Mostrar el registro sencillo del ítem
dc.contributor.author | Rodríguez-Sánchez, Rafael | es_ES |
dc.contributor.author | Igual, Francisco D. | es_ES |
dc.contributor.author | Quintana-Ortí, Enrique S. | es_ES |
dc.date.accessioned | 2021-11-10T19:05:36Z | |
dc.date.available | 2021-11-10T19:05:36Z | |
dc.date.issued | 2020-04 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/176790 | |
dc.description.abstract | [EN] Malleability is a property of certain applications (or tasks) that, given an external request or autonomously, can accommodate a dynamic modification of the degree of parallelism being exploited at runtime. Malleability improves resource usage (core occupation) on modern multicore architectures for applications that exhibit irregular and divergent execution paths and heavily depend on the underlying library performance to attain high performance. The integration of malleability within high-performance instances of the Basic Linear Algebra Subprograms (BLAS) is nonexistent, and, in addition, it is difficult to attain given the rigidity of current application programming interfaces (APIs). In this paper, we overcome these issues presenting the integration of a malleability mechanism within BLIS, a high-performance and portable framework to implement BLAS-like operations. For this purpose, we leverage low-level (yet simple) APIs to integrate on-demand malleability across all Level-3 BLAS routines, and we demonstrate the performance benefits of this approach by means of a higher-level dense matrix operation: the LU factorization with partial pivoting and look-ahead | es_ES |
dc.description.sponsorship | The researchers from Universidad Complutense de Madrid were supported by the EU (FEDER) and Spanish MINECO (TIN2015-65277-R, RTI2018-093684-B-I00), and by Spanish CM (S2018/TCS-4423). The researcher from Universitat Poliecnica de Valencia was supported by the Spanish MINECO (TIN2017-82972-R) | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | The Journal of Supercomputing (Online) | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Malleability | es_ES |
dc.subject | Linear algebra | es_ES |
dc.subject | BLAS | es_ES |
dc.subject | Multicore architectures | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Integration and exploitation of intra-routine malleability in BLIS | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s11227-019-03078-z | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-82972-R/ES/TECNICAS ALGORITMICAS PARA COMPUTACION DE ALTO RENDIMIENTO CONSCIENTE DEL CONSUMO ENERGETICO Y RESISTENTE A ERRORES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CAM//S2018%2FTCS-4423 / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093684-B-I00/ES/HETEROGENEIDAD Y ESPECIALIZACION EN LA ERA POST-MOORE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2015-65277-R/ES/COMPPUTACION HETEROGENEA EFICIENTE: DEL PROCESADOR AL DATACENTER/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | Rodríguez-Sánchez, R.; Igual, FD.; Quintana-Ortí, ES. (2020). Integration and exploitation of intra-routine malleability in BLIS. The Journal of Supercomputing (Online). 76(4):2860-2875. https://doi.org/10.1007/s11227-019-03078-z | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s11227-019-03078-z | es_ES |
dc.description.upvformatpinicio | 2860 | es_ES |
dc.description.upvformatpfin | 2875 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 76 | es_ES |
dc.description.issue | 4 | es_ES |
dc.identifier.eissn | 1573-0484 | es_ES |
dc.relation.pasarela | S\417896 | es_ES |
dc.contributor.funder | Comunidad de Madrid | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.description.references | Augonnet C, Thibault S, Namyst R, Wacrenier PA (2011) StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concurr Comput Pract Exp Spec Issue Euro Par 2009(23):187–198 | es_ES |
dc.description.references | Catalán S, Castelló A, Igual FD, Rodríguez-Sánchez R, Quintana-Ortí ES (2019) Programming parallel dense matrix factorizations with look-ahead and OpenMP. Cluster Comput. https://doi.org/10.1007/s10586-019-02927-z | es_ES |
