Debian Science Project
Summary
Distributed computing
Debian Science Distributed Computing packages

This metapackage will install Debian Science packages useful for various types of distributed computing, such as grid-, cloud-, cluster- and parallel-computing.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

Links to other tasks

Debian Science Distributed computing packages

Official Debian packages with high relevance

Dmtcp
리눅스 프로세스에 대한 검사/재시작 기능
Maintainer: Paul Grosu
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DMTCP (Distributed MultiThreaded Checkpointing/분산 멀티쓰래드 검사)는 멀티 쓰래드 및 분산 계산을 포함하는 임의의 어플리케이션 그룹 상태를 투명하게 검사하는 도구입니다. 리눅스 커널 모듈 또는 다른 커널 모드가 없는 사용자 바이너리 실행파일에서 직접 작동합니다.

 .
DMTCP가 지원하는 어플리케이션에는 Open MPI, MVAPICH2, MATLAB, R, Python, Perl 그리고 많은 프로그래밍 언어와 쉘 스크립트 언어가 있습니다. DMTCP는 또 vim/cscope, emacs를 포함하는 GNU 스크린 세션을 지원합니다. TightVNC를 사용하면, 확장 기능 (예: no OpenGL, no video)을 사용하지 않는 동안 X-Window 어플리케이션을 검사하고 재실행할 수 있습니다.

이 패키지는 DMTCP 바이너리를 포함합니다.

Please cite: Jason Ansel, Kapil Arya, and Gene Cooperman: DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop (2009)
Environment-modules
Modular system for handling environment variables
Maintainer: Alastair McKinstry
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The Modules package provides for the dynamic modification of a user's environment via modulefiles. Each modulefile contains the information needed to configure the shell for an application. Once the Modules package is initialized, the environment can be modified dynamically on a per-module basis using the module command which interprets modulefiles. Typically modulefiles instruct the module command to alter or set shell environment variables such as PATH, MANPATH, etc. modulefiles may be shared by many users on a system and users may have their own collection to supplement or replace the shared modulefiles. The modules environment is common on SGI/Crays and many workstation farms.

Globus-core
Globus Toolkit - Globus Core - Transitional Package
Maintainer: Mattias Ellert
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The Globus Toolkit is an open source software toolkit used for building Grid systems and applications. It is being developed by the Globus Alliance and many others all over the world. A growing number of projects and companies are using the Globus Toolkit to unlock the potential of grids for their cause.

The globus-core package is no longer needed with Globus Toolkit 6. This transitional package provides a dummy pkg-config file to fulfill dependencies during the transition. In can be removed when no longer needed.

Gridengine-client
Utilities for Grid Engine queue management
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Grid Engine is software that facilitates "distributed resource management" (DRM). Far more than just simple load-balancing tools or batch scheduling mechanisms, DRM software typically provides the following key features across large sets of distributed resources:

  • Policy based allocation of distributed resources (CPU time, software licenses, etc.)
  • Batch queuing & scheduling
  • Support diverse server hardware, OS and architectures
  • Load balancing & remote job execution
  • Detailed job accounting statistics
  • Fine-grained user specifiable resources
  • Suspend/resume/migrate jobs
  • Tools for reporting Job/Host/Cluster status
  • Job Arrays
  • Integration & control of parallel jobs

This package contains the Grid Engine user utilities.

Gridengine-exec
Distributed resource management - Execution Server
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Grid Engine is software that facilitates "distributed resource management" (DRM). Far more than just simple load-balancing tools or batch scheduling mechanisms, DRM software typically provides the following key features across large sets of distributed resources:

  • Policy based allocation of distributed resources (CPU time, software licenses, etc.)
  • Batch queuing & scheduling
  • Support diverse server hardware, OS and architectures
  • Load balancing & remote job execution
  • Detailed job accounting statistics
  • Fine-grained user specifiable resources
  • Suspend/resume/migrate jobs
  • Tools for reporting Job/Host/Cluster status
  • Job Arrays
  • Integration & control of parallel jobs

This package contains the Grid Engine execution server.

