We present a simple multiple view 3D model for object tracking and identification in camera networks. Our model is composed of 8 distinct views in the interval [0, 7pi/4]. Each of the 8 parts describes the person's appearance from that particular viewpoint. The model contains both color and structure information about each view which are assembled into a single entity and is meant as a simple, lightweight object representation for use in camera sensor networks. It is versatile in that it can be gradually assembled on-line while a person is tracked. The model's ease of use and effectiveness for identification in surveillance video is demonstrated.

Case-based analysis is a bottom-up methodology for ethical reasoning. In a teaching environment, it can be a useful alternative to top-down methods, such as Kantianism, utilitarianism, rights-based theories, and virtue ethics. We describe a methodology for case-based analysis and illustrate the methodology by evaluating three test cases in the areas of Internet etiquette, intellectual property, and privacy, respectively. We summarize the strengths and weaknesses of using the case-based approach in a classroom setting.

We have developed a compiler, nicknamed "Otter", which translates ordinary MATLAB scripts into C programs targeting parallel computers supporting ScaLAPACK and several other parallel numerical libraries. We outline the phases of the multi-pass compiler and we discuss the design and implementation of the runtime library. We present the performance achieved by ten compiled benchmark scripts on four distinct parallel platforms, relative to the speed of the MATLAB interpreter executing these scripts on a single CPU. Finally, we illustrate how different data distributions and block sizes can affect the performance of a compiled code.

One-to-all broadcast is frequently implemented on multicomputers using a binomial tree communication pattern. The source processor, at the root of the broadcast tree, completes the broadcast before the processors at the leaves of the tree. This imbalance can degrade performance when a data-parallel algorithm requires a series of broadcasts from different source processors. In this paper, we show that using a reflected Gray code to order broadcasts minimizes the communication time.

We describe a software environment for high performance distributed computing on a network of multiprocessor workstations. In designing this environment, we have used a problem oriented approach as opposed to the traditional algorithm oriented approach. This paradigm shift enables us to generate efficient programs automatically for a well defined class of problems. Thus, our system frees the users from the esoteric tasks of algorithm design and implementation. An important feature of our system is its ability to handle the large variation in granularity-we call this dual level parallelism-in a hybrid processing environment. This feature is the key to the superior efficiency delivered by the system. We give preliminary results from a case study in which our system is used to generate programs automatically for a scientific application, with a network of multiprocessors as the target platform.

We describe an architecture-adaptable methodology for the parallel implementation of finite element numerical models of physical systems. We use a model of time-dependent ocean currents as our working example. The heart of the computation is the solution of a banded linear system, and we describe an algorithm based on the domain decompositionmethod to solve the banded system. The algorithm is represented in a divide-and-conquer framework facilitates easy implementation of various algorithmic options. The process is straightforward and amenable to automation. We demonstrate the validity of this approach using two radically different target machine, a workstation network and a supercomputer. Our results show very good speedup on both platforms.

Abstract With the increased performance capabilities of desktop computers, networked computing has become a popular vehicle for using parallelism to solve a variety of computationally intense problems. However, node heterogeneity and high communication costs may limit performance unless the problem space is carefully partitioned across the network in a way that considers both the capabilities of the machines and the high network communication costs. We describe an advisory system that is designed to help the programmer, compiler or run‐time environment choose the best decomposition strategy for partitioning specific data‐parallel applications across a given collection of machines. The system includes provisions for assessing the capabilities of the participating machines and the network in light of the current workload. Given information about the problem space, the machine speeds and the network, the system provides a ranking of three standard partitioning methods. We test the validity of our system by comparing the observed relative performance with predicted relative performance of different data decompositions on a program with a variable number of floating point operations and a 5‐point stencil communication pattern.

