources and provide richer, targeted services to end-users, while also taking into account arising privacy issues. While multiple context management platform...

Real time systems are defined as the systems which are bound to time constraints and if not fulfilled lead to catastrophic results. In the present technological era, more and more real time systems are making their way through even in the commercial sphere in the form of satellites, auto pilot systems, ecommerce applications and many more. This increasing utility of real time systems requires them to be more and more fault tolerant. This paper gives a brief account of ‘Information Redundancy’ as a measure of fault tolerance in real time systems and concludes by giving the future perspectives in the field.

As small unmanned aircraft Systems UASs are emerging from the horizon, it is uplifting to see
technology saving lives because the AEC industry is one of the world’s deadliest jobs, according to
Forbes (Smith, 2014). Consequently, by reducing exposure and preemptively targeting the inspection
pipeline process, aerial surveying is a new market with the potential to pioneer solutions for surveying
construction sites and relaying as-built information to headquarters. The ability to immediately see,
hear, and communicate allows companies to optimize a strategy for economically and safely
prioritizing tasks to laborers, from automated safety checks, RFID tagging, thermal/volumetric scans,
to photogrammetry.

Therefore, this paper presents a guideline to prepare façade specialists in commercial UAV
operation and technically evaluate and explore the main challenges in addressing ‘autonomous flight
and integration of collected data into the existing BIM software’ inspired by (Dupont, 2017). As there
is no definite protocol for façade surveying, the goal is to define the limitations of DJI’s latest drone,
Matrice 210, equipped with upgraded IMU/GPS & camera sensors, and to confirm accuracies for
1mm,2mm,4mm, and 10 mm ground sampling distance (GSD). Through photogrammetry, the survey
will compare ground control points (GCPs) with a TS/TSL surveyor and transform photographs into an
ortho-mosaic 3D model, and lastly contribute a program to discuss BIM interoperability. As managing
the façade sector requires processing large quantities of data and different installation constraints,
the paper’s second object is to create a tool using BIM/KBE/MDO to link the model to Rhino-
Grasshopper-Flux-Dynamo-Revit workflow and assess the façade & fabrication challenges suggested
by (Voss, 2013).
With this extensive guideline, the study concludes (1) that photogrammetry is acceptable using
2mm & 4mm GSD, (2) while Lidar requires further integration for aerial surveying. This scenario to
capture the real world for AEC values will increase transparency between as-built and as-drawn by the
designer/contractors and enhance the protocol for façade surveys.

A grid computing environment provides a type of distributed computation that is unique because it is not centrally managed and it has the capability to connect heterogeneous resources. A grid system provides location-independent access to the resources and services of geographically distributed machines. An essential ingredient for supporting location-independent computations is the ability to discover resources that have been requested by the users. Because the number of grid users can increase and the grid environment is continuously changing, a scheduler that can discover decentralized resources is needed. Grid resource scheduling is considered to be a complicated, NP-hard problem because of the distribution of resources, the changing conditions of resources, and the unreliability of infrastructure communication. Various artificial intelligence algorithms have been proposed for scheduling tasks in a computational grid. This paper uses the imperialist competition algorithm (ICA) to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan relative to the others.  Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms.

Parallel computing has become most important issue right this time but because of the high cost of supercomputer it is not accessible for everyone. Cluster is the only technique that provides parallel computing, scalability and high availability at low cost. Collection of personal computers (PCs) builds a cluster that provides us parallel execution. High Performance Computing (HPC) is the field of computer science that emphases on making of cluster computers, supercomputers and parallel algorithms. At this present time, clusters technique has practical in numerous areas, for instance scientific calculations, weather forecasting, bioinformatics, signal processing, petroleum exploration and so on. This paper compares the two different types of clusters to check the overall performance in execution time. One cluster is made up of Dell core 2 duo systems and second cluster is made up of HP core 2 duo systems each with two nodes having almost same configurations. To analyze the performance of these two clusters we have executed two different parallel programs on the clusters for pi calculation and quick sort with different problem sizes. We observed that with small size problems Dell cluster performed better against HP cluster while with large size problems HP cluster won the game.

This article discusses the application of high performance computing (HPC) to the remote processing of high quality image based models of rock carvings in the eld, using Agisoft PhotoScan / Metashape Professional software. Most work to date has been carried out on the Danish e-Infrastructure Collaboration National High Performance Computer Abacus 2.0, hosted as part of the eScience Centre at The University of Southern Denmark (SDU). More recently, the author has been testing the cloud processing facility available within Metashape Professional, that utilizes Amazon Web Services (AWS) EC2 HPC cluster. This work forms part of the fieldwork component of the author’s PhD project at Aarhus University, Denmark, investigating regionality in Southern Tradition rock art through a programme of extensive documentation of rock carvings, principally on the island of Bornholm, Denmark, and also parts of Central and Southern Norway and Western Sweden. The successful deployment of this technology as part of a programme of surface based documentation breaks new ground in the fields of rock art research, archaeology, and high performance computing in equal measure. Therefore, the workflow discussed in the following pages has wide ranging implications for a number of disciplines.

URL: http://www.rockartscandinavia.com/the-application-of-high-performance-computing-in-rock-art-documentation-and-research-by-james-dodd-aa138.php