Gridengine-master
Distributed resource management - Master Server
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Grid Engine is software that facilitates "distributed resource management" (DRM). Far more than just simple load-balancing tools or batch scheduling mechanisms, DRM software typically provides the following key features across large sets of distributed resources:

  • Policy based allocation of distributed resources (CPU time, software licenses, etc.)
  • Batch queuing & scheduling
  • Support diverse server hardware, OS and architectures
  • Load balancing & remote job execution
  • Detailed job accounting statistics
  • Fine-grained user specifiable resources
  • Suspend/resume/migrate jobs
  • Tools for reporting Job/Host/Cluster status
  • Job Arrays
  • Integration & control of parallel jobs

This package contains the Grid Engine master server.

Gridengine-qmon
Grid Engine 큐 관리를 위한 그래픽 유틸리티
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Grid Engine은 "분산 자원 관리"(DRM)을 가능하게하는 소프트웨어입니다. 대규모 분산 리소스 세트에 대해 DRM 소프트웨어는 단순 로드 밸런싱 또는 배치 스케쥴링 메커니즘에 비해 아래와 같은 주요 기능을 더 많이 제공합니다:

  • 정책 기분 분산 리소스 할당 (CPU 시간, 소프트웨어 라이센스등)
  • 배치 큐잉 & 스케쥴링
  • 다양한 서버 하드웨어, OS 및 아키텍처 지원
  • 로드 밸런싱 & 원격 잡 실행
  • 세부적인 잡 어카운팅 통계
  • 세분화된 사용자 지정 리소스
  • 작업 일시중지/재개/마이그레이션
  • 잡/호스트/클러스트 상태 보고 도구
  • 잡 배열
  • 병렬 작업 통합 & 제어

이 패키지는 그래픽 Grid Engine 관리 프로트엔드를 포함합니다.

Htcondor
distributed workload management system
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Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion.

Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user.

This package can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing HTCondor pool or as a "Personal" (single machine) HTCondor pool.

Please cite: Michael Litzkow, Miron Livny and Matt Mutka: Condor - A Hunter of Idle Workstations (eprint) Proceedings of the 8th International Conference of Distributed Computing Systems :104-111 (1988)
Ipython3
Enhanced interactive Python 3 shell
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IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs.

This package contains the actual terminal shell for Python 3.

The package is enhanced by the following packages: isympy3 python3-matplotlib python3-sympy
Screenshots of package ipython3
Mpich
Implementation of the MPI Message Passing Interface standard
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MPICH is a high-performance and widely portable implementation of the MPI-3.1 standard from the Argonne National Laboratory. It efficiently supports different computation and communication platforms including commodity clusters, SMPs, massively parallel systems, and high-speed networks. This release has all MPI 3.1 functions and features required by the standard with the exception of support for the "external32" portable I/O format and user-defined data representations for I/O.

This package includes the program binaries necessary to run MPICH programs.

Mpich2python
MPI-enhanced Python interpreter (MPICH2 based version)
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The package provides a python interpreter with MPI (Message Passing Interface, message-based parallel programming) support.

Nordugrid-arc-nox
ARC grid middleware
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The NorduGrid is a collaboration aiming at development, maintenance and support of the free Grid middleware, known as the Advanced Resource Connector (ARC).

The Advanced Resource Connector (ARC) brings computing resources together across institutional boundaries. This concept is commonly referred to as a "computational grid". Historically, grids address the organisation of distributed storage of data and parallel computation, but arbitrary services are thinkable.

Just like the web, ARC has its roots in the IT infrastructure that was erected to analyse the experiments for high energy physics at CERN. The first release, ARC-0.x, was dependent on Globus, the current release keeps that compatibility but can also be used independently.

With ARC-nox, formerly known as ARC-1, the user gains flexibility with additional services and more supported platforms. The service developer notices that even for persistent functionality across the two major versions, what has been a script on the server side once, that was repeatedly started and ran through, this has now become a service. Those are only started once and can then be queried, which is far more efficient, far more responsive, and is no longer stateless but can observe changes of values over time.

