COMP Superscalar

Big Data Distributed Computing Programming Models

COMP Superscalar (COMPSs) is a framework which aims to ease the development and execution of applications for distributed infrastructures, such as Clusters, Grids and Clouds.

Software Author: 

Workflows and Distributed Computing Group

Contact:

Jorge Ejarque (jorge [dot] ejarque [at] bsc [dot] es)

Rosa M. Badia (rosa [dot] m [dot] badia [at] bsc [dot] es)

Support mailing list (support-compss [at] bsc [dot] es)

Software Cost: 

COMP Superscalar is distributed under Apache License version 2

Primary tabs

COMPSs Download Form
Please, fill the following form in order to access this download:
16 + 0 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
COMPSs VM Download Form
Please, fill the following form in order to access this download:
1 + 1 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

2.5 (Latest Version)

COMP Superscalar version 2.5 (Freesia) Release date: June-2019

Release Notes

New features

  • Runtime:
    • New Concurrent direction type for task parameter.
    • Multi-node tasks support for native (Java, Python) tasks. Previously, multi-node tasks were only posible with @mpi or @decaf tasks.
    • @Compss decorator for executing compss applications as tasks.
    • New runtime API to synchronize files without opening them.
    • Customizable task failure management with the "onFailure" task property.
    • Enabled master node to execute tasks
  • Python:
    • Partial support of numba in tasks.
    • Support for collection as task parameter.
    • Supported task inheritance.
    • New persistent MPI worker mode (alternative to subprocess).
    • Support to ARM MAP and DDT tools (with MPI worker mode).
  • C:
    • Support for task without parameters and applications without src folder.

Improvements:

  • New task property "targetDirection" to indicate direction of the target object in object methods. Substitutes the "isModifier" task property.
  • Warnings for deprecated or incorrect task parameters.
  • Improvements in Jupyter for Supercomputers.
  • Upgrade of runcompss_docker script to docker stack interface.
  • Several bug fixes.

Known Limitations

  • Tasks that invoke Numpy and MKL may experience issues if a different MKL threads count is used in different tasks. This is due to the fact that MKL reuses  threads in the different calls and it does not change the number of threads from one call to another
  • C++ Objects declared as arguments in a coarse-grain tasks must be passed in the task methods as object pointers in order to have a proper dependency management.
  • Master as worker is not working for executions with persistent worker in C++.
  • Coherence and concurrent writing in parameters annotated with the "Concurrent" direction must be managed by the underlaying distributed storage system.
  • Delete file calls for files used as input can produce a significant synchronization of the main code.

For further information please refer to COMPSs User Manual: Application development

COMPSs for Supercomputers
Check Installation manual for details about how to install from the repository

Read this document before downloading the VM image: COMPSs VM Instructions

Docker Image pull command:

docker pull compss/compss:2.5

COMPSs VM
COMPSs Docker Image

Old Versions

2.4

2.3

2.2

2.1

2.0

1.4