Federation and Abstraction of Heterogeneous Global Computing Platforms with the YML Framework

Heterogeneous computing platforms with multicore host system and many-core accelerator devices have taken a major step forward in the mainstream HPC computing market this year with the announcement of HP Apollo 6000 System's ProLiant XL250a server features the Intel® Xeon Phi™ coprocessors. Although many application developers attempt to use it in the same way as GPGPU acceleration platforms, doing so forfeits the processing capability of multicore host processors and introduces power inefficiency in business operations. In this paper, we propose an application optimization framework to turn sequential legacy applications into highly parallel applications that make use of the hardware resources both on the host CPU and on the accelerator devices to enable simultaneous heterogeneous computing. As a case study, we look at how to apply this framework and adopt a structured methodology to develop option pricing applications to take advantages of a heterogeneous computing environment.

Biological pathways typically consist of upto hundreds of reacting chemical species and reactions within a biological system. Modeling and simulation of biological pathways in explicit process space is a computationally intensive, both due to the number of interactions and time-scale of processes. Traditional stochastic or ODE based simulation of chemical processes ignore spatial and biological information. Hence there is a need for new underlying simulation algorithms as well as need for newer computing systems, platforms and techniques. Such pathways describe exhibit considerable behavioral complexity in multiple fundamental cellular processes. In this work we present a new heterogeneous computing platform to accelerate the simulation study of such complex biochemical pathways in 3D reaction process space. Several tasks involved in the simulation study has been carefully partitioned to run on a combination of reconfigurable hardware and massively parallel processor such as the GPU. This paper also presents an implementation to accelerate one of the most compute intensive tasks - sifting through the reaction space to determine reacting particles. Finally, we present the new heterogeneous computing framework integrating a FPGA and GPU to accelerate the computation over the use of a any single platform. This framework can achieve 10-times speedup over a single GPU-only platform. Besides, the extensible architecture is general enough to be used to study a variety of biological pathways in order to gain deeper insights into biomolecular systems.

Future distribution grids will be subjected to fluctuations in voltages and power flows due to the presence of renewable sources with intermittent power generation. The advanced smart metering infrastructure (AMI) enables the distribution system operators (DSOs) to measure and analyze electrical quantities such as voltages, currents and power at each customer connection point. Various smart grid applications can make use of the AMI data either in offline or close to real-time mode to assess the grid voltage conditions and estimate losses in the lines/cables. The outputs of these applications can enable DSOs to take corrective action and make a proper plan for grid upgrades. In this paper, the process of development and deployment of applications for improving the observability of distributions grids is described, which consists of the novel deployment framework that encompasses the proposition of data collection, communication to the servers, data storage, and data visualization. This paper discussed the development of two observability applications for grid monitoring and loss calculation, their validation in a laboratory setup, and their field deployment. A representative distribution grid in Denmark is chosen for the study using an OPAL-RT real-time simulator. The results of the experimental studies show that the proposed applications have high accuracy in estimating grid voltage magnitudes and active energy losses. Further, the field deployment of the applications prove that DSOs can gain insightful information about their grids and use them for planning purposes.

Personal mobile devices, such as cell phones and PDAs, are undergoing continual improvements in computing power, storage, and connectivity. This trend presents many opportunities for information sharing and collaboration among massively distributed data sources. However, the current mobile environment, which consists of incompatible operating systems, development models, and communication options, has hindered application development and data sharing over ad hoc networks of mobile devices. In this paper, we present a middleware framework, called Infinity, that unifies techniques for local (and remote) device data access, automatic communication channel selection, and application deployment across heterogeneous platforms. Infinity enables information sharing and collaboration among mobile devices in a privacy-preserving manner. We describe the prototype implementation of Infinity and demonstrate how it eases application development and supports a wide variety of application scenarios.

Biological Sequence Comparison is an important operation in Bioinformatics that is often used to relate organisms. Smith and Waterman proposed an exact algorithm (SW) that compares two sequences in quadratic time and space. Due to high computing and memory requirements, SW is usually executed on HPC platforms such as multicore clusters and CellBEs. Since HPC architectures exhibit very different hardware characteristics, porting an application between them is an error-prone time-consuming task. BSP++ is an implementation of BSP that aims to reduce the effort to write parallel code. In this paper, we propose and evaluate a parallel BSP++ strategy to execute SW in multiple platforms like MPI, OpenMP, MPI/OpenMP, CellBE and MPI/CellBE. The results obtained with real DNA sequences show that the performance of our versions is comparable to the ones in the literature, evidencing the appropriateness and flexibility of our approach.

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

The cloud computing is nowadays an embarrassing computing technology by many organisations, academic institutions and business centres. Cloud Service Providers (CSP)s are limited to some resources, lacking some resources requested by its customers, this gives rise to the needs for interconnecting multiple clouds to interoperate and share resources. The interconnected clouds can be in different features and schemes, and the system can be prone to insecurity or intrusion. In this paper, we developed a Digital Forensics framework that can detect an intrusion within heterogeneous joint clouds. We also developed architecture and algorithm that can handle the joint clouds heterogeneity and complexity during inter-clouds resources management.

Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based Task Scheduling (SOATS) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost-based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy-GA (FUGE). In the heavily loaded environment, the SOATS algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

Many people use multiple online and social computing platforms, and choose to share varying amounts of personal information about themselves depending on the context and type of site. For example, people may be willing to share personally-identifiable details (including their real name and date of birth) on a site like Facebook, but may withhold their identity on a dating site that may be widely viewed by strangers. We study the extent to which subtle correlations in a user's activity patterns across different sites may be used to infer that two accounts correspond to the same person. We study a variety of features, including similarity of temporal access patterns, textual content, geo-tags, and social connections, finding that even very weak signals yield surprisingly accurate de-anonymization results.

People use personal data assistants in the field to collect data and to communicate with others both in the field and office. The individual in the office invariably has a laptop or a high-end personal workstation and thus, significantly more computing power, more screen real estate, and higher volume input devices, such as a mouse and keyboard. These differences give the high-end user the ability to represent and manipulate collaborative tasks more effectively. It is therefore useful to know what impact these differences have on work performance and work communications. Four different platform combinations involving a PC and a PDA were used to examine the effect of communicating via heterogeneous computer platforms. The PC platform used a mouse, a keyboard, and a 3-dimensional screen display. The PDA platform used a stylus, soft buttons, and a 2-dimensional screen display. A variation of the Tetris wall-building game called Slow Tetris was used as the subjects' collaborative task. A second factor in the experiment was role asymmetry. One subject was arbitrarily put in charge of the task solution in all of the combinations. An analysis of the solution times found that subjects with mixed platforms worked slower than their homogeneous counterparts, that is, a person in charge with a PC worked faster if his partner had a PC. An in-depth analysis of the communication patterns found significant differences in the exchanges between heterogeneous and homogenous combinations. The PC-to-PDA combination (with the person on the PC in charge of the solution) took significantly more time than the PC-to-PC combination. This extra time appears to come from the disadvantage of having a partner on the PDA who is unable to help in solving the problems. The PDA-to-PC combination took approximately the same amount of time as the PDA-to-PDA combination despite having one team member with a better representation. This member was, unfortunately, not in charge of the solution. The PDA-to-PC heterogeneous combination exhibited more direction giving, less one-sided collaboration, and more takeover attempts than any of the other combinations. Overall, roles were maintained in the partnerships except for the person with the PDA directing the person with the PC.

HOSVD prototype based on modular SW libraries running on a high-performance CPU+GPU platform

We describe the development of two parallel applications running on our PC cluster which consists of 64 Pentium Pro 200MHz microprocessors. One is an integrated parallel calculation system, called the PAPIA system, dedicated to protein information analysis. The PAPIA system enables very fast protein database searches (3-0 structure matching and sequence homology search) by fully utilizing the power of the 64 local hard-disks, as well as fast parallel calculations supported by the high-bandwidth and low-latency communication driver developed by RWCR We have starteda WWWsewice (http://www. rwcp.oKjp/papid) so any biologist can easily submit jobs to the PAPIA system through a WWW browse,: The service has been accessed from 55 countries in the world. The other application is parallel molecular dynamics simulation. We have eficiently ported the AMBER program to the PC cluster; and then appended an optional functionality of the Barnes-Hut tree code for quick and accurate calculation of Coulomb potentials without a cut-off approximation (PPPC method). The RWC PC Cluster shows good scalability in these two real parallel applications.

In this paper, the problem of parallel loop scheduling for heterogeneous networks of workstations is discussed. A practical network of workstations is heterogeneous where computing power varies in different composing workstations. A simple loop scheduling technique is insufficient in exploiting maximum computing power of such a system. We propose a fundamental idea for a performance prediction tool to gauge the relative computing power among composing workstations so that parallel performance of a program run on a given network can be predicted. In view of the performance prediction results, a loop scheduling approach is then incorporated into the system to achieve close-to-optimal parallel performance. Examples running benchmark programs demonstrate a significant gain from the proposed approach over traditional scheduling approaches.

A new tool and web portal are presented for deployment of High Performance Computing applications on distributed heterogeneous computing platforms. This tool relies on the decentralized environment P2PDC and the OMF and OML multithreaded control, instrumentation and measurement libraries. Deployment on PlanetLab of a numerical simulation application is studied. A first series of computational results is displayed and analyzed.

The chaotic growth of the IoT determined a fragmented landscape with a huge number of devices, technologies, and platforms available on the market, and consequential issues of interoperability on many system deployments. The Web of Things (WoT) architecture recently proposed by the W3C consortium constitutes a novel solution to enable interoperability across IoT Platforms and application domains. At the same time, in order to see an effective improvement, a wide adoption of the W3C WoT solutions from the academic and industrial communities is required; this translates into the need of accurate and complete support tools to ease the deployment of W3C WoT applications, as well as reference guidelines about how to enable the WoT on top of existing IoT scenarios and how to deploy WoT scenarios from scratch. In this thesis, we bring three main contributions for filling such gap: (1) we introduce the WoT Store, a novel platform for managing and easing the deployment of Things and applications on the W3C WoT, and additional strategies for bringing old legacy IoT systems into the WoT. The WoT Store allows the dynamic discovery of the resources available in the environment, i.e. the Things, and to interact with each of them through a dashboard by visualizing their properties, executing commands, or observing the notifications produced. (2) We map three different IoT scenarios to WoT scenarios: a generic heterogeneous environmental monitoring scenario, a structural health monitoring scenario and an Industry4.0 scenario. (3) We make proposals to improve both the W3C standard and the node-wot software stack design: in the first case, new vocabularies are needed in order to handle particular protocols employed in industrial scenarios, while in the second case we present some contributions required for the dynamic instantiation and the migration of Web Things and WoT services in a cloud-to-edge continuum environment.