More computing power to accelerate your phylogenetics analysis
Computing performance often leaves DNA sequencing researchers frustrated with lengthy lead-times and high costs.
Need to speed up genome sequencing with RAxML? EDRA’s first framework application is a hardware accelerator giving you quick, secure and affordable access to computing power on Amazon Web Services.
- Best value-for-money compared to