1,449 research outputs found
Characterizing traits of coordination
How can one recognize coordination languages and technologies? As this report
shows, the common approach that contrasts coordination with computation is
intellectually unsound: depending on the selected understanding of the word
"computation", it either captures too many or too few programming languages.
Instead, we argue for objective criteria that can be used to evaluate how well
programming technologies offer coordination services. Of the various criteria
commonly used in this community, we are able to isolate three that are strongly
characterizing: black-box componentization, which we had identified previously,
but also interface extensibility and customizability of run-time optimization
goals. These criteria are well matched by Intel's Concurrent Collections and
AstraKahn, and also by OpenCL, POSIX and VMWare ESX.Comment: 11 pages, 3 table
Transcending POSIX: The End of an Era?
In this article, we provide a holistic view of the Portable Operating System Interface (POSIX) abstractions by a systematic review of their historical evolution. We discuss some of the key factors that drove the evolution and identify the pitfalls that make them infeasible when building modern applications.Peer reviewe
Threads and Or-Parallelism Unified
One of the main advantages of Logic Programming (LP) is that it provides an
excellent framework for the parallel execution of programs. In this work we
investigate novel techniques to efficiently exploit parallelism from real-world
applications in low cost multi-core architectures. To achieve these goals, we
revive and redesign the YapOr system to exploit or-parallelism based on a
multi-threaded implementation. Our new approach takes full advantage of the
state-of-the-art fast and optimized YAP Prolog engine and shares the underlying
execution environment, scheduler and most of the data structures used to
support YapOr's model. Initial experiments with our new approach consistently
achieve almost linear speedups for most of the applications, proving itself as
a good alternative for exploiting implicit parallelism in the currently
available low cost multi-core architectures.Comment: 17 pages, 21 figures, International Conference on Logic Programming
(ICLP 2010
String Matching with Multicore CPUs: Performing Better with the Aho-Corasick Algorithm
Multiple string matching is known as locating all the occurrences of a given
number of patterns in an arbitrary string. It is used in bio-computing
applications where the algorithms are commonly used for retrieval of
information such as sequence analysis and gene/protein identification.
Extremely large amount of data in the form of strings has to be processed in
such bio-computing applications. Therefore, improving the performance of
multiple string matching algorithms is always desirable. Multicore
architectures are capable of providing better performance by parallelizing the
multiple string matching algorithms. The Aho-Corasick algorithm is the one that
is commonly used in exact multiple string matching algorithms. The focus of
this paper is the acceleration of Aho-Corasick algorithm through a multicore
CPU based software implementation. Through our implementation and evaluation of
results, we prove that our method performs better compared to the state of the
art
A conceptual model for megaprogramming
Megaprogramming is component-based software engineering and life-cycle management. Magaprogramming and its relationship to other research initiatives (common prototyping system/common prototyping language, domain specific software architectures, and software understanding) are analyzed. The desirable attributes of megaprogramming software components are identified and a software development model and resulting prototype megaprogramming system (library interconnection language extended by annotated Ada) are described
3. IPC. Threads. Process Scheduling.
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State-of-the-Art in Parallel Computing with R
R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly useful for general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems four different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix
An Efficient OpenMP Runtime System for Hierarchical Arch
Exploiting the full computational power of always deeper hierarchical
multiprocessor machines requires a very careful distribution of threads and
data among the underlying non-uniform architecture. The emergence of multi-core
chips and NUMA machines makes it important to minimize the number of remote
memory accesses, to favor cache affinities, and to guarantee fast completion of
synchronization steps. By using the BubbleSched platform as a threading backend
for the GOMP OpenMP compiler, we are able to easily transpose affinities of
thread teams into scheduling hints using abstractions called bubbles. We then
propose a scheduling strategy suited to nested OpenMP parallelism. The
resulting preliminary performance evaluations show an important improvement of
the speedup on a typical NAS OpenMP benchmark application
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