What is COMPSs?

COMP Superscalar (COMPSs) is a task-based programming model which aims to ease the development of applications for distributed infrastructures, such as large High-Performance clusters (HPC), clouds and container managed clusters. COMPSs provides a programming interface for the development of the applications and a runtime system that exploits the inherent parallelism of applications at execution time.

To improve programming productivity, the COMPSs programming model has following characteristics:

  • Sequential programming: COMPSs programmers do not need to deal with the typical duties of parallelization and distribution, such as thread creation and synchronization, data distribution, messaging or fault tolerance. Instead, the model is based on sequential programming, which makes it appealing to users that either lack parallel programming expertise or are looking for better programmability.

  • Agnostic of the actual computing infrastructure: COMPSs offers a model that abstracts the application from the underlying distributed infrastructure. Hence, COMPSs programs do not include any detail that could tie them to a particular platform, like deployment or resource management. This makes applications portable between infrastructures with diverse characteristics.

  • Single memory and storage space: the memory and file system space is also abtracted in COMPSs, giving the illusion that a single memory space and single file system is available. The runtime takes care of all the necessary data transfers.

  • Standard programming languages: COMPSs is based on the popular programming language Java, but also offers language bindings for Python (PyCOMPSs) and C/C++ applications. This makes it easier to learn the model since programmers can reuse most of their previous knowledge.

  • No APIs: In the case of COMPSs applications in Java, the model does not require to use any special API call, pragma or construct in the application; everything is pure standard Java syntax and libraries. With regard the Python and C/C++ bindings, a small set of API calls should be used on the COMPSs applications.

PyCOMPSs/COMPSs can be seen as a programming environment for the development of complex workflows. For example, in the case of PyCOMPSs, while the task-orchestration code needs to be written in Python, it supports different types of tasks, such as Python methods, external binaries, multi-threaded (internally parallelised with alternative programming models such as OpenMP or pthreads), or multi-node (MPI applications). Thanks to the use of Python as programming language, PyCOMPSs naturally integrates well with data analytics and machine learning libraries, most of them offering a Python interface. PyCOMPSs also supports reading/writing streamed data.

At a lower level, the COMPSs runtime manages the execution of the workflow components implemented with the PyCOMPSs programming model. At runtime, it generates a task-dependency graph by analysing the existing data dependencies between the tasks defined in the Python code. The task-graph encodes the existing parallelism of the workflow, which is then scheduled and executed by the COMPSs runtime in the computing resources.

The COMPSs runtime is also able to react to tasks failures and to exceptions in order to adapt the behaviour accordingly. These functionalities, offer the possibility of designing a new category of workflows with very dynamic behaviour, that can change their configuration at execution time upon the occurrence of given events.

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