Networked computing has become a popular method for using parallelism to solve a variety of computationally intense problems. However, high communication costs and processor heterogeneity may limit performance unless the problem space is carefully partitioned. We propose a decomposition advisory system that is designed to help choose the best data partitioning strategy. The goal of this research is to determine the partitioning scheme(s) expected to yield the best performance for a particular data-parallel problem with known regular communication patterns on a collection of heterogeneous processors. Given information about the problem space and the network, the system provides a ranking of standard partitioning methods.>

The authors present a block data decomposition algorithm for two-dimensional grid problems. Their method includes local balancing to accommodate heterogeneous processors, and they characterize the conditions that must be met for their partitioning strategy to be of value. While they concentrate on the workstation network model of parallel processing because of its high communication costs and inherent heterogeneity, their method is applicable to other parallel architectures.>

Multicomputers have the potential to deliver Gigaflop performance on many scientific applications. Initial implementations of parallel programs on these machines, however, are often inefficient and require significant optimization before they can harness the potential power of the machine. Performance prediction tools can provide valuable information on which optimizations will result in increased performance. This paper describes an analytical performance prediction model. The model is designed to provide performance data to compilers, programmers and system architects to assist them in making choices which will lead to more efficient implementations. Efficient performance prediction tools can provide information which will help programmers make better use of the power of multicomputers. 1 Introduction One of the most important advances in high performance computing is the increasing availability of commercial parallel computers. These machines promise to provide solutions to many p...

Abstract We describe a compiler and run‐time system that allow data‐parallel programs to execute on a network of heterogeneous UNIX workstations. The programming language supported is Dataparallel C, a SIMD language with virtual processors and a global name space. This parallel programming environment allows the user to take advantage of the power of multiple workstations without adding any message‐passing calls to the source program. Because the performance of Individual workstations in a multi‐user environment may change during the execution of a Dataparallel C program, the run‐time system automatically performs dynamic load balancing. We present experimental results that demonstrate the usefulness of dynamic load‐balancing In a multi‐user environment These results suggest that initially allocating the same amount of work to each processor and letting the dynamic load balancing algorithm adjust the load during program execution yields very good performance. Hence neither the compiler nor the run‐time system need a priori knowledge of the speeds of the machines that will participate in a program execution.

The authors describe a compiler and run-time system that allows data-parallel programs to execute on a network of heterogeneous UNIX workstations. The programming language supported is Dataparallel C, a SIMD language with virtual processors and a global name space. This parallel programming environment allows the user to take advantage of the power of multiple workstations without adding any message-passing calls to the source program. Because the performance of individual workstations in a multi-user environment may change during the execution of a Dataparallel C program, the run-time system automatically performs dynamic load balancing. The authors present experimental results that demonstrate the usefulness of dynamic load balancing in a multi-user environment.>

article Free Access Share on A production-quality C* compiler for Hypercube multicomputers Authors: Philip J. Hatcher View Profile , Anthony J. Lapadula View Profile , Robert R. Jones View Profile , Michael J. Quinn View Profile , Ray J. Anderson View Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 26Issue 7July 1991 pp 73–82https://doi.org/10.1145/109626.109634Online:01 April 1991Publication History 24citation249DownloadsMetricsTotal Citations24Total Downloads249Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

DataparallelC is a SIMD programming language suitable for solving many problems arising in science and engineering, We have developed compilers that translate Dataparallel C programs into C code that can be compiled and executed on multicomputers and multiprocessors.We describe the Dataparallel C programming language, the implementation of three scienti$c programs in Dataparallel C, and the pe~ormance of the compiled programs on three MIMD computers: the Intel iPSC/2, the nCUBE 3200, and the Sequent Symmetry S81.These case studies-an ocean circulation model, the "shallow-water" atmospheric model, and the Sharks World model-illustrate that nontrivial SIMD programs can achieve good speedup on MIMD computers and still be portable.

The implementation of two compilers for the data-parallel programming language Dataparallel C is described. One compiler generates code for Intel and nCUBE hypercube multicomputers; the other generates code for Sequent multiprocessors. A suite of Dataparallel C programs has been compiled and executed, and their execution times and speedups on the Intel iPSC/2, the nCUBE 3200 and the Sequent Symmetry are presented.>

The feasibility of implementing best-first (best-bound) branch-and-bound algorithms on hypercube multicomputers is discussed. The computationally-intensive nature of these algorithms might lead a causal observer to believe that their parallelization is trivial. However, as the number of processors grows, two goals must be satisfied to some degree in order to maintain a reasonable level of efficiency. First, processors must be kept busy doing productive work (i.e. exploring worthwhile subproblems). Second, the number of interprocessor communications must be minimized along the critical path in the state-space tree from the original problem to the subproblem yielding a solution. It is difficult to improve performance in one of these areas without degrading performance in the other. Analytical models for the execution time of loosely synchronous and asynchronous parallel branch-and-bound algorithms are presented, and the models are validated with data from the execution of five algorithms that solve the traveling salesperson problem.>