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ORCIDJavier Prades
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2020 – today
- 2024
[j12]Javier Prades
, Carlos Reaño, Federico Silla:
NGS: A network GPGPU system for orchestrating remote and virtual accelerators. J. Syst. Archit. 151: 103138 (2024)- 2023
- [j11]José M. Cecilia, Juan Morales-García, Baldomero Imbernón, Javier Prades, Juan-Carlos Cano, Federico Silla:
Using remote GPU virtualization techniques to enhance edge computing devices. Future Gener. Comput. Syst. 142: 14-24 (2023) - [c15]Benjamín Arratia, Javier Prades, Salvador Peña-Haro, José Maria Cecilia, Pietro Manzoni:
BODOQUE: An Energy-Efficient Flow Monitoring System for Ephemeral Streams. MobiHoc 2023: 358-363 - [c14]Diana M. Naranjo Delgado, Manuel Contreras, Germán Moltó, Sebastián Risco, Ignacio Blanquer, Javier Prades, Federico Silla:
On the Acceleration of FaaS Using Remote GPU Virtualization. ICPE (Companion) 2023: 157-164 - 2021
- [j10]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) - 2020
- [j9]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) - [j8]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
- [j7]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) - [j6]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) - [j5]Javier Prades, Federico Silla:
GPU-Job Migration: The rCUDA Case. IEEE Trans. Parallel Distributed Syst. 30(12): 2718-2729 (2019) - 2018
- [j4]Baldomero Imbernón, Javier Prades, Domingo Giménez, José M. Cecilia, Federico Silla:
Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study. Future Gener. Comput. Syst. 79: 26-37 (2018) - [c13]Carlos Reaño, Javier Prades, Federico Silla:
Improving the Efficiency of Future Exascale Systems with rCUDA. HiPINEB@HPCA 2018: 40-47 - [c12]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 - [c11]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 - [c10]Federico Silla, Javier Prades, Carlos Reaño:
Leveraging rCUDA for Enhancing Low-Power Deployments in the Physics Domain. ICPP Workshops 2018: 17:1-17:8 - [c9]Javier Prades, Federico Silla:
Made-to-Measure GPUs on Virtual Machines with rCUDA. ICPP Workshops 2018: 19:1-19:8 - 2017
- [j3]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) - [j2]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) - [c8]Javier Prades, Federico Silla:
A Live Demo for Showing the Benefits of Applying the Remote GPU Virtualization Technique to Cloud Computing. CCGrid 2017: 735-738 - [c7]Javier Prades, Federico Silla:
Turning GPUs into Floating Devices over the Cluster: The Beauty of GPU Migration. ICPP Workshops 2017: 129-136 - 2016
- [c6]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 - [c5]Federico Silla, Javier Prades, Sergio Iserte, Carlos Reaño:
Remote GPU Virtualization: Is It Useful? HiPINEB@HPCA 2016: 41-48 - [c4]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
- [j1]Javier Prades, Federico Silla, Holger Fröning, Mondrian Nüssle, José Duato:
On the design of a new dynamic credit-based end-to-end flow control mechanism for HPC clusters. Parallel Comput. 46: 32-59 (2015) - [c3]Blesson Varghese, Javier Prades, Carlos Reaño, Federico Silla:
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application. e-Science 2015: 47-56 - [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
- [c2]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 - 2012
- [c1]Javier Prades, Federico Silla, José Duato, Holger Fröning, Mondrian Nüssle:
A New End-to-End Flow-Control Mechanism for High Performance Computing Clusters. CLUSTER 2012: 320-328
Coauthor Index
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