Parallelization and Optimization of Iterative Solvers on High Performance Architectures

The main objective of this thesis is to develop an optimal sparse matrix storage format and implement efficient computing kernels that accelerate the execution of the sparse matrix vector (SpMV) product on modern computer architectures. The SpMV product is an essential building brick for a myriad of numerical application codes, especially for iterative solvers and numerical simulators. Improving the performance of the SpMV product is of special interest for researchers, because it is the major bottleneck for codes where it is required. Optimizing this product on modern computer architectures requires knowledge of parallel programing paradigms, efficient parallel algorithms and a basic idea of the device architecture being targeted

A new AXT format for an efficient SpMV product using AVX-512 instructions and CUDA

The Sparse Matrix-Vector (SpMV) product is a key operation used in many scientific applications. This work proposes a new sparse matrix storage scheme, the AXT format, that improves the SpMV performance on vector capability platforms. AXT can be adapted to different platforms, improving the storage efficiency for matrices with different sparsity patterns. Intel AVX-512 instructions and CUDA are used to optimise the performances of the four different AXT subvariants. Performance comparisons are made with the Compressed Sparse Row (CSR) and AXC formats on an Intel Xeon Gold 6148 processor and an NVIDIA Tesla V100 Graphics Processing Units using 26 matrices. On the Intel platform the overall AXT performance is 18% and 44.3% higher than the AXC and CSR respectively, reaching speed-up factors of up to x7.33. On the NVIDIA platform the AXT performance is 44% and 8% higher than the AXC and CSR performances respectively, reaching speed-up factors of up to x378.5S

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-