Abstract
EDRA was an Horizon 2020 FET Launchpad project that focused on the commercialization of the Decoupled Access Execution Reconfigurable (DAER) framework - developed within the FET-HPC EXTRA project - on Amazon’s Elastic Cloud (EC2) Compute FPGA-based infrastructure. The delivered framework encapsulates DAER into a EC2 virtual machine (VM), and uses a simple, directive-based, high-level application programming interface (API) to facilitate application mapping to the underlying hardware architecture. EDRA’s Minimum Viable Product (MVP) is an accelerator for the Phylogenetic Likelihood Function (PLF), one of the cornerstone functions in most phylogenetic inference tools, achieving up to 8x performance improvement compared to optimized software implementations. Towards entering the market, research revealed that Europe is an extremely promising geographic region for focusing the project efforts on dissemination, MVP promotion and advertisement (EDRA was funded by the European Union’s Horizon 2020 research and innovation programme “FET Innovation Launchpad” under grant agreement No 851631).
Authors alphabetically: Nikolaos, Alachiotis, Andreas Brokalakis, Dionisios Pnevmatikatos, Dimitris Theodoropoulos.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Charitopoulos, G., Vatsolakis, C., Chrysos, G., Pnevmatikatos, D.: A decoupled access-execute architecture for reconfigurable accelerators. In: IEEE 18th International Conference on Computational Science and Engineering, pp. 244–247, May 2018
Ciobanu, C.B., et al.: Extra: towards an efficient open platform for reconfigurable high performance computing. In: 2015 IEEE 18th International Conference on Computational Science and Engineering, pp. 339–342 (2015)
Izquierdo-Carrasco, F., Alachiotis, N., Berger, S., Flouri, T., Pissis, S.P., Stamatakis, A.: A generic vectorization scheme and a GPU kernel for the phylogenetic likelihood library. In: 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and PhD Forum, pp. 530–538 (2013)
Malakonakis, P., Brokalakis, A., Alachiotis, N., Sotiriades, E., Dollas, A.: Exploring modern FPGA platforms for faster phylogeny reconstruction with RAxML. In: 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 97–104 (2020)
MolBioCloud. http://molbiocloud.com/. Accessed 17 May 2021
Ott, M., Stamatakis, A.: Preparing RAxML for the spec MPI benchmark suite. In: High Performance Computing in Science and Engineering, pp. 757–768, January 2010
Smith, J.E.: Decoupled access/execute computer architectures. ACM Trans. Comput. Syst. 112–119 (1984)
Stamatakis, A.: RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Theodoropoulos, D., Brokalakis, A., Alachiotis, N., Pnevmatikatos, D. (2022). EDRA: A Hardware-Assisted Decoupled Access/Execute Framework on the Digital Market. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science, vol 13227. Springer, Cham. https://doi.org/10.1007/978-3-031-04580-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-04580-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04579-0
Online ISBN: 978-3-031-04580-6
eBook Packages: Computer ScienceComputer Science (R0)