46 research outputs found
AutoParallel: A Python module for automatic parallelization and distributed execution of affine loop nests
The last improvements in programming languages, programming models, and
frameworks have focused on abstracting the users from many programming issues.
Among others, recent programming frameworks include simpler syntax, automatic
memory management and garbage collection, which simplifies code re-usage
through library packages, and easily configurable tools for deployment. For
instance, Python has risen to the top of the list of the programming languages
due to the simplicity of its syntax, while still achieving a good performance
even being an interpreted language. Moreover, the community has helped to
develop a large number of libraries and modules, tuning them to obtain great
performance.
However, there is still room for improvement when preventing users from
dealing directly with distributed and parallel computing issues. This paper
proposes and evaluates AutoParallel, a Python module to automatically find an
appropriate task-based parallelization of affine loop nests to execute them in
parallel in a distributed computing infrastructure. This parallelization can
also include the building of data blocks to increase task granularity in order
to achieve a good execution performance. Moreover, AutoParallel is based on
sequential programming and only contains a small annotation in the form of a
Python decorator so that anyone with little programming skills can scale up an
application to hundreds of cores.Comment: Accepted to the 8th Workshop on Python for High-Performance and
Scientific Computing (PyHPC 2018
AutoParallel: A Python module for automatic parallelization and distributed execution of affine loop nests
The last improvements in programming languages, programming models, and frameworks have focused on abstracting the users from many programming issues. Among others, recent programming frameworks include simpler syntax, automatic memory management and garbage collection, which simplifies code re-usage through library packages, and easily configurable tools for deployment. For instance, Python has risen to the top of the list of the programming languages due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. Moreover, the community has helped to develop a large number of libraries and modules, tuning them to obtain great performance.
However, there is still room for improvement when preventing users from dealing directly with distributed and parallel computing issues. This paper proposes and evaluates AutoParallel, a Python module to automatically find an appropriate task-based parallelization of affine loop nests to execute them in parallel in a distributed computing infrastructure. This parallelization can also include the building of data blocks to increase task granularity in order to achieve a good execution performance. Moreover, AutoParallel is based on sequential programming and only contains a small annotation in the form of a Python decorator so that anyone with little programming skills can scale up an application to hundreds of cores
A Programming Model for Hybrid Workflows: combining Task-based Workflows and Dataflows all-in-one
This paper tries to reduce the effort of learning, deploying, and integrating
several frameworks for the development of e-Science applications that combine
simulations with High-Performance Data Analytics (HPDA). We propose a way to
extend task-based management systems to support continuous input and output
data to enable the combination of task-based workflows and dataflows (Hybrid
Workflows from now on) using a single programming model. Hence, developers can
build complex Data Science workflows with different approaches depending on the
requirements. To illustrate the capabilities of Hybrid Workflows, we have built
a Distributed Stream Library and a fully functional prototype extending COMPSs,
a mature, general-purpose, task-based, parallel programming model. The library
can be easily integrated with existing task-based frameworks to provide support
for dataflows. Also, it provides a homogeneous, generic, and simple
representation of object and file streams in both Java and Python; enabling
complex workflows to handle any data type without dealing directly with the
streaming back-end.Comment: Accepted in Future Generation Computer Systems (FGCS). Licensed under
CC-BY-NC-N
AutoParallel: A Python module for automatic parallelization and distributed execution of affine loop nests
International audienceThe last improvements in programming languages, programming models, and frameworks have focused on abstracting the users from many programming issues. Among others, recent programming frameworks include simpler syntax , automatic memory management and garbage collection, which simplifies code re-usage through library packages, and easily configurable tools for deployment. For instance, Python has risen to the top of the list of the programming languages due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. Moreover, the community has helped to develop a large number of libraries and modules, tuning the most commonly used to obtain great performance. However, there is still room for improvement when preventing users from dealing directly with distributed and parallel computing issues. This paper proposes and evaluates AutoPar-allel, a Python module to automatically find an appropriate task-based parallelization of affine loop nests to execute them in parallel in a distributed computing infrastructure. This parallelization can also include the building of data blocks to increase task granularity in order to achieve a good execution performance. Moreover, AutoParallel is based on sequential programming and only contains a small annotation in the form of a Python decorator so that anyone with little programming skills can scale up an application to hundreds of cores
