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Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.
The Globus Toolkit is an open-source toolkit for grid computing developed and provided by the Globus Alliance. On 25 May 2017 it was announced that the open source support for the project would be discontinued in January 2018, due to a lack of financial support for that work. The Globus service continues to be available to the research community under a freemium approach, designed to sustain the software, with most features freely available but some restricted to subscribers.
MOSIX is a proprietary distributed operating system. Although early versions were based on older UNIX systems, since 1999 it focuses on Linux clusters and grids. In a MOSIX cluster/grid there is no need to modify or to link applications with any library, to copy files or login to remote nodes, or even to assign processes to different nodes – it is all done automatically, like in an SMP.
HTCondor is an open-source high-throughput computing software framework for coarse-grained distributed parallelization of computationally intensive tasks. It can be used to manage workload on a dedicated cluster of computers, or to farm out work to idle desktop computers – so-called cycle scavenging. HTCondor runs on Linux, Unix, Mac OS X, FreeBSD, and Microsoft Windows operating systems. HTCondor can integrate both dedicated resources and non-dedicated desktop machines into one computing environment.
A job scheduler is a computer application for controlling unattended background program execution of jobs. This is commonly called batch scheduling, as execution of non-interactive jobs is often called batch processing, though traditional job and batch are distinguished and contrasted; see that page for details. Other synonyms include batch system, distributed resource management system (DRMS), distributed resource manager (DRM), and, commonly today, workload automation (WLA). The data structure of jobs to run is known as the job queue.
The Terascale Open-source Resource and QUEue Manager (TORQUE) is a distributed resource manager providing control over batch jobs and distributed compute nodes. TORQUE can integrate with the non-commercial Maui Cluster Scheduler or the commercial Moab Workload Manager to improve overall utilization, scheduling and administration on a cluster.
Distributed Resource Management Application API (DRMAA) is a high-level Open Grid Forum (OGF) API specification for the submission and control of jobs to a distributed resource management (DRM) system, such as a cluster or grid computing infrastructure. The scope of the API covers all the high level functionality required for applications to submit, control, and monitor jobs on execution resources in the DRM system.
A cluster manager is usually backend graphical user interface (GUI) or command-line interface (CLI) software that runs on a set of cluster nodes that it manages. The cluster manager works together with a cluster management agent. These agents run on each node of the cluster to manage and configure services, a set of services, or to manage and configure the complete cluster server itself In some cases the cluster manager is mostly used to dispatch work for the cluster to perform. In this last case a subset of the cluster manager can be a remote desktop application that is used not for configuration but just to send work and get back work results from a cluster. In other cases the cluster is more related to availability and load balancing than to computational or specific service clusters.
The Open Grid Forum (OGF) is a community of users, developers, and vendors for standardization of grid computing. It was formed in 2006 in a merger of the Global Grid Forum and the Enterprise Grid Alliance. The OGF models its process on the Internet Engineering Task Force (IETF), and produces documents with many acronyms such as OGSA, OGSI, and JSDL.
Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism.
Arvind is the Johnson Professor of Computer Science and Engineering in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). He was also elected as a member into the National Academy of Engineering in 2008 for contributions to data flow and multi-thread computing and the development of tools for the high-level synthesis of hardware.
Parallel Extensions was the development name for a managed concurrency library developed by a collaboration between Microsoft Research and the CLR team at Microsoft. The library was released in version 4.0 of the .NET Framework. It is composed of two parts: Parallel LINQ (PLINQ) and Task Parallel Library (TPL). It also consists of a set of coordination data structures (CDS) – sets of data structures used to synchronize and co-ordinate the execution of concurrent tasks.
HPX, short for High Performance ParalleX, is a runtime system for high-performance computing. It is currently under active development by the STE||AR group at Louisiana State University. Focused on scientific computing, it provides an alternative execution model to conventional approaches such as MPI. HPX aims to overcome the challenges MPI faces with increasing large supercomputers by using asynchronous communication between nodes and lightweight control objects instead of global barriers, allowing application developers to exploit fine-grained parallelism.
Many-task computing (MTC) in computational science is an approach to parallel computing that aims to bridge the gap between two computing paradigms: high-throughput computing (HTC) and high-performance computing (HPC).
Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications which devote most of their execution time to computational requirements are deemed compute-intensive, whereas computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data are deemed data-intensive.
In parallel computing, work stealing is a scheduling strategy for multithreaded computer programs. It solves the problem of executing a dynamically multithreaded computation, one that can "spawn" new threads of execution, on a statically multithreaded computer, with a fixed number of processors. It does so efficiently in terms of execution time, memory usage, and inter-processor communication.
Swift is an implicitly parallel programming language that allows writing scripts that distribute program execution across distributed computing resources, including clusters, clouds, grids, and supercomputers. Swift implementations are open-source software under the Apache License, version 2.0.
In the high-performance computing environment, burst buffer is a fast intermediate storage layer positioned between the front-end computing processes and the back-end storage systems. It bridges the performance gap between the processing speed of the compute nodes and the Input/output (I/O) bandwidth of the storage systems. Burst buffers are often built from arrays of high-performance storage devices, such as NVRAM and SSD. It typically offers from one to two orders of magnitude higher I/O bandwidth than the back-end storage systems.
Ishfaq Ahmad is a computer scientist, IEEE Fellow and Professor of Computer Science and Engineering at the University of Texas at Arlington (UTA). He is the Director of Center For Advanced Computing Systems (CACS) and has previously directed IRIS at UTA. He is widely recognized for his contributions to scheduling techniques in parallel and distributed computing systems, and video coding.
References
- ↑ Sfiligoi, I. (2008). "glideinWMS—a generic pilot-based workload management system". J. Phys.: Conf. Ser. 119 (6): 062044. doi: 10.1088/1742-6596/119/6/062044 .
- ↑ Luckow, André; Lacinski, Lukasz; Jha, Shantenu (2010). SAGA BigJob: An Extensible and Interoperable Pilot-Job Abstraction for Distributed Applications and Systems, 10th IEEE/ACM International Conference on Cluster. Cloud and Grid Computing. pp. 135–144. doi:10.1109/CCGRID.2010.91. ISBN 978-1-4244-6987-1.
- ↑ Wilde, Michael; et al. (2011). "Swift: A language for distributed parallel scripting". Parallel Computing. 37 (9): 633–652. CiteSeerX 10.1.1.658.8990 . doi:10.1016/j.parco.2011.05.005.
- ↑ I. Raicu, Y. Zhao, C. Dumitrescu, I. Foster, M. Wilde. "Falkon: A Fast and Lightweight Task Execution Framework," IEEE/ACM SC, 2007, http://www.cs.iit.edu/~iraicu/research/publications/2007_SC07_Falkon.pdf
- ↑ Jik-Soo Kim, Seungwoo Rho, Seoyoung Kim, Sangwan Kim, Seokkyoo Kim, and Soonwook Hwang, HTCaaS: Leveraging Distributed Supercomputing Infrastructures for Large-Scale Scientific Computing, ACM 6th Workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS'13) held with SC13, November 2013, http://datasys.cs.iit.edu/events/MTAGS13/p02.pdf