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Manuel F. Dolz
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- affiliation: Charles III University of Madrid
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2020 – today
- 2024
- [j45]Antonio Maciá-Lillo
, Sergio Barrachina
Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors. IEEE Internet Things J. 11(15): 26203-26219 (2024) - [j44]Jose A. Belloch
Urban sound classification using neural networks on embedded FPGAs. J. Supercomput. 80(9): 13176-13186 (2024) - [j43]Guillermo Alaejos, Héctor Martínez, Adrián Castelló, Manuel F. Dolz, Francisco D. Igual, Pedro Alonso-Jordá, Enrique S. Quintana-Ortí:
Automatic generation of ARM NEON micro-kernels for matrix multiplication. J. Supercomput. 80(10): 13873-13899 (2024) - 2023
- [j42]Sergio Barrachina, Adrián Castelló
Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs. Computing 105(5): 915-934 (2023) - [j41]Adrián Castelló
Analyzing the impact of the MPI allreduce in distributed training of convolutional neural networks. Computing 105(5): 1101-1119 (2023) - [j40]Sergio Barrachina, Adrián Castelló
Reformulating the direct convolution for high-performance deep learning inference on ARM processors. J. Syst. Archit. 135: 102806 (2023) - [j39]Manuel F. Dolz
Performance-energy trade-offs of deep learning convolution algorithms on ARM processors. J. Supercomput. 79(9): 9819-9836 (2023) - [j38]Manuel F. Dolz
Efficient and portable Winograd convolutions for multi-core processors. J. Supercomput. 79(10): 10589-10610 (2023) - [i4]José Duato, José I. Mestre, Manuel F. Dolz, Enrique S. Quintana-Ortí:
GreenLightningAI: An Efficient AI System with Decoupled Structural and Quantitative Knowledge. CoRR abs/2312.09971 (2023) - 2022
- [j37]Sergio Barrachina
Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors. J. Parallel Distributed Comput. 167: 240-254 (2022) - [j36]Adrián Castelló
High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS. J. Syst. Archit. 125: 102459 (2022) - [j35]Sergio Barrachina, Adrián Castelló
BestOf: an online implementation selector for the training and inference of deep neural networks. J. Supercomput. 78(16): 17543-17558 (2022) - [c32]Manuel F. Dolz
Towards Portable Realizations of Winograd-based Convolution with Vector Intrinsics and OpenMP. PDP 2022: 39-46 - [c31]Manuel F. Dolz
Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor. SBAC-PAD 2022: 160-169 - 2021
- [j34]Maria Barreda, Manuel F. Dolz
Convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product. Int. J. High Perform. Comput. Appl. 35(3) (2021) - [j33]Sergio Barrachina
PyDTNN: A user-friendly and extensible framework for distributed deep learning. J. Supercomput. 77(9): 9971-9987 (2021) - [c30]Sergio Barrachina
A Flexible Research-Oriented Framework for Distributed Training of Deep Neural Networks. IPDPS Workshops 2021: 730-739 - [c29]Adrián Castelló
Performance Modeling for Distributed Training of Convolutional Neural Networks. PDP 2021: 99-108 - [c28]Adrián Castelló
Evaluation of MPI Allreduce for Distributed Training of Convolutional Neural Networks. PDP 2021: 109-116 - [i3]Adrián Castelló, Sergio Barrachina, Manuel F. Dolz, Enrique S. Quintana-Ortí, Pau San Juan:
High performance and energy efficient inference for deep learning on ARM processors. CoRR abs/2105.09187 (2021) - [i2]Julio Silva-Rodríguez, Manuel F. Dolz, Miguel Ferrer, Adrián Castelló, Valery Naranjo, Gema Piñero
Acoustic Echo Cancellation using Residual U-Nets. CoRR abs/2109.09686 (2021) - 2020
- [j32]Manuel F. Dolz
A pipeline for the QR update in digital signal processing. Comput. Math. Methods 2(1) (2020) - [j31]Javier López-Gómez, David del Rio Astorga, Manuel F. Dolz
Detecting semantic violations of lock-free data structures through C++ contracts. J. Supercomput. 76(7): 5057-5078 (2020) - [j30]Maria Barreda, Manuel F. Dolz
Performance modeling of the sparse matrix-vector product via convolutional neural networks. J. Supercomput. 76(11): 8883-8900 (2020) - [c27]Pablo San Juan, Adrián Castelló
High Performance and Portable Convolution Operators for Multicore Processors. SBAC-PAD 2020: 91-98 - [i1]Pablo San Juan, Adrián Castelló, Manuel F. Dolz, Pedro Alonso-Jordá, Enrique S. Quintana-Ortí:
High Performance and Portable Convolution Operators for ARM-based Multicore Processors. CoRR abs/2005.06410 (2020)
2010 – 2019
- 2019
- [j29]Javier López-Gómez, Javier Fernández Muñoz, David del Rio Astorga, Manuel F. Dolz
Exploring stream parallel patterns in distributed MPI environments. Parallel Comput. 84: 24-36 (2019) - [j28]Manuel F. Dolz
A pipeline structure for the block QR update in digital signal processing. J. Supercomput. 75(3): 1470-1482 (2019) - [j27]David del Rio Astorga, Manuel F. Dolz
Hybrid static-dynamic selection of implementation alternatives in heterogeneous environments. J. Supercomput. 75(8): 4098-4113 (2019) - [j26]Raul Torres
A similarity study of I/O traces via string kernels. J. Supercomput. 75(12): 7814-7826 (2019) - [c26]Adrián Castelló
Theoretical Scalability Analysis of Distributed Deep Convolutional Neural Networks. CCGRID 2019: 534-541 - [c25]Adrián Castelló
Analysis of model parallelism for distributed neural networks. EuroMPI 2019: 7:1-7:10 - 2018
- [j25]Manuel F. Dolz
Towards Automatic Parallelization of Stream Processing Applications. IEEE Access 6: 39944-39961 (2018) - [j24]David del Rio Astorga, Manuel F. Dolz
Paving the way towards high-level parallel pattern interfaces for data stream processing. Future Gener. Comput. Syst. 87: 228-241 (2018) - [j23]David del Rio Astorga, Manuel F. Dolz
An adaptive offline implementation selector for heterogeneous parallel platforms. Int. J. High Perform. Comput. Appl. 32(6) (2018) - [j22]David del Rio Astorga, Manuel F. Dolz
Finding parallel patterns through static analysis in C++ applications. Int. J. High Perform. Comput. Appl. 32(6) (2018) - [j21]Francisco Almeida
Energy monitoring as an essential building block towards sustainable ultrascale systems. Sustain. Comput. Informatics Syst. 17: 27-42 (2018) - [j20]Julian M. Kunkel, Manuel F. Dolz
Understanding hardware and software metrics with respect to power consumption. Sustain. Comput. Informatics Syst. 17: 43-54 (2018) - [c24]Raul Torres, Thomas Ludwig, Julian M. Kunkel, Manuel F. Dolz
Comparison of Clang Abstract Syntax Trees using String Kernels. HPCS 2018: 106-113 - [c23]Placido Fernández, David del Rio Astorga, Manuel F. Dolz
Parallelizing and Optimizing LHCb-Kalman for Intel Xeon Phi KNL Processors. PDP 2018: 741-750 - [c22]Javier Fernández Muñoz, Manuel F. Dolz
Supporting MPI-distributed stream parallel patterns in GrPPI. EuroMPI 2018: 17:1-17:10 - 2017
- [j19]Manuel F. Dolz
Enabling semantics to improve detection of data races and misuses of lock-free data structures. Concurr. Comput. Pract. Exp. 29(15) (2017) - [j18]David del Rio Astorga, Manuel F. Dolz
A generic parallel pattern interface for stream and data processing. Concurr. Comput. Pract. Exp. 29(24) (2017) - [j17]José Ignacio Aliaga
Adapting concurrency throttling and voltage-frequency scaling for dense eigensolvers. J. Supercomput. 73(1): 29-43 (2017) - [c21]David del Rio Astorga, Manuel F. Dolz
Supporting Advanced Patterns in GrPPI, a Generic Parallel Pattern Interface. Euro-Par Workshops 2017: 55-67 - [c20]Javier Fernández
Probabilistic-Based Selection of Alternate Implementations for Heterogeneous Platforms. ICA3PP 2017: 749-758 - [c19]Raul Torres
A Novel String Representation and Kernel Function for the Comparison of I/O Access Patterns. PaCT 2017: 500-512 - 2016
- [j16]Manuel F. Dolz
An analytical methodology to derive power models based on hardware and software metrics. Comput. Sci. Res. Dev. 31(4): 165-174 (2016) - [j15]Pablo Llopis, Manuel F. Dolz
Analyzing the energy consumption of the storage data path. J. Supercomput. 72(11): 4089-4106 (2016) - [c18]David del Rio Astorga, Manuel F. Dolz
A C++ Generic Parallel Pattern Interface for Stream Processing. ICA3PP 2016: 74-87 - [c17]Javier García Blas
Porting Matlab Applications to High-Performance C++ Codes: CPU/GPU-Accelerated Spherical Deconvolution of Diffusion MRI Data. ICA3PP 2016: 630-643 - [c16]David del Rio Astorga, Manuel F. Dolz
Discovering Pipeline Parallel Patterns in Sequential Legacy C++ Codes. PMAM@PPoPP 2016: 11-19 - [c15]Manuel F. Dolz
Embedding Semantics of the Single-Producer/Single-Consumer Lock-Free Queue into a Race Detection Tool. PMAM@PPoPP 2016: 20-29 - [c14]Luis Miguel Sánchez, David del Rio Astorga, Manuel F. Dolz
