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Reaño, Carlos

Personal Information:

Position: PostDoc Researcher Reaño, Carlos
Email: This e-mail address is being protected from spambots. You need JavaScript enabled to view it
Phone or fax: +34963877007x75738
Location: Valencia
Description:

Carlos Reaño received a BS degree in Computer Engineering from University of Valencia, Spain, in 2008. He also holds a MS degree in Software Engineering, Formal Methods and Information Systems from Technical University of Valencia, Spain, since 2012, and a PhD in Computer Engineering from the same university since 2017. He is currently a postdoctoral researcher at the Department of Computer Engineering (DISCA) of Technical University of Valencia, where he is working in the rCUDA project (www.rcuda.net) since 2011. His research is mainly focused on the virtualization of remote GPUs. He has published several papers in peer-reviewed conferences and journals, and has also participated as reviewer in some conferences and journals.

Publications

  • Reaño, C. (2017). On the Enhancement of Remote GPU Virtualization in High Performance Clusters. Phd Thesis, Universitat Politècnica de València. [More] 
  • Reaño, C., Silla, F. & Duato, J (2017). Enhancing the rCUDA Remote GPU Virtualization Framework: from a Prototype to a Production Solution. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Madrid, Spain, May 14-17, 2017, pages 695-698. [More] 
  • Reaño, C. & Silla, F (2017). A Comparative Performance Analysis of Remote GPU Virtualization over Three Generations of GPUs. In 46th International Conference on Parallel Processing Workshops, ICPP Workshops 2017, Bristol, United Kingdom, August 14-17, 2017, pages 121-128. [More] 
  • Prades, J., Varghese, B., Reaño, C. & Silla, F. (2017). Multi-tenant virtual GPUs for optimising performance of a financial risk application. J. Parallel Distrib. Comput., 108, 28-44. [More] 
  • Silla, F., Iserte, S., Reaño, C. & Prades, J. (2017). On the benefits of the remote GPU virtualization mechanism: The rCUDA case. Concurrency and Computation: Practice and Experience, 29(13). [More] 
  • Prades, J., Campos, F., Reaño, C. & Silla, F (2016). GPGPU as a Service: Providing GPU-Acceleration Services to Federated Cloud Systems. Developing Interoperable and Federated Cloud Architecture. [More] 
  • Iserte, S., Prades, J., Reaño, C. & Silla, F (2016). Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm. In IEEE/ACM 16th International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2016, Cartagena, Colombia, May 16-19, 2016, pages 98-101. [More] 
  • Perez, F., Reaño, C. & Silla, F (2016). Providing CUDA Acceleration to KVM Virtual Machines in InfiniBand Clusters with rCUDA. In Distributed Applications and Interoperable Systems - 16th IFIP WG 6.1 International Conference, DAIS 2016, Held as Part of the 11th International Federated Conference on Distributed Computing Techniques, Dis, pages 82-95. [More] 
  • Silla, F., Prades, J., Iserte, S. & Reaño, C (2016). Remote GPU Virtualization: Is It Useful?. In 2nd IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era HiPINEB@HPCA 2016, Barcelona, Spain, March 12, 2016, pages 41-48. [More] 
  • Reaño, C. & Silla, F (2016). Performance Evaluation of the NVIDIA Pascal GPU Architecture: Early Experiences. In 18th IEEE International Conference on High Performance Computing and Communications; 14th IEEE International Conference on Smart City; 2nd IEEE International Conference on Data Science and Systems, HPCC/Smar, pages 1234-1235. [More] 
  • Reaño, C. & Silla, F (2016). Extending rCUDA with Support for P2P Memory Copies between Remote GPUs. In 18th IEEE International Conference on High Performance Computing and Communications; 14th IEEE International Conference on Smart City; 2nd IEEE International Conference on Data Science and Systems, HPCC/Smar, pages 789-796. [More] 
