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ORCIDCarlos Reaño
Person information
- affiliation: University of Valencia, Spain
- affiliation (former): Queen's University Belfast, UK
- affiliation (former, PhD): Polytechnic University of Valencia, Spain
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2020 – today
- 2024
[j23]Cristian Peñaranda
, Carlos Reaño, Federico Silla:
Hybrid-Smash: A Heterogeneous CPU-GPU Compression Library. IEEE Access 12: 32706-32723 (2024)- [j22]Carlos Reaño, Federico Silla, Blesson Varghese:
Accelerator virtualization. Concurr. Comput. Pract. Exp. 36(10) (2024) - [j21]Carlos Reaño, José V. Riera, Verónica Romero, Pedro Morillo, Sergio Casas-Yrurzum:
A cloud-edge computing architecture for monitoring protective equipment. J. Cloud Comput. 13(1): 82 (2024) - [j20]Javier Prades, Carlos Reaño, Federico Silla:
NGS: A network GPGPU system for orchestrating remote and virtual accelerators. J. Syst. Archit. 151: 103138 (2024) - [j19]Cristian Peñaranda, Carlos Reaño, Federico Silla:
A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers. Sensors 24(14): 4649 (2024) - [j18]Carlos Reaño, Ricardo Olanda, Elvira Baydal, Mariano Pérez, Juan M. Orduña:
Accelerating the detection of DNA differentially methylated regions using multiple GPUs. J. Supercomput. 80(9): 13386-13410 (2024) - [c34]Moustafa Faheem, Vishal Sharma, Adrian Murphy, Carlos Reaño:
Accelerating scalable manufacturing models with Discrete Event Simulation as-a-service. FMEC 2024: 185-189 - 2023
- [j17]Cristian Peñaranda, Carlos Reaño, Federico Silla:
Exploring the use of data compression for accelerating machine learning in the edge with remote virtual graphics processing units. Concurr. Comput. Pract. Exp. 35(20) (2023) - [j16]Jason Kennedy, Vishal Sharma, Blesson Varghese, Carlos Reaño:
Multi-Tier GPU Virtualization for Deep Learning in Cloud-Edge Systems. IEEE Trans. Parallel Distributed Syst. 34(7): 2107-2123 (2023) - [c33]Robert Williamson, Jesús Martínez del Rincón, Anastasios Koidis, Carlos Reaño:
RGB-2-Hyper-Spectral Image Reconstruction for Food Science Using Encoder/Decoder Neural Architectures. ISCC 2023: 872-875 - [c32]Majed Alsharari, Son T. Mai, Romain Garnier, Carlos Reaño, Roger F. Woods:
An Intelligent Image Processing System for Enhancing Blood Vessel Segmentation on Low-Power SoC. SAMOS 2023: 123-138 - 2022
- [c31]Majed Alsharari, Lorenzo Niemitz, Simon Sorensen, Roger F. Woods, Ray Burke, Stefan Andersson-Engels, Carlos Reaño, Son T. Mai:
Multi-spectral In-Vivo FPGA-Based Surgical Imaging. ARC 2022: 103-117 - [c30]Cristian Peñaranda, Carlos Reaño, Federico Silla:
Smash: A Compression Benchmark with AI Datasets from Remote GPU Virtualization Systems. HAIS 2022: 236-248 - [c29]Cristian Peñaranda Cebrián, Carlos Reaño, Federico Silla:
Pipelined Compression in Remote GPU Virtualization Systems using rCUDA: Early Experiences. ICPP Workshops 2022: 7:1-7:7 - [c28]Ryan Dyson, Carlos Reaño:
Litener: An Accelerator-Enabled Lightweight Container for Edge Computing. IPDPS Workshops 2022: 987-994 - [c27]Moustafa Faheem, Adrian Murphy, Vishal Sharma, Carlos Reaño:
Discrete Event Simulation in Cloud-Edge Manufacturing Environments: Performance, Energy and Cost Trade-offs. ISPA/BDCloud/SocialCom/SustainCom 2022: 307-314 - 2021
- [j15]Sergio Iserte, Javier Prades, Carlos Reaño, Federico Silla:
Improving the management efficiency of GPU workloads in data centers through GPU virtualization. Concurr. Comput. Pract. Exp. 33(2) (2021) - [j14]Carlos Reaño, Federico Silla:
Redesigning the rCUDA communication layer for a better adaptation to the underlying hardware. Concurr. Comput. Pract. Exp. 33(14) (2021) - [j13]Carlos Reaño, Federico Silla, Blesson Varghese:
Editorial. J. Parallel Distributed Comput. 147: 268-269 (2021) - [c26]Jason Kennedy, Blesson Varghese, Carlos Reaño:
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries. ICFEC 2021: 37-44 - [c25]Moustafa Faheem, Adrian Murphy, Carlos Reaño:
GPU-Accelerated Discrete Event Simulations: Towards Industry 4.0 Manufacturing. ISCC 2021: 1-7 - [i5]Jason Kennedy, Blesson Varghese, Carlos Reaño:
AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries. CoRR abs/2103.04930 (2021) - 2020
- [j12]Javier Prades, Baldomero Imbernón, Carlos Reaño, Jorge Peña-García, José Pedro Cerón-Carrasco, Federico Silla, Horacio Pérez Sánchez:
Maximizing resource usage in multifold molecular dynamics with rCUDA. Int. J. High Perform. Comput. Appl. 34(1) (2020) - [j11]Federico Silla, Javier Prades, Elvira Baydal, Carlos Reaño:
Improving the performance of physics applications in atom-based clusters with rCUDA. J. Parallel Distributed Comput. 137: 160-178 (2020)
2010 – 2019
- 2019
- [j10]Javier Prades, Carlos Reaño, Federico Silla:
On the effect of using rCUDA to provide CUDA acceleration to Xen virtual machines. Clust. Comput. 22(1): 185-204 (2019) - [j9]Carlos Reaño, Federico Silla:
On the support of inter-node P2P GPU memory copies in rCUDA. J. Parallel Distributed Comput. 127: 28-43 (2019) - [j8]Carlos Reaño, Javier Prades, Federico Silla:
Analyzing the performance/power tradeoff of the rCUDA middleware for future exascale systems. J. Parallel Distributed Comput. 132: 344-362 (2019) - 2018
- [j7]Blesson Varghese, Carlos Reaño, Federico Silla:
Accelerator Virtualization in Fog Computing: Moving from the Cloud to the Edge. IEEE Cloud Comput. 5(6): 28-37 (2018) - [j6]Carlos Reaño, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese:
Intra-Node Memory Safe GPU Co-Scheduling. IEEE Trans. Parallel Distributed Syst. 29(5): 1089-1102 (2018) - [c24]Carlos Reaño, Javier Prades, Federico Silla:
Improving the Efficiency of Future Exascale Systems with rCUDA. HiPINEB@HPCA 2018: 40-47 - [c23]Carlos Reaño, Javier Prades, Federico Silla:
Exploring the Use of Remote GPU Virtualization in Low-Power Systems for Bioinformatics Applications. ICPP Workshops 2018: 8:1-8:8 - [c22]Javier Prades, Carlos Reaño, Federico Silla, Baldomero Imbernón, Horacio Pérez Sánchez, José M. Cecilia:
Increasing Molecular Dynamics Simulations Throughput by Virtualizing Remote GPUs with rCUDA. ICPP Workshops 2018: 9:1-9:8 - [c21]Federico Silla, Javier Prades, Carlos Reaño:
Leveraging rCUDA for Enhancing Low-Power Deployments in the Physics Domain. ICPP Workshops 2018: 17:1-17:8 - [i4]Blesson Varghese, Carlos Reaño, Federico Silla:
Accelerator Virtualization in Fog Computing: Moving From the Cloud to the Edge. CoRR abs/1810.06046 (2018) - 2017
- [b1]Carlos Reaño:
On the Enhancement of Remote GPU Virtualization in High Performance Clusters. Technical University of Valencia, Spain, 2017 - [j5]Federico Silla, Sergio Iserte, Carlos Reaño, Javier Prades:
On the benefits of the remote GPU virtualization mechanism: The rCUDA case. Concurr. Comput. Pract. Exp. 29(13) (2017) - [j4]Javier Prades, Blesson Varghese, Carlos Reaño, Federico Silla:
Multi-tenant virtual GPUs for optimising performance of a financial risk application. J. Parallel Distributed Comput. 108: 28-44 (2017) - [c20]Carlos Reaño, Federico Silla, José Duato:
Enhancing the rCUDA Remote GPU Virtualization Framework: from a Prototype to a Production Solution. CCGrid 2017: 695-698 - [c19]Carlos Reaño, Federico Silla:
A Comparative Performance Analysis of Remote GPU Virtualization over Three Generations of GPUs. ICPP Workshops 2017: 121-128 - [i3]Carlos Reaño, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese:
Intra-node Memory Safe GPU Co-Scheduling. CoRR abs/1712.04495 (2017) - 2016
- [j3]Carlos Reaño, Federico Silla:
Tuning remote GPU virtualization for InfiniBand networks. J. Supercomput. 72(12): 4520-4545 (2016) - [c18]Sergio Iserte, Javier Prades, Carlos Reaño, Federico Silla:
Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm. CCGrid 2016: 98-101 - [c17]Ferran Perez, Carlos Reaño, Federico Silla:
Providing CUDA Acceleration to KVM Virtual Machines in InfiniBand Clusters with rCUDA. DAIS 2016: 82-95 - [c16]Federico Silla, Javier Prades, Sergio Iserte, Carlos Reaño:
Remote GPU Virtualization: Is It Useful? HiPINEB@HPCA 2016: 41-48 - [c15]Carlos Reaño, Federico Silla:
Extending rCUDA with Support for P2P Memory Copies between Remote GPUs. HPCC/SmartCity/DSS 2016: 789-796 - [c14]Carlos Reaño, Federico Silla:
Performance Evaluation of the NVIDIA Pascal GPU Architecture: Early Experiences. HPCC/SmartCity/DSS 2016: 1234-1235 - [c13]Carlos Reaño, Federico Silla, Matthew J. Leslie:
schedGPU: Fine-grain dynamic and adaptative scheduling for GPUs. HPCS 2016: 993-997 - [c12]Carlos Reaño, Federico Silla:
Reducing the performance gap of remote GPU virtualization with InfiniBand Connect-IB. ISCC 2016: 920-925 - [c11]Javier Prades, Carlos Reaño, Federico Silla:
CUDA acceleration for Xen virtual machines in infiniband clusters with rCUDA. PPoPP 2016: 35:1-35:2 - [i2]Javier Prades, Blesson Varghese, Carlos Reaño, Federico Silla:
Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application. CoRR abs/1606.04473 (2016) - 2015
- [j2]Carlos Reaño, Federico Silla, Adrián Castelló, Antonio J. Peña, Rafael Mayo, Enrique S. Quintana-Ortí, José Duato:
Improving the user experience of the rCUDA remote GPU virtualization framework. Concurr. Comput. Pract. Exp. 27(14): 3746-3770 (2015) - [c10]Carlos Reaño, Ferran Perez, Federico Silla:
On the Design of a Demo for Exhibiting rCUDA. CCGRID 2015: 1169-1172 - [c9]Carlos Reaño, Federico Silla:
A Performance Comparison of CUDA Remote GPU Virtualization Frameworks. CLUSTER 2015: 488-489 - [c8]Carlos Reaño, Federico Silla:
InfiniBand Verbs Optimizations for Remote GPU Virtualization. CLUSTER 2015: 825-832 - [c7]Blesson Varghese, Javier Prades, Carlos Reaño, Federico Silla:
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application. e-Science 2015: 47-56 - [c6]Carlos Reaño, Federico Silla:
A Live Demo on Remote GPU Accelerated Deep Learning Using the rCUDA Middleware. Middleware Posters and Demos 2015: 3:1-3:2 - [c5]Carlos Reaño, Federico Silla, Gilad Shainer, Scot Schultz:
Local and Remote GPUs Perform Similar with EDR 100G InfiniBand. Middleware Industry 2015: 4:1-4:7 - [i1]Blesson Varghese, Javier Prades, Carlos Reaño, Federico Silla:
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application. CoRR abs/1508.02558 (2015) - 2014
- [j1]Antonio J. Peña, Carlos Reaño, Federico Silla, Rafael Mayo, Enrique S. Quintana-Ortí, José Duato:
A complete and efficient CUDA-sharing solution for HPC clusters. Parallel Comput. 40(10): 574-588 (2014) - [c4]Carlos Reaño, Federico Silla, Antonio J. Peña, Gilad Shainer, Scot Schultz, Adrián Castelló, Enrique S. Quintana-Ortí, José Duato:
Boosting the performance of remote GPU virtualization using InfiniBand connect-IB and PCIe 3.0. CLUSTER 2014: 266-267 - [c3]Sergio Iserte, Adrián Castelló, Rafael Mayo, Enrique S. Quintana-Ortí, Federico Silla, José Duato, Carlos Reaño, Javier Prades:
SLURM Support for Remote GPU Virtualization: Implementation and Performance Study. SBAC-PAD 2014: 318-325 - 2013
- [c2]Carlos Reaño, Rafael Mayo, Enrique S. Quintana-Ortí, Federico Silla, José Duato, Antonio J. Peña:
Influence of InfiniBand FDR on the performance of remote GPU virtualization. CLUSTER 2013: 1-8 - 2012
- [c1]Carlos Reaño, Antonio J. Peña, Federico Silla, José Duato, Rafael Mayo, Enrique S. Quintana-Ortí:
CU2rCU: Towards the complete rCUDA remote GPU virtualization and sharing solution. HiPC 2012: 1-10
Coauthor Index
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