CID: A Compile-Time Implementation Decider for Heterogeneous Platforms Based on C++ Attributes. UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld 2016: 1149-1156 - 2015
- [j14]Manuel F. Dolz
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 (2015) - [j13]Joseph Charles, William B. Sawyer, Manuel F. Dolz
Evaluating the performance and energy efficiency of the COSMO-ART model system. Comput. Sci. Res. Dev. 30(2): 177-186 (2015) - [j12]José Ignacio Aliaga
Are our dense linear algebra libraries energy-friendly? Comput. Sci. Res. Dev. 30(2): 187-196 (2015) - [c13]Manuel F. Dolz
ARDUPOWER: A low-cost wattmeter to improve energy efficiency of HPC applications. IGSC 2015: 1-8 - 2014
- [b1]Manuel F. Dolz:
Energy-aware matrix computacion on multirhreaded architectures. Jaume I University, Spain, 2014 - [j11]José Ignacio Aliaga
Assessing the impact of the CPU power-saving modes on the task-parallel solution of sparse linear systems. Clust. Comput. 17(4): 1335-1348 (2014) - [j10]Pedro Alonso
Enhancing performance and energy consumption of runtime schedulers for dense linear algebra. Concurr. Comput. Pract. Exp. 26(15): 2591-2611 (2014) - [j9]Pedro Alonso
Modeling power and energy consumption of dense matrix factorizations on multicore processors. Concurr. Comput. Pract. Exp. 26(17): 2743-2757 (2014) - [j8]Pedro Alonso
Modeling power and energy of the task-parallel Cholesky factorization on multicore processors. Comput. Sci. Res. Dev. 29(2): 105-112 (2014) - [j7]Maria Barreda
Automatic detection of power bottlenecks in parallel scientific applications. Comput. Sci. Res. Dev. 29(3-4): 221-229 (2014) - [j6]Pedro Alonso
Block pivoting implementation of a symmetric Toeplitz solver. J. Parallel Distributed Comput. 74(5): 2392-2399 (2014) - [j5]Robert Schöne, Jan Treibig, Manuel F. Dolz
Tools and methods for measuring and tuning the energy efficiency of HPC systems. Sci. Program. 22(4): 273-283 (2014) - [j4]Mohammed el Mehdi Diouri, Manuel F. Dolz
Assessing Power Monitoring Approaches for Energy and Power Analysis of Computers. Sustain. Comput. Informatics Syst. 4(2): 68-82 (2014) - [c12]Konstantinos Chasapis, Manuel F. Dolz
Evaluating Lustre's metadata server on a multi-socket platform. PDSW@SC 2014: 13-18 - 2013
- [j3]Pedro Alonso
Energy-efficient execution of dense linear algebra algorithms on multi-core processors. Clust. Comput. 16(3): 497-509 (2013) - [c11]Mohammed el Mehdi Diouri, Manuel F. Dolz
Solving Some Mysteries in Power Monitoring of Servers: Take Care of Your Wattmeters! EE-LSDS 2013: 3-18 - [c10]Pedro Alonso
Runtime Scheduling of the LU Factorization: Performance and Energy. EE-LSDS 2013: 153-167 - 2012
- [j2]Pedro Alonso
DVFS-control techniques for dense linear algebra operations on multi-core processors. Comput. Sci. Res. Dev. 27(4): 289-298 (2012) - [j1]Manuel F. Dolz
A simulator to assess energy saving strategies and policies in HPC workloads. ACM SIGOPS Oper. Syst. Rev. 46(2): 2-9 (2012) - [c9]Pedro Alonso
Tools for Power-Energy Modelling and Analysis of Parallel Scientific Applications. ICPP 2012: 420-429 - [c8]José Ignacio Aliaga
Leveraging Task-Parallelism in Energy-Efficient ILU Preconditioners. ICT-GLOW 2012: 55-63 - [c7]Pedro Alonso
Reducing Energy Consumption of Dense Linear Algebra Operations on Hybrid CPU-GPU Platforms. ISPA 2012: 56-62 - [c6]Maria Barreda
Binding Performance and Power of Dense Linear Algebra Operations. ISPA 2012: 63-70 - [c5]Pedro Alonso
Saving Energy in the LU Factorization with Partial Pivoting on Multi-core Processors. PDP 2012: 353-358 - 2011
- [c4]Pedro Alonso
Improving power efficiency of dense linear algebra algorithms on multi-core processors via slack control. HPCS 2011: 463-470 - [c3]Maribel Castillo
Evaluation of the Energy Performance of Dense Linear Algebra Kernels on Multi-core and Many-Core Processors. IPDPS Workshops 2011: 846-853 - [c2]Pedro Alonso, Manuel F. Dolz, Francisco D. Igual, Bryan Marker, Rafael Mayo, Enrique S. Quintana-Ortí, Robert A. van de Geijn:
Power-aware Dense Linear Algebra Implementations on Multi-core and Many-core Processors. MARC Symposium 2011: 103-106 - 2010
- [c1]Manuel F. Dolz
EnergySaving Cluster Roll: Power Saving System for Clusters. ARCS 2010: 162-173
Coauthor Index
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