Abstract—Today, the combination of computing power and fast networks provides a workable solution to solve complex problems by using computational clusters. Java is considered one of the most popular languages for writing platform independent applications. There are many High Performance Computing (HPC) frameworks available for writing parallel applications in Java mostly using synchronous mode of communication. This paper presents a new easy-to-use message passing framework. This framework facilitates writing parallel code in Java for hybrid architectures using different standard protocols. It also supports blocking and pure asynchronous communication mode. The communication layer of the proposed message passing framework is based on Java Messaging Services (JMS). JMS provides asynchronous functionality of code distribution and message passing between processes running on different machines. A set of easy to use Application Programming Interfaces (APIs) are also provided to write MPI like code for parallel applications. The performance analysis of remote code execution is also included along with the comparison study of the proposed framework with MPJ Express and Java Threads on multi-core systems. The benchmark tests for the network latency and code speed-up have shown promising results. Keywords-HPC; Java; JMS; MPI; Parallel Code Execution I. INTRODUCTION Since long time, clusters are used to build powerful high performance computing systems. Yet, for more complex and compute intensive problems, still it is a challenge to provide required computer resources under one roof. Thus, the parallel systems like clusters have evolved into computational grids [1]. The Grid consists of clusters that are geographically dispersed and ... ... middle of paper ... ...rm for Java,” in Proc. 12th European PVM/MPI Users’ Group Meeting (EuroPVM/MPI’05), Lecture Notes in Computer Science, vol. 3666, pp. 217-224, Sorrento, Italy, 2005. [26] S. Bang and J. Ahn, “Implementation and Performance Evaluation of Socket and RMI based Java Message Passing Systems,” in Proc. 5th Intl. Conf. on Software Engineering Research, Management and Applications (SERA'07), pp. 153-159, Busan, Korea, 2007. [27] G. L. Taboada, J. Tourino, and R. Doallo, “F-MPJ: scalable Java message-passing communications on parallel systems,” Journal of Supercomputing, 2009. [28] TIOBE homepage [Online]. Available: http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html [29] B. Carpenter (2002) MPJ specification (mpijava 1.2 : API Specification) homepage on HPJAVA. [Online]. Available: http://www.hpjava.org/reports/mpiJava-spec/mpiJava-spec/mpiJava-spec.html
Cloud is the result of a decade research in the field of distributing computing , utility computing, virtualization , grid computing and more recently software, network services and web technology which is changeable evolution on demanding technology and services also as looking to the rapid growth of cloud computing which have changed the global computing infrastructure as well as the concept of computing resources toward cloud infrastructure. The important and interest of cloud computing increasing day by day and this technology receives more and more attention in the world (Jain, 2014) the mostly widely used definition of cloud computing is introduced by NIST “as a model for enabling a convenient on demand network access
Dir. Mark Herman. 2008. The 'Standard' of the 'Standard'.
Over the years, computer science kept evolving; leading to the emergence of what has become a standard in modern software development: Multitasking. Whether logical or physical, it has become a requirement for today's programs. In order to make it possible it became necessary to establish the notion of concurrency and scheduling. In this essay, concurrency will be discussed as well as two types of scheduling; pre-emptive used in threads and cooperative used in agents, their similarities and differences.
The brain functions as the epicenter of the nervous system, similar to the way in which the nervous system acts as the command center of the body. The brain is believed to be the most complex organ in the entire body; with the cerebral cortex being the largest system of the brain. The cerebral cortex contains billions of neurons and the neurons are regulated by synapses which are responsible for communication between other neurons. The communication process of neurons is facilitated by the axon or axon fibers which relay signals or action potentials to the parts of the brain and body, generating either a motor or sensory response and in most cases both. A primary role that the brain serves is translating sensory information into bodily
Why NetBatch? At my workplace, we have way more computing needs for the number of machines we own. Hence, it would be economically infeasible to buy enough machines to satisfy our peak consumption, which is growing constantly. NetBatch is a tool, which allows our organization to maximize utilization of the available computing resources. This paper discusses about NetBatch and NBS, a package around NetBatch that handles job management, which use principles of queuing, job scheduling, sequencing to achieve its goals.
Recently C++ has made its way into the Internet community. For over ten years, business’ have used C++ for their Internet needs, for example, sending and receiving important data pertaining to their business across the Internet and allowing it to quickly and safely reach the other end of communication and all in one piece. With the high demands of today’s Internet users, whether it be an online shopper or one that desires to seek information on a certain topic, it is essential that the information can be sent from the user, to the server, and then back again as swiftly as possible and with utmost dependability… all, of course, without the loss of security.
