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

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

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3.1 (Latest Version)

COMP Superscalar version 3.1 (Margarita) Release date: November-2022

Release Notes

New features

  • Support for Julia applications as tasks
  • Support for uDocker for container tasks.
  • New decorator to apply a transformation to a parameter (@data_transformation)
  • Automatic creation of Data Provenance information from Java applications.
  • Constraint to force execution in the local agent. (is_local=True)
  • Extended external software description (JSON file in Software decorator) to allow the definition of task parameters.
  • Enable the specification of master working directory (location where data serializations are stored).Old flag for indicating the execution dir is now --job_execution_dir.

Improvements:

  • Data Provenance: enhanced addition of source files using directories
  • Fix issues when wait_on cancelled data. Get the latest version.
  • Swap distutils with setuptools in the Python binding installation.
  • Improvements in the management of tasks returning a modified parameter (a=task(a)).
  • Container decorator allows to define flags to be passed to the container execution commands.
  • Improvements in the support for agents and task nesting.
  • Improvements in pluggable schedulers.
  • Improvements in python cache.
  • Improvements in tracing system: Fix assigned gpus events, include python cache events.
  • Fix issues in Collection graph generation
  • Configuration files for Hawk, Mahti, Dardel and Lenox system.
  • Several Bug fixes.

Known Limitations

  • Issues when using tracing with Java 14+. For Java 17+ require to include this jvm flag "-Dcom.sun.xml.bind.v2.bytecode.ClassTailor.noOptimize=true"
  • Collections are not supported in http tasks.
  • OSX support is limited to Java and Python without CPU affinity (require to execute with --cpu_affinity=disable).  Tracing is not available.
  • Reduce operations can consume more disk space than the manually programmed n-ary reduction
  • 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.  To fix these issues use the DLB option for in the cpu_affinity flag.
  • 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

Docker Image pull command:

docker pull compss/compss:3.1
docker pull compss/compss-tutorial:3.1

Old Versions

3.0

2.10

2.9

2.8

2.7

2.6

2.5

2.4

2.3

2.2

2.1

2.0