R. Amela, Enabling Python to Execute Efficiently in Heterogeneous Distributed Infrastructures with PyCOMPSs, Proceedings of the 7th Workshop on Python for High-Performance and Scientific Computing, 2017.

R. Amela, Executing linear algebra kernels in heterogeneous distributed infrastructures with PyCOMPSs. Oil -& Gas Science and Technology -Revue d'IFP Energies Nouvelles (OGST), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01904616

. Anaconda, Numba: A High Performance Python Compiler, 2020.

A. E. , LAPACK Users' guide. SIAM. Apache Software Fundation, 1999.

R. M. Badia, COMP superscalar, an interoperable programming framework, SoftwareX, vol.3, pp.32-36, 2015.

, Barcelona Supercomputing Center (BSC) (2019a) COMPSs GitHub, 2019.

, Barcelona Supercomputing Center (BSC) (2019b) Extrae Tool, 2019.

. Marenostrum, , 2019.

, Barcelona Supercomputing Center (BSC) (2019d) Paraver Tool, 2019.

S. Barcelona and . Center, BSC) (2020) PyCOMPSs User Manual

, Development.html#python-binding, 2020.

C. Bastoul, Code Generation in the Polyhedral Model Is Easier Than You Think. In: PACT'13 IEEE International Conference on Parallel Architecture and Compilation Techniques, pp.7-16, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00017260

C. Bastoul, OpenScop: A Specification and a Library for Data Exchange in Polyhedral Compilation Tools, 2011.

C. Bastoul, Putting Polyhedral Loop Transformations to Work, International Workshop on Languages and Compilers for Parallel Computing, pp.209-225, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00071681

P. Bientinesi, G. B. Van-de-geijn, and R. A. , Families of Algorithms Related to the Inversion of a Symmetric Positive Definite Matrix, ACM Trans. Math. Softw, vol.35, issue.1, 2008.

U. Bondhugula, , 2017.

U. Bondhugula, A Practical Automatic Polyhedral Parallelizer and Locality Optimizer, SIGPLAN Not, vol.43, issue.6, pp.101-113, 2008.

U. Bondhugula, Automatic Transformations for Communication-Minimized Parallelization and Locality Optimization in the Polyhedral Model, International Conference on Compiler Construction, pp.132-146, 2008.

S. Cass, The Top Programming Languages 2019: Python remains the big kahuna, but specialist languages hold their own, 2019.

A. Cohen, Facilitating the Search for Compositions of Program Transformations, Proceedings of the 19th Annual International Conference on Supercomputing. ACM, pp.151-160, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01257296

J. Conejero, Task-based programming in COMPSs to converge from HPC to big data, The International Journal of High Performance Computing Applications, vol.32, issue.1, pp.45-60, 2018.

D. M. Cooke, NumExpr: Fast numerical expression evaluator for NumPy, 2020.

L. Dagum and R. Menon, OpenMP: An Industry-Standard API for Shared-Memory Programming, IEEE Comput. Sci. Eng, vol.5, issue.1, pp.46-55, 1998.

L. Dalcín, P. R. Storti, and M. , MPI for Python, Journal of Parallel and Distributed Computing DOI, 2005.

, Dask: Library for dynamic task scheduling, Dask Development Team, 2016.

J. W. Demmel and N. J. Higham, Stability of Block Algorithms with Fast Level-3 BLAS, ACM Trans. Math. Softw, vol.18, issue.3, pp.274-291, 1992.

G. H. Golub and C. F. Van-loan, Matrix Computations, 1996.

M. D. Baltimore and . Usa,

. Google, The Go Programming Language, 2019.

J. A. Gunnels, FLAME: Formal Linear Algebra Methods Environment, ACM Trans. Math. Softw, vol.27, issue.4, pp.422-455, 2001.

. Intel, Threading Building Blocks (Intel®TBB), 2019.

E. Jones, T. Oliphant, and P. Peterson, SciPy: Open source scientific tools for Python, 2001.

S. K. Lam, A. Pitrou, and S. Seibert, Numba: A llvm-based python jit compiler, Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, pp.1-6, 2015.

S. Liang, Java Native Interface: Programmer's Guide and Reference, vol.0201325772, 1999.

F. Lordan, ServiceSs: an interoperable programming framework for the Cloud, Journal of Grid Computing, vol.12, issue.1, pp.67-91, 2014.

M. Caamaño and J. M. , Full runtime polyhedral optimizing loop transformations with the generation, instantiation, and scheduling of code-bones, Concurrency and Computation: Practice and Experience, vol.29, issue.15, 2017.

W. Mckinney, Pandas: a Foundational Python Library for Data Analysis and Statistics. Python for High Performance and Scientific Computing, pp.1-9, 2011.

S. C. Müller, Pydron: Semi-automatic parallelization for multi-core and the cloud, 11th {USENIX} Symposium on Operating Systems Design and Implementation, pp.645-659, 2014.

, Python Software Fundation (2019) Parallel Processing and Multiprocessing in Python, 2019.

G. Quintana-orti, Scheduling of QR Factorization Algorithms on SMP and Multi-Core Architectures, Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing, pp.301-310, 2008.

C. Ramon-cortes, PyCOMPSs AutoParallel Module GitHub, 2019.

R. , Transparent Orchestration of Task-based Parallel Applications in Containers Platforms, Journal of Grid Computing, vol.16, issue.1, pp.137-160, 2018.

A. Sukumaran-rajam and P. Clauss, The Polyhedral Model of Nonlinear Loops, ACM Trans. Archit. Code Optim, vol.12, issue.4, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01244464

E. Tejedor, PyCOMPSs: Parallel computational workflows in Python, The International Journal of High Performance Computing Applications (IJHPCA), vol.31, pp.66-82, 2017.

, PolyBench/C: The Polyhedral Benchmark suite, 2015.

G. Van-rossum, D. Fl-;-var-der-walt, S. , C. Sc, and G. Varoquaux, The Python Language Reference Manual. Network Theory Ltd. ISBN 1906966141, 9781906966140, Computing in Science and Engg, vol.13, issue.2, 2011.

V. Vanovschi, Parallel Python Software, 2019.

M. Zaharia, Spark: Cluster Computing with Working Sets, Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, pp.95-102, 2010.