  • Prades, J., Reaño, C. & Silla, F (2016). CUDA acceleration for Xen virtual machines in infiniband clusters with rCUDA. In Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2016, Barcelona, Spain, March 12-16, 2016, pages 35:1-35:2. [More] 
  • Reaño, C., Silla, F. & Leslie, M. J (2016). schedGPU: Fine-grain dynamic and adaptative scheduling for GPUs. In International Conference on High Performance Computing & Simulation, HPCS 2016, Innsbruck, Austria, July 18-22, 2016, pages 993-997. [More] 
  • Reaño, C. & Silla, F (2016). Reducing the performance gap of remote GPU virtualization with InfiniBand Connect-IB. In IEEE Symposium on Computers and Communication, ISCC 2016, Messina, Italy, June 27-30, 2016, pages 920-925. [More] 
  • Reaño, C., Silla, F., Castelló, A., Na, A. J., Mayo, R., Quintana-Ortí, E. S. et al. (2015). Improving the user experience of the rCUDA remote GPU virtualization framework. Concurrency and Computation: Practice and Experience, 27(14), 3746-3770. [More] 
  • Varghese, B., Prades, J., Reaño, C. & Silla, F (2015). Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application. In 11th IEEE International Conference on e-Science, e-Science 2015, Munich, Germany, August 31 - September 4, 2015, pages 47-56. [More] 
  • Reaño, C., Perez, F. & Silla, F (2015). On the Design of a Demo for Exhibiting rCUDA. In 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2015, Shenzhen, China, May 4-7, 2015, pages 1169-1172. [More] 
  • Reaño, C. & Silla, F (2015). A Performance Comparison of CUDA Remote GPU Virtualization Frameworks. In 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015, Chicago, IL, USA, September 8-11, 2015, pages 488-489. [More] 
  • Reaño, C. & Silla, F (2015). InfiniBand Verbs Optimizations for Remote GPU Virtualization. In 2015 IEEE International Conference on Cluster Computing, CLUSTER 2015, Chicago, IL, USA, September 8-11, 2015, pages 825-832. [More] 
  • Reaño, C. & Silla, F (2015). A Live Demo on Remote GPU Accelerated Deep Learning Using the rCUDA Middleware. In Proceedings of the Posters and Demos Session of the 16th International Middleware Conference, Middleware Posters and Demos 2015, Vancouver, BC, Canada, December 7-11, 2015, pages 3:1-3:2. [More] 
  • Reaño, C., Silla, F., Shainer, G. & Schultz, S (2015). Local and Remote GPUs Perform Similar with EDR 100G InfiniBand. In Proceedings of the Industrial Track of the 16th International Middleware Conference, Middleware Industry 2015, Vancouver, BC, Canada, December 7-11, 2015, pages 4:1-4:7. [More] 
  • Peña, A. J., Reaño, C., Silla, F., Mayo, R., Quintana-Ortí, E. S. & Duato, J. (2014). A complete and efficient CUDA-sharing solution for HPC clusters. Parallel Computing, 40(10), 574-588. [More] 
  • Reaño, C., Silla, F., Peña, A. J., Shainer, G., Schultz, S., Gimeno, A. C. et al (2014). Boosting the performance of remote GPU virtualization using InfiniBand connect-IB and PCIe 3.0. In 2014 IEEE International Conference on Cluster Computing, CLUSTER 2014, Madrid, Spain, September 22-26, 2014, pages 266-267. [More] 
  • Iserte, S., Gimeno, A. C., Mayo, R., Quintana-Ortí, E. S., Silla, F., Duato, J. et al (2014). SLURM Support for Remote GPU Virtualization: Implementation and Performance Study. In 26th IEEE International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2014, Paris, France, October 22-24, 2014, pages 318-325. [More] 
  • Reaño, C., Peña, A. J., Silla, F., Mayo, R., Quintana-Ortí, E. S. & Duato, J (2013). Influence of InfiniBand FDR on the Performance of Remote GPU Virtualization. In International Conference on Cluster Computing (Cluster). [More] 
 

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