7,870 research outputs found
A partial breadth-first execution model for prolog
MEM (Multipath Execution Model) is a novel model for the execution of Prolog programs which combines a depth-first and breadth-first exploration of the search tree. The breadth-first search allows more than one path of the SLD-tree to be explored at the same time. In this way, the computational cost of traversing the whole search tree associated to a program can be decreased because the MEM model reduces the overhead due to the execution of control instructions and also diminishes the number of unifications to be performed. This paper focuses on the description of the MEM model and its sequential implementation. Moreover, the MEM execution model can be implemented in order to exploit a new kind of parallelism, called path parallelism, which allows the parallel execution of unify operations related to simultaneously traversed pathsPeer ReviewedPostprint (published version
Range Queries on Uncertain Data
Given a set of uncertain points on the real line, each represented by
its one-dimensional probability density function, we consider the problem of
building data structures on to answer range queries of the following three
types for any query interval : (1) top- query: find the point in that
lies in with the highest probability, (2) top- query: given any integer
as part of the query, return the points in that lie in
with the highest probabilities, and (3) threshold query: given any threshold
as part of the query, return all points of that lie in with
probabilities at least . We present data structures for these range
queries with linear or nearly linear space and efficient query time.Comment: 26 pages. A preliminary version of this paper appeared in ISAAC 2014.
In this full version, we also present solutions to the most general case of
the problem (i.e., the histogram bounded case), which were left as open
problems in the preliminary versio
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Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures
The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu
Task-based Augmented Contour Trees with Fibonacci Heaps
This paper presents a new algorithm for the fast, shared memory, multi-core
computation of augmented contour trees on triangulations. In contrast to most
existing parallel algorithms our technique computes augmented trees, enabling
the full extent of contour tree based applications including data segmentation.
Our approach completely revisits the traditional, sequential contour tree
algorithm to re-formulate all the steps of the computation as a set of
independent local tasks. This includes a new computation procedure based on
Fibonacci heaps for the join and split trees, two intermediate data structures
used to compute the contour tree, whose constructions are efficiently carried
out concurrently thanks to the dynamic scheduling of task parallelism. We also
introduce a new parallel algorithm for the combination of these two trees into
the output global contour tree. Overall, this results in superior time
performance in practice, both in sequential and in parallel thanks to the
OpenMP task runtime. We report performance numbers that compare our approach to
reference sequential and multi-threaded implementations for the computation of
augmented merge and contour trees. These experiments demonstrate the run-time
efficiency of our approach and its scalability on common workstations. We
demonstrate the utility of our approach in data segmentation applications
Real-time detection of grid bulk transfer traffic
The current practice of physical science research has yielded a continuously growing demand for interconnection network bandwidth to support the sharing of large datasets. Academic research networks and internet service providers have provisioned their networks to handle this type of load, which generates prolonged, high-volume traffic between nodes on the network. Maintenance of QoS for all network users demands that the onset of these (Grid bulk) transfers be detected to enable them to be reengineered through resources specifically provisioned to handle this type of traffic. This paper describes a real-time detector that operates at full-line-rate on Gb/s links, operates at high connection rates, and can track the use of ephemeral or non-standard ports
Life of occam-Pi
This paper considers some questions prompted by a brief review of the history of computing. Why is programming so hard? Why is concurrency considered an “advanced” subject? What’s the matter with Objects? Where did all the Maths go? In searching for answers, the paper looks at some concerns over fundamental ideas within object orientation (as represented by modern programming languages), before focussing on the concurrency model of communicating processes and its particular expression in the occam family of languages. In that focus, it looks at the history of occam, its underlying philosophy (Ockham’s Razor), its semantic foundation on Hoare’s CSP, its principles of process oriented design and its development over almost three decades into occam-? (which blends in the concurrency dynamics of Milner’s ?-calculus). Also presented will be an urgent need for rationalisation – occam-? is an experiment that has demonstrated significant results, but now needs time to be spent on careful review and implementing the conclusions of that review. Finally, the future is considered. In particular, is there a future
On the Impact of Memory Allocation on High-Performance Query Processing
Somewhat surprisingly, the behavior of analytical query engines is crucially
affected by the dynamic memory allocator used. Memory allocators highly
influence performance, scalability, memory efficiency and memory fairness to
other processes. In this work, we provide the first comprehensive experimental
analysis on the impact of memory allocation for high-performance query engines.
We test five state-of-the-art dynamic memory allocators and discuss their
strengths and weaknesses within our DBMS. The right allocator can increase the
performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server
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