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
Checkpoint/Restart functionality for Linux processes
Maintainer: Kapil Arya
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DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods.

Among the applications supported by DMTCP are Open MPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video).

This package contains DMTCP binaries.

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
Graphical 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 graphical Grid Engine administration frontend.

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)
Ipython
Enhanced interactive Python 2 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 2.

The package is enhanced by the following packages: powerline python-matplotlib
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Mpich
Implementation of the MPI Message Passing Interface standard
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MPICH is a high-performance and widely portable implementation of the Message Passing Interface (MPI) standard (both MPI-1 and MPI-2). It efficiently supports different computation and communication platforms including commodity clusters, SMPs, massively parallel systems, and high-speed networks.

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.

Python-mpi
MPI module for Python
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The package provides MPI (Message Passing Interface, message-based parallel programming) support for Python.

Python-scoop
Python 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 Python modules.

Torque-common
??? missing short description for package torque-common :-(
Maintainer: Morten Kjeldgaard
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Torque-server
??? missing short description for package torque-server :-(
Maintainer: Morten Kjeldgaard
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Official Debian packages with lower relevance

Coop-computing-tools
cooperative computing tools
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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 benchmark
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The High Performance Computing (HPC) Challenge benchmark runs a suite of 7 tests that measure the performance of CPU, memory and network for HPC clusters. Amongst others, it includes the High-Performance LINPACK (HPL) benchmark, used by the Top500 ranking (http://www.top500.org/).

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.

Python-mpi4py
bindings of the Message Passing Interface (MPI) standard
Versions of package python-mpi4py
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jessie1.3.1+hg20131106-2amd64,arm64,armel,armhf,i386,mips,mipsel,powerpc,ppc64el,s390x
wheezy1.3+hg20120611-3amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
stretch2.0.0-2.1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.0.0-3amd64,arm64,armel,armhf,hurd-i386,i386,kfreebsd-amd64,kfreebsd-i386,mips,mips64el,mipsel,powerpc,ppc64el,s390x
buster2.0.0-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 665 users (342 upd.)*
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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.
*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 200581