Institution:
COMP Superscalar (COMPSs)
A task-based programming models that is able to parallelize applications for distributed platforms.
Research Group:
Workflows and Distributed Computing
Researcher/s:
Rosa Maria Badia
Website:
https://compss-doc.readthedocs.io/en/stable/
Description:
A task-based programming models that is able to parallelize applications for distributed platforms.
Type of asset:
Service
Category:
Computer Sciences
Problem:
Making easier the development of applications in parallel and distributed platforms.
Solution:
PyCOMPSs/COMPSs parallelizes applications at task level. Tasks are annotated in the code, and a task-dependency graph is generated at runtime which expresses the potential parallelism of the application. The COMPSs runtime takes care of all the scheduling and data transfer decisions to orchestrate the execution of the application. The system supports the execution in large clusters (supercomputers), clouds, edge-to-cloud environments, and container-managed clusters
Aplication areas:
COMPSs has been applied to implement use cases provided by different communities across diverse disciplines as biomedicine, engineering, biodiversity, chemistry, astrophysics, financial, telecommunications, manufacturing, and earth sciences.
Novelty:
In PyCOMPSs/COMPSs, many novel aspects have been added to the system in recent years: for example, the support for heterogeneous processors, the support to handle exceptions and faults, the support for the continuum edge-to-cloud, the support for streamed data, etc.
Protection:
Open source (apache v2).
Target market:
Any interested in using our technologies.
Keywords:
Workflows, parallel computing, distributed computing, HPC+AI convergence, machine learning.
TRL: 7
CRL: N/A
BRL: N/A
IPRL: N/A
TmRL: N/A
FRL: N/A
More information
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