AutoParallel: Automatic parallelisation and distributed execution of affine loop nests in Python
International audienceThe last improvements in programming languages and models have focused on simplicity and abstraction; leading Python to the top of the list of the programming languages. However, there is still room for improvement when preventing users from dealing directly with distributed and parallel computing issues. This paper proposes and evaluates AutoParallel, a Python module to automatically find an appropriate task-based parallelisation of affine loop nests and execute them in parallel in a distributed computing infrastructure. It is based on sequential programming and contains one single annotation (in the form of a Python decorator) so that anyone with intermediate-level programming skills can scale up an application to hundreds of cores. The evaluation demonstrates that AutoParallel goes one step further in easing the development of distributed applications. On the one hand, the programmability evaluation highlights the benefits of using a single Python decorator instead of manually annotating each task and its parameters or, even worse, having to develop the parallel code explicitly (e.g., using OpenMP, MPI). On the other hand, the performance evaluation demonstrates that AutoParallel is capable of automatically generating task-based workflows from sequential Python code while achieving the same performances than manually taskified versions of established state-of-the-art algorithms (i.e., Cholesky, LU, and QR decompositions). Finally, AutoParallel is also capable of automatically building data blocks to increase the tasks' granularity; freeing the user from creating the data chunks, and redesigning the algorithm. For advanced users, we believe that this feature can be useful as a baseline to design blocked algorithms
Diseño de un vehĂculo controlado remotamente para apoyo en labores de bĂşsqueda y rescate en estructuras colapsadas (BREC) (VCR 1.0)
178 p. // 14 - 39:El VCR1.0 es un vehĂculo todo terreno operado remotamente, con la capacidad de recolectar y enviar informaciĂłn vital de forma segura para los grupos USAR (BĂşsqueda y Rescate Urbano) en operaciones BREC (BĂşsqueda y rescate en estructuras colapsadas) en lugares donde se arriesga la vida de las personas que efectĂşan estas actividades
Executing linear algebra kernels in heterogeneous distributed infrastructures with PyCOMPSs
Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large number of libraries and modules, which has helped to put Python on the top of the list of the programming languages [1]. Task-based programming has been proposed in the recent years as an alternative parallel programming model. PyCOMPSs follows such approach for Python, and this paper presents its extensions to combine task-based parallelism and thread-level parallelism. Also, we present how PyCOMPSs has been adapted to support heterogeneous architectures, including Xeon Phi and GPUs. Results obtained with linear algebra benchmarks demonstrate that significant performance can be obtained with a few lines of Python
Executing linear algebra kernels in heterogeneous distributed infrastructures with PyCOMPSs
Python is a popular programming language due to the simplicity of its syntax, while still achieving a good performance even being an interpreted language. The adoption from multiple scientific communities has evolved in the emergence of a large number of libraries and modules, which has helped to put Python on the top of the list of the programming languages [1]. Task-based programming has been proposed in the recent years as an alternative parallel programming model. PyCOMPSs follows such approach for Python, and this paper presents its extensions to combine task-based parallelism and thread-level parallelism. Also, we present how PyCOMPSs has been adapted to support heterogeneous architectures, including Xeon Phi and GPUs. Results obtained with linear algebra benchmarks demonstrate that significant performance can be obtained with a few lines of Python.This work has been supported by the
Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and
2014-SGR-1272). Javier Conejero postdoctoral contract is co-financed by the Ministry of Economy and Competitiveness
under Juan de la Cierva FormaciĂłn postdoctoral fellowship number FJCI-2015-24651. Cristian Ramon-Cortes predoctoral contract is financed by the Ministry of Economy and Competitiveness under the contract BES-2016-076791. This work is
supported by the Intel-BSC Exascale Lab.
This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program
under contract 687584 (TANGO project).Peer Reviewe