Simultaneous multithreading ¡ª put simply, the shar-ing of the execution resources of a superscalar processor betweenmultiple execution threads ¡ª has recently become widespread viaits introduction (under the name ¡°Hyper-Threading¡±) into IntelPentium 4 processors. In this implementation, for reasons of ef-ficiency and economy of processor area, the sharing of processorresources between threads extends beyond the execution units; ofparticular concern is that the threads share access to the memorycaches.We demonstrate that this shared access to memory caches pro-vides not only an easily used high bandwidth covert channel be-tween threads, but also permits a malicious thread (operating, intheory, with limited privileges) to monitor the execution of anotherthread, allowing in many cases for theft of cryptographic keys.Finally, we provide some suggestions to processor designers, op-erating system vendors, and the authors of cryptographic software,of how this attack could be mitigated or eliminated entirely.1. IntroductionAs integrated circuit fabrication technologies have improved, provid-ing not only faster transistors but smaller transistors, processor design-ers have been met with two critical challenges. First, memory latencieshave increased dramatically in relative terms; and second, while it iseasy to spend extra transistors on building additional execution units,many programs have fairly limited instruction-level parallelism, whichlimits the extent to which additional execution resources can be uti-lized. Caches provide a partial solution to the first problem, whileout-of-order execution provides a partial solution to the second.In 1995, simultaneous multithreading was revived1in order to com-bat these two difficulties [12]. Where out-of-order execution allowsinstructions to be reordered (subject to maintaining architectural se-mantics) within a narrow window of perhaps a hundred instructions,Key words and phrases. Side channels, simultaneous multithreading, caching.1Simultaneous multithreading had existed since at least 1974 in theory [10], evenif it had not yet been shown to be practically feasible.
In my opinion, the major potential in parallel computing lies in the software part. Hardware architectures have been constantly evolving since the last 40 years and sooner or later saturation may start. The number of transistors cannot keep increasing forever. Even though software has evolved, it’s still not up to pace. There is a dearth of programmers trained to design and program parallel systems. Intel recently launched Parallel Computing Center program with the main purpose as “keeping the parallel software in sync with the parallel hardware”. The international community needs to develop the parallel programming skills to keep pace with the new processors being created. As this realization spreads, the parallel architectural landscape will touch even greater heights than expected.
Murakami, K.; Inoue, K.; and Miyajima, H.; “Parallel Processing RAM (PPRAM) (in English),” Japan-Germany Forum on Information Technology, Nov. 1997.
Distributed systems are grouping of computers linked through a network that uses software to coordinate their resources to complete a given task. The majority of computer systems in use today are distributed systems. There are limited uses for a singular software application running on an unconnected individual hardware device. A perfect distributed system would appear to be a single unit. However, this ideal system is not practical in real world application due to many environmental components. There are many attributes to consider when designing and implementing distributed systems. Distributed Software Engineering is the implementation of all aspects of software production in the creation of a distributed
Cloud computing technology is a very known and popular paradigm in the field of Information technology. It is an incipient computing model which emanates from grid computing which is resulting into an emerging paramount concept in Information Technology. Vast number of operating systems and virtual servers are mutually dependent through the internet and allow sharable resources with each other. This generates expeditious and efficient computing speed. The concept of cloud computing is predicated on the time sharing of expensive resources and benefits of the providence of scale. The word Cloud originates from the well-known cloud shape which is expressed as a network in architectural system diagrams. Cloud computing applies traditional supercomputing to provide tremendous throughput computing power. Cloud computing allows user to execute simulated applications on a virtual server. In this report, we discuss about cloud computing technologies for example “Distributed File System (DFS), Map-Reduce, and Big tab”. [3] This cloud computing architecture is designed for, geographic information Services which contains functional utilization, perceptions, benefits, computing reserving data and infrastructure layers. We also discuss about software backdrops called as “D cloud”. D-Cloud provides a condition for analysis on the cloud elements using a particular structural configuration and implements large number of evaluations automatically as per the scenario. We also discuss about combining Peer to peer systems and technologies related cloud computing in order to construct a blueprint of the architecture and establish PC2, an open and free cloud computing platform. [4]
Testing One Two. By: Nisley, Ed. Dr. Dobb's Journal: Software Tools for the Professional Programmer, May2003, Vol. 28 Issue 5, p80, 4p, 1c; (AN 9457433)
Data center, in the context of big data, is not only for data storage but it plays significant role to acquire, manage and organize the big data. Big data has uncompromising requirement for storage and processing capacity. Hence the data center development should be the focus for effective and rapid processing capacity. With the increasing scale of data centers, the operational cost should be reduced for the development of data centers. Today’s data centers are application-centric, powering the many business applications, standalone websites and e-commerce offerings on the web. Tomorrow’s data centers need to be data centric: storage and infrastructure capacity must be expanded to support IoT/Big Data-generated information. This also affects future bandwidth in data centers as resources will be mostly consumed by IoT sensors and machines, as opposed to user activity and behaviour.
Multithreading is the ability of an operating system to run programs concurrently that are divided into sub parts or threads. It is similar to multitasking but instead of running on multiple processes concurrently, multithreading allows multiple threads in a process to run at the same time. Threads are more basic and smaller unit of instruction. Hence multithreading can occur within a single process. Multithreading can also be defined as a combination of microprocessor design and machine code which allows computer instructions to be carried out concurrently and the results to be combined in right logical order. Programs can execute multiple tasks simultaneously by incorporating multithreading. The real purpose of multithreading is to help in proper and resource effective utilization of the hardware and software resources. Multithreading provides concurrency as it enables many programs to run in parallel and execute simultaneously thus saving time and providing efficiency (Ball et al., 2011).