COMP Superscalar

Big Data Distributed Computing Programming Models

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

Software Author: 

Workflows and Distributed Computing Group


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

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2.8 (Latest Version)

COMP Superscalar version 2.8 (Iris) Release date: November-2020

Release Notes

New features

  • Runtime:
    • New @Container task annotation to allow the execution of a task inside a container.
    • New IN_DELETE parameter direction type for one-use parameters. It deletes de element after task execution.
    • Support to Reductions.
    • Data layout for collections in MPI tasks. Allow to group elements of a collection to MPI processes.
    • New cpu affinity and runtime events for a better performance analysis
  • Java:
    • CPU Affinity in Java tasks.
  • Python:
    • Support for python dictionaries as collection parameters.
    • Dummy implementations for the @binary and @mpi tasks to allow testing in sequential implementations.
    • Allow tracing events at master user code.
    • New python binding tracing events.
  • C++:
    • CPU Affinity in tasks executed with persistent executors.
  • DDS-2:
    • New methods and optimizations in DDS class.


  • Change locality calculation from scheduling to location update.
  • Some runtime file system operations removed from task execution critical path.
  • Improvements in CPU-Task Executor affinity. Try executor tries to reuse its previous affinity.
  • Support for Collection INOUT/OUT in Persistent storage executions.
  • Improvements in tracing cfgs.
  • Improvements of storage events in traces.
  • Improvements in enqueue_compss and supercomputers cfg files semantics for shared and local disks.
  • Adding NVRAM mode flag in enqueue_compss to allow initialization of nodes with specific NVRAM mode. Requires support for the cluster's resource manager.
  • Configuration files for Irene and A64FX-based systems.
  • Several bug fixes.

Known Limitations

  • Objects used as task parameters must be serializable.
  • 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. This can be also happen with other libraries implemented with OpenMP.
  • 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.
  • Defining a parameter as OUT is only allowed for files and collection files.
  • There is an issue with hwloc and Docker which could affect to python mpi workers. Fixing it require to upgrade the hwloc version used by the MPI runtime.

For further information please refer to COMPSs Documentation

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:2.8

Old Versions