dc.description.references | Catalán S, Herrero JR, Quintana-Ortí ES, Rodríguez-Sánchez R, Van De Geijn R (2019) A case for malleable thread-level linear algebra libraries: the LU factorization with partial pivoting. IEEE Access 7:17617–17633 | es_ES |
dc.description.references | Catalán S, Igual FD, Mayo R, Rodríguez-Sánchez R, Quintana-Ortí ES (2016) Architecture-aware configuration and scheduling of matrix multiplication on asymmetric multicore processors. Cluster Comput 19(3):1037–1051 | es_ES |
dc.description.references | Chan E, Van Zee FG, Bientinesi P, Quintana-Ortí ES, Quintana-Ortí G, van de Geijn R (2008)Supermatrix: A multithreaded runtime scheduling system for algorithms-by-blocks. In: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, New York, pp 123–132 | es_ES |
dc.description.references | Corporation I (2019) Intel ® math kernel library developer reference. Tech rep, Intel Corporation. https://software.intel.com/sites/default/files/mkl-2019-developer-reference-c_2.pdf. Accessed 13 Nov 2019 | es_ES |
dc.description.references | Dolz MF, Igual FD, Ludwig T, Piñuel L, Quintana-Ortí ES (2015) Balancing task- and data-level parallelism to improve performance and energy consumption of matrix computations on the intel xeon phi. Comput Electr Eng 46:95–111 | es_ES |
dc.description.references | Dongarra JJ, Du Croz J, Hammarling S, Duff IS (1990) A set of level 3 basic linear algebra subprograms. ACM Trans Math Softw 16(1):1–17 | es_ES |
dc.description.references | Duran A, Ayguadé E, Badia RM, Labarta J, Martinell L, Martorell X, Planas J (2011) OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Process Lett 21(2):173–193 | es_ES |
dc.description.references | Gates M, Luszczek P, Abdelfattah A, Kurzak J, Dongarra J, Arturov K, Cecka C, Freitag C (2018) C++ API for BLAS and LAPACK. Tech Rep 2, ICL-UT-17-03 (2017). Revision 21 Feb 2018 | es_ES |
dc.description.references | Guennebaud G, Jacob B et al (2019) Eigen v3. http://eigen.tuxfamily.org. Accessed 13 Nov 2019 | es_ES |
dc.description.references | LAPACK project home page. http://www.netlib.org/lapack. Accessed 13 Nov 2019 | es_ES |
dc.description.references | Leung J, Kelly L, Anderson JH (2004) Handbook of scheduling: algorithms, models, and performance analysis. CRC Press Inc, Boca Raton, FL | es_ES |
dc.description.references | Smith TM, van de Geijn RA, Smelyanskiy M, Hammond JR, Van Zee FG (2014) Anatomy of high-performance many-threaded matrix multiplication. In: 28th IEEE International Parallel & Distributed Processing Symposium | es_ES |
dc.description.references | Strazdins P (1998) A comparison of lookahead and algorithmic blocking techniques for parallel matrix factorization. Tech Rep TR-CS-98-07, Department of Computer Science, The Australian National University, Canberra 0200 ACT, Australia | es_ES |
dc.description.references | Whaley RC, Petitet A, Dongarra JJ (2001) Automated empirical optimization of software and the ATLAS project. Parallel Comput 27(1–2):3–35 | es_ES |
dc.description.references | Van Zee FG, Implementing high-performance complex matrix multiplication via the 1m method. ACM Trans Math Softw (submitted) | es_ES |
dc.description.references | Van Zee FG, van de Geijn RA (2015) BLIS: a framework for rapidly instantiating BLAS functionality. ACM Trans Math Softw 41(3):14:1–14:33 | es_ES |
dc.description.references | Van Zee FG, Parikh DN, van de Geijn RA, Supporting mixed-domain mixed-precision matrix multiplication within the BLIS framework. ACM Trans Math Softw (submitted) | es_ES |
dc.description.references | Van Zee FG, Smith T (2017) Implementing high-performance complex matrix multiplication via the 3m and 4m methods. ACM Trans Math Softw 44(1):7:1–7:36 | es_ES |
dc.description.references | Van Zee FG, Smith T, Igual FD, Smelyanskiy M, Zhang X, Kistler M, Austel V, Gunnels J, Low TM, Marker B, Killough L, van de Geijn RA (2016) The BLIS framework: experiments in portability. ACM Trans Math Softw 42(2):12:1–12:19 | es_ES |