...new technologies after 2004 has re-enabled anything like the scaling in the 90s. From 2007 to 2011, maximum CPU clock speed (with Turbo Mode enabled) rose from 2.93GHz to 3.9GHz, an increase of 33%. From 1994 to 1998, CPU clock speeds rose by 300%. CPU manufactures hence were forced to shift towards increasing processor count per die. Most processors today have at least 4 cores, while the higher end machines commonly have 16 cores or more. This brought in the era of multicore computing. However, increasing the number of cores does not necessarily mean faster processing. The software written for the multi core computers of today must be able to utilize the additional processing power resident in the extra cores. Today’s software therefore has to be written for multicore computers, and sequential modes of computation in software must be replaced by parallel processing to harness the power of the additional cores. However, most software in the world, excluding specialized cases of high performance computing and ADA, has always been sequential in nature. The sudden influx of multicores meant a complete new paradigm of programming, and while a considerable work has been done in this regard, programming multicores still...
Words: 1388 - Pages: 6
...krafft@gmx.net Abstract This paper analyses the requirements of performing parallel transaction-oriented simulations with a special focus on the space-parallel approach and discrete event simulation synchronisation algorithms that are suitable for transaction-oriented simulation and the target environment of Ad Hoc Grids. To demonstrate the findings a Java-based parallel transaction-oriented simulator for the simulation language GPSS/H is implemented on the basis of the most promising Shock Resistant Time Warp synchronisation algorithm and using the Grid framework ProActive. The validation of this parallel simulator shows that the Shock Resistant Time Warp algorithm can successfully reduce the number of rolled back Transaction moves but it also reveals circumstances in which the Shock Resistant Time Warp algorithm can be outperformed by the normal Time Warp algorithm. The conclusion of this paper suggests possible improvements to the Shock Resistant Time Warp algorithm to avoid such problems. 1. Introduction The growing demand of complex Computer Simulations for instance in engineering, military, biology and climate research has also lead to a growing demand in computing power. One possibility to reduce the runtime of large, complex Computer Simulations is to perform such simulations distributed on several CPUs or computing nodes. This has induced the availability of highperformance parallel computer systems. Even so the performance of such systems has constantly increased...
Words: 4076 - Pages: 17
...Software Engineering COMPSCI 711 - Parallel and Distributed Computing http://www.cs.swarthmore.edu/~newhall/cs87/s10/ Course Description This course covers a broad range of topics related to parallel and distributed computing, including parallel and distributed architectures and systems, parallel and distributed programming paradigms, parallel algorithms, and scientific and other applications of parallel and distributed computing. In lecture/discussion sections, students examine both classic results as well as recent research in the field. The lab portion of the course includes programming projects using different programming paradigms, and students will have the opportunity to examine one course topic in depth through an open-ended project of their own choosing. Course topics may include: multi-core, SMP, MMP, client-server, clusters, clouds, grids, peer-to-peer systems, GPU computing, scheduling, scalability, resource discovery and allocation, fault tolerance, security, parallel I/0, sockets, threads, message passing, MPI, RPC, distributed shared memory, data parallel languages, MapReduce, parallel debugging, and applications of parallel and distributed computing. Class will be run as a combination of lecture and seminar-style discussion. During the discussion based classes, students will read research papers prior to the class meeting that we will discuss in class. During the first part of the course, we will examine different parallel and distributed programming paradigms...