Openmpi-bin
high performance message passing library -- binaries
Maintainer: Alastair McKinstry
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Open MPI is a project combining technologies and resources from several other projects (FT-MPI, LA-MPI, LAM/MPI, and PACX-MPI) in order to build the best MPI library available. A completely new MPI-3.1 compliant implementation, Open MPI offers advantages for system and software vendors, application developers and computer science researchers.

Features:

  • Full MPI-3.1 standards conformance
  • Thread safety and concurrency
  • Dynamic process spawning
  • High performance on all platforms
  • Reliable and fast job management
  • Network and process fault tolerance
  • Support network heterogeneity
  • Single library supports all networks
  • Run-time instrumentation
  • Many job schedulers supported
  • Internationalized error messages
  • Component-based design, documented APIs

This package contains the Open MPI utility programs.

Openmpipython
MPI-enhanced Python interpreter (OpenMPI based version)
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The package provides a python interpreter with MPI (Message Passing Interface, message-based parallel programming) support.

Python3-scoop
Python3 library for concurrent parallel programming
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SCOOP (Scalable COncurrent Operations in Python) is a distributed task module allowing concurrent parallel programming on various environments, from heterogeneous grids to supercomputers.

This package contains the Python3 modules.

Official Debian packages with lower relevance

Coop-computing-tools
cooperative computing tools
Maintainer: Alastair McKinstry
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This is a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. This includes:

  • Chirp: A personal filesystem and I/O protocol that allows unprivileged users to share space securely, efficiently, and conveniently. When combined with Parrot, Chirp allows users to create custom wide-area distributed filesystems.
  • Parrot: A transparent user-level virtual filesystem that allows any ordinary program to be attached to a remote storage device such as an FTP server or a Chirp server.
  • Makeflow: A workflow system for parallel and distributed computing that uses a language very similar to Make.
  • Work Queue: A system and API for building master-worker style programs that scale up to thousands of processors.
  • All Pairs: A computational abstraction for running very large Cartesian products.
  • Wavefront: A computational abstraction for running very large dynamic programming problems.
  • The Fault Tolerant Shell: A high-level programming language that allows users to combine the ease of shell scripting, the power of distributed programming, and the precision of compiled languages. Basically, parallel programming and exception handling for scripts.
Please cite: Douglas Thain, Christopher Moretti, and Jeffrey Hemmes: Chirp: A Practical Global Filesystem for Cluster and Grid Computing. (2009)
Hpcc
HPC Challenge 벤치마크
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High Performance Computing (HPC) Challenge 벤치마크는 HPC 클러스터에 대한 CPU, 메모리, 네트워크 성능을 계산하는 7개의 테스트를 실행합니다. 무엇보다도, Top500 랭킹 (http://www.top500.org/)에서 사용하는 High-Performance LINPACK (HPL) 벤치마크가 포함되어 있습니다.

Octave-mpi
Octave toolbox for parallel computing using MPI
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This package brings parallel computing capabilities to Octave by providing a convenient interface to MPI primitives. This is achieved using simple MPI Derived Datatypes.

This Octave add-on package is part of the Octave-Forge project.

Python3-mpi4py
bindings of the Message Passing Interface (MPI) standard
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MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.

mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects).

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Psom
pipeline system for Octave and Matlab
Responsible: NeuroDebian Team
License: MIT/Expat
Debian package not available
Language: Matlab/Octave

PSOM is a lightweight library to manage complex multi-stage data processing. A pipeline is a collection of jobs, i.e. Matlab or Octave code with a well identified set of options that are using files for inputs and outputs. To use PSOM, the only requirement is to generate a description of a pipeline in the form of a simple Matlab/Octave structure. PSOM then automatically offers the following services:

  • Run jobs in parallel using multiple CPUs or within a distributed computing environment.
  • Generate log files and keep track of the pipeline execution. These logs are detailed enough to fully reproduce the analysis.
  • Handle job failures: successful completion of jobs is checked and failed jobs can be restarted.
  • Handle updates of the pipeline: change options or add jobs and let PSOM figure out what to reprocess.
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