13,098 research outputs found
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Leveraging legacy codes to distributed problem solving environments: A web service approach
This paper describes techniques used to leverage high performance legacy codes as CORBA components to a distributed problem solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a userâs desktop computing environment can be extended to a high performance computing environment using a cluster of workstations or a parallel computer
AsterixDB: A Scalable, Open Source BDMS
AsterixDB is a new, full-function BDMS (Big Data Management System) with a
feature set that distinguishes it from other platforms in today's open source
Big Data ecosystem. Its features make it well-suited to applications like web
data warehousing, social data storage and analysis, and other use cases related
to Big Data. AsterixDB has a flexible NoSQL style data model; a query language
that supports a wide range of queries; a scalable runtime; partitioned,
LSM-based data storage and indexing (including B+-tree, R-tree, and text
indexes); support for external as well as natively stored data; a rich set of
built-in types; support for fuzzy, spatial, and temporal types and queries; a
built-in notion of data feeds for ingestion of data; and transaction support
akin to that of a NoSQL store.
Development of AsterixDB began in 2009 and led to a mid-2013 initial open
source release. This paper is the first complete description of the resulting
open source AsterixDB system. Covered herein are the system's data model, its
query language, and its software architecture. Also included are a summary of
the current status of the project and a first glimpse into how AsterixDB
performs when compared to alternative technologies, including a parallel
relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data
analytics platform, for things that both technologies can do. Also included is
a brief description of some initial trials that the system has undergone and
the lessons learned (and plans laid) based on those early "customer"
engagements
The Family of MapReduce and Large Scale Data Processing Systems
In the last two decades, the continuous increase of computational power has
produced an overwhelming flow of data which has called for a paradigm shift in
the computing architecture and large scale data processing mechanisms.
MapReduce is a simple and powerful programming model that enables easy
development of scalable parallel applications to process vast amounts of data
on large clusters of commodity machines. It isolates the application from the
details of running a distributed program such as issues on data distribution,
scheduling and fault tolerance. However, the original implementation of the
MapReduce framework had some limitations that have been tackled by many
research efforts in several followup works after its introduction. This article
provides a comprehensive survey for a family of approaches and mechanisms of
large scale data processing mechanisms that have been implemented based on the
original idea of the MapReduce framework and are currently gaining a lot of
momentum in both research and industrial communities. We also cover a set of
introduced systems that have been implemented to provide declarative
programming interfaces on top of the MapReduce framework. In addition, we
review several large scale data processing systems that resemble some of the
ideas of the MapReduce framework for different purposes and application
scenarios. Finally, we discuss some of the future research directions for
implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite is a foundational software framework that provides query
processing, optimization, and query language support to many popular
open-source data processing systems such as Apache Hive, Apache Storm, Apache
Flink, Druid, and MapD. Calcite's architecture consists of a modular and
extensible query optimizer with hundreds of built-in optimization rules, a
query processor capable of processing a variety of query languages, an adapter
architecture designed for extensibility, and support for heterogeneous data
models and stores (relational, semi-structured, streaming, and geospatial).
This flexible, embeddable, and extensible architecture is what makes Calcite an
attractive choice for adoption in big-data frameworks. It is an active project
that continues to introduce support for the new types of data sources, query
languages, and approaches to query processing and optimization.Comment: SIGMOD'1
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Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
AMR on the CM-2
We describe the development of a structured adaptive mesh algorithm (AMR) for the Connection Machine-2 (CM-2). We develop a data layout scheme that preserves locality even for communication between fine and coarse grids. On 8K of a 32K machine we achieve performance slightly less than 1 CPU of the Cray Y-MP. We apply our algorithm to an inviscid compressible flow problem
Parallelization of irregularly coupled regular meshes
Regular meshes are frequently used for modeling physical phenomena on both serial and parallel computers. One advantage of regular meshes is that efficient discretization schemes can be implemented in a straight forward manner. However, geometrically-complex objects, such as aircraft, cannot be easily described using a single regular mesh. Multiple interacting regular meshes are frequently used to describe complex geometries. Each mesh models a subregion of the physical domain. The meshes, or subdomains, can be processed in parallel, with periodic updates carried out to move information between the coupled meshes. In many cases, there are a relatively small number (one to a few dozen) subdomains, so that each subdomain may also be partitioned among several processors. We outline a composite run-time/compile-time approach for supporting these problems efficiently on distributed-memory machines. These methods are described in the context of a multiblock fluid dynamics problem developed at LaRC
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