Words: 765 - Pages: 4
...Abbreviated version of this report is published as "Trends in Computer Science Research" Apirak Hoonlor, Boleslaw K. Szymanski and M. Zaki, Communications of the ACM, 56(10), Oct. 2013, pp.74-83 An Evolution of Computer Science Research∗ Apirak Hoonlor, Boleslaw K. Szymanski, Mohammed J. Zaki, and James Thompson Abstract Over the past two decades, Computer Science (CS) has continued to grow as a research field. There are several studies that examine trends and emerging topics in CS research or the impact of papers on the field. In contrast, in this article, we take a closer look at the entire CS research in the past two decades by analyzing the data on publications in the ACM Digital Library and IEEE Xplore, and the grants awarded by the National Science Foundation (NSF). We identify trends, bursty topics, and interesting inter-relationships between NSF awards and CS publications, finding, for example, that if an uncommonly high frequency of a specific topic is observed in publications, the funding for this topic is usually increased. We also analyze CS researchers and communities, finding that only a small fraction of authors attribute their work to the same research area for a long period of time, reflecting for instance the emphasis on novelty (use of new keywords) and typical academic research teams (with core faculty and more rapid turnover of students and postdocs). Finally, our work highlights the dynamic research landscape in CS, with its focus constantly ...
Words: 15250 - Pages: 61
...HadoopJitter: The Ghost in the Cloud and How to Tame It Vivek Kale∗ , Jayanta Mukherjee† , Indranil Gupta‡ , William Gropp§ Department of Computer Science, University of Illinois at Urbana-Champaign 201 North Goodwin Avenue, Urbana, IL 61801-2302, USA Email: ∗ vivek@illinois.edu, † mukherj4@illinois.edu, ‡ indy@illinois.edu, § wgropp@illinois.edu Abstract—The small performance variation within each node of a cloud computing infrastructure (i.e. cloud) can be a fundamental impediment to scalability of a high-performance application. This performance variation (referred to as jitter) particularly impacts overall performance of scientific workloads running on a cloud. Studies show that the primary source of performance variations comes from disk I/O and the underlying communication network [1]. In this paper, we explore the opportunities to improve performance of high performance applications running on emerging cloud platforms. Our contributions are 1. the quantification and assessment of performance variation of data-intensive scientific workloads on a small set of homogeneous nodes running Hadoop and 2. the development of an improved Hadoop scheduler that can improve performance (and potentially scalability) of these application by leveraging the intrinsic performance variation of the system. In using our enhanced scheduler for data-intensive scientific workloads, we are able to obtain more than a 21% performance gain over the default Hadoop scheduler. I. I NTRODUCTION Certain...
Words: 7930 - Pages: 32
...E.g. in neurophysiological clinics, it is impractical for doctors to manually monitor hourly long EEG data of epileptic patients to observe for abnormalities. Another scenario is when the patient is on his/her own and gets a seizure. Automatic seizure detection in combination with alarm signal can be used to alert the patient or a relative regarding the seizure. In these scenarios we have to analyze the data as soon as it is generated, the strategy commonly known as online analysis. In online analysis we have to make decisions and provide results with negligible delay. Due to this reason, very complex and compute intensive algorithms cannot be used for seizure detection on real...
Words: 840 - Pages: 4
...Demo Script Parallel Computing on Azure - Travelling Salesman Demo Demo version: 1.0.0 Last updated: 12/7/2011 Contents Overview 3 Key Messages 4 Key Technologies 4 Prerequisites 4 Time Estimates 5 Setup and Configuration 6 Demo Flow 7 Opening Statement 9 Step-by-Step Walkthrough 10 Segment #1: Scaling-Up Windows Azure Applications using a Single Instance 10 Segment #2: Scaling-Out Windows Azure Applications using Multiple instances 17 Summary 24 * Overview This demo highlights how to scale-up Web Applications on Windows Azure, using the .NET Task Parallel Library (TPL) classes from .NET Framework 4.0. This library efficiently utilizes multiple processors within Windows Azure roles, where the size of the Virtual Machine instance is greater than Small (i.e. where there are multiple processors available). Additionally, the demo shows how to scale-out applications taking advantage of Technical Computing across multiple role instances, using a Job scheduling algorithm. The work is distributed to all the available instances, maximizing the CPU processing of each. Travelling Salesman demo is using a “genetic” algorithm to quickly solve the problem that would ordinarily require very many conventional interactions to solve. The problem and its real–life applications are widely documented (for example, see http://www.tsp.gatech.edu/index.html). The algorithm used in this demo was taken from http://www.heatonresearch.com/online/introduction–neural–networks–cs–edition–2/chapter–6...
Words: 3071 - Pages: 13
...Chapter 1 Parallel Computer Models Prof. D. P Theng . GHRCE TAE TAE - I TAE - II TAE Components Quiz Test Assignment Date of Submission Second week- July 2013 Sept 2013 TAE - III TAE - IV TAE - V TAE - VI Technical Presentation Attendance PPT on Paper Review Chapter Review Fourth Week- July 2013 Sept 2013 First week- Aug 2013 Fourth Week- Aug 2013 Sept 2013 TAE - VII Guest Lecture/Industrial Visit Early computing was entirely mechanical: abacus (about 500 BC) mechanical adder/subtracter (Pascal, 1642) difference engine design (Babbage, 1827) binary mechanical computer (Zuse, 1941) electromechanical decimal machine (Aiken, 1944) Mechanical and electromechanical machines have limited speed and reliability because of the many moving parts. Modern machines use electronics for most information transmission. Computing is normally thought of as being divided into generations. Each successive generation is marked by sharp changes in hardware and software technologies. With some exceptions, most of the advances introduced in one generation are carried through to later generations. We are currently in the fifth generation. Technology and Architecture Vacuum tubes and relay memories CPU driven by a program counter (PC) and accumulator Machines had only fixed-point arithmetic Software and Applications Machine and assembly language Single user at a time No subroutine linkage...
Words: 2199 - Pages: 9
...Graduate Student, DOE Computational Science Graduate Fellow 657 Rhodes Hall, Ithaca, NY, 14853 September 19, 2011 sc932@cornell.edu cam.cornell.edu/∼sc932 Education Cornell University Ph.D. Applied Math (current), M.S. Computer Science Ithaca, NY 2008 - 2012(projected) • – Department of Energy Computational Science Graduate Fellow (Full Scholarship, 4 years) – Emphasis on machine learning/data mining and algorithm design/software development related to bioinformatics and optimization • Oregon State University B.Sc. Mathematics, B.Sc. Computational Physics, B.Sc. Physics Corvallis, OR 2004 - 2008 – Graduated Magna Cum Laude with minors in Actuarial Sciences and Mathematical Sciences – Strong emphasis on scientific computing, numerical analysis and software development Skills • Development: C/C++, Python, CUDA, JavaScript, Ruby (Rails), Java, FORTRAN, MATLAB • Numerical Analysis: Optimization, Linear Algebra, ODEs, PDEs, Monte Carlo, Computational Physics, Complex Systems, Iterative Methods, Tomology • Computer Science: Machine Learning, Data Mining, Parallel Programming, Data Structures, Artificial Intelligence, Operating Systems • Discovering and implementing new ideas. Give me an API and a problem and I will figure it out. • Diverse background in Math, Computer Science, Physics and Biology allows me to communicate to a wide scientific and general audience and begin contributing to any group immediately. • I have worked in many places in a myriad of fields. I can readily...
Words: 673 - Pages: 3
...For the MASSES AUTHOR Havirdhara B.Tech III-II year AFFILIATIONS: KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY AFFILIATED TO JNTU HYDERABAD E-Mail:- havirdhara@gmail.com ABSTRACT: In this age of super-computing, the demand for the extremely high speed processors is surging. This ever-increasing demand forces us to go for the high speed Multi-core processors. Multi-core processors are no longer the future of computing-they are the present day reality. With the rise of multi-core architectures the question of the hour is: how to program massively parallel processors. Nvidia, the pioneer in GPU design, has come up with an advanced user-friendly architecture, “THE CUDA” that enables dramatic increases in computing performance by harnessing the power of the GPU (graphics processing unit). CUDA (an acronym for COMPUTE UNIFED DEVICE ARCHITECTURE) reduces the complexity of the parallel programming to a great extent. The best feature of CUDA is that we can program the GPUs using C, JAVA and other high level programming environments. In this paper, we present the basics of CUDA programming with the need for the evolution of the same. This paper also presents the different applications of CUDA, which tells us why and how CUDA scores over other parallel programming architectures. Introduction: Parallelism is the age old technique used for the efficient data processing. The same technique re-emerging into TLP i.e., Thread Level Parallelism and in combination with some...
Words: 1310 - Pages: 6
...applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to address growing size and increasing variety of organizations’ data, including file-based, MapReduce (e.g. Hadoop) or In-Database Analytics. Second, the company is optimizing algorithms - even complex ones - to work well with Big Data. Open Source R was not built for Big Data Analytics because it is memory-bound...
Words: 1996 - Pages: 8
...3 Elective -I Digital Control Systems Distributed Operating Systems Cloud Computing 3 0 3 Elective -II Digital Systems Design Fault Tolerant Systems Advanced Computer Networks 3 0 3 Lab Micro Processors and Programming Languages Lab 0 3 2 Seminar - - 2 Total Credits (6 Theory + 1 Lab.) 22 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD MASTER OF TECHNOLOGY (REAL TIME SYSTEMS) I SEMESTER ADVANCED COMPUTER ARCHITECTURE UNIT I Concept of instruction format and instruction set of a computer, types of operands and operations; addressing modes; processor organization, register organization and stack organization; instruction cycle; basic details of Pentium processor and power PC processor, RISC and CISC instruction set. UNIT II Memory devices; Semiconductor and ferrite core memory, main memory, cache memory, associative memory organization; concept of virtual memory; memory organization and mapping; partitioning, demand paging, segmentation; magnetic disk organization, introduction to magnetic tape and CDROM. UNIT III IO Devices, Programmed IO, interrupt driver IO, DMA IO modules, IO addressing; IO channel, IO Processor, DOT matrix printer, ink jet printer, laser printer. Advanced concepts; Horizontal and vertical instruction format, microprogramming, microinstruction sequencing and control; instruction pipeline; parallel processing; problems in parallel processing; data hazard, control hazard. UNIT IV ILP software approach-complier...
Words: 3183 - Pages: 13
...Computer science From Wikipedia, the free encyclopedia Jump to: navigation, search Computer science or computing science (abbreviated CS) is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems.[1][2] Computer scientists invent algorithmic processes that create, describe, and transform information and formulate suitable abstractions to model complex systems. Computer science has many sub-fields; some, such as computational complexity theory, study the fundamental properties of computational problems, while others, such as computer graphics, emphasize the computation of specific results. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to describe computations, while computer programming applies specific programming languages to solve specific computational problems, and human-computer interaction focuses on the challenges in making computers and computations useful, usable, and universally accessible to humans. The general public sometimes confuses computer science with careers that deal with computers (such as information technology), or think that it relates to their own experience of computers, which typically involves activities such as gaming, web-browsing, and word-processing. However, the focus of computer science is more on understanding the properties of the programs used to implement...
Words: 5655 - Pages: 23
...Today data security plays an important role in software domain and quality of service and confidence, Cloud computing [1] focuses a new challenging security threats. Therefore, a data security model must solve the most challenges of cloud computing security issues [2]. Using Internet as the backbone, cloud computing confirms that it is possible to provide resources as a “utility” to end users “as and when needed” basis [3]. Cloud Computing has some security issues such as access control, authentication and authorization [9] which requires a high-guaranteed security model [4, 5]. Biometric identification [6, 7] is a very good candidate technology which can facilitate a trusted user authentication with the minimum constraints on the security of the access point. However, most of the biometric identification techniques require special hardware thus complicate the access point and make it costly....
Words: 991 - Pages: 4
...2Associate Professor, Department of Computer Science, NGM College, Pollachi, India, Abstract —Due to trends like Cloud Computing and Green cloud Computing, virtualization technologies are gaining increasing importance. Cloud is a novel model for computing resources, which aims to computing infrastructure to the network in order to reduce costs of hardware and software resources. Nowadays, power is one of big issue of data centers has huge impacts on society. Researchers are seeking to find solutions to make data centers reduce power consumption. These IDC (Internet Data Center) consume vast amounts...
Words: 1888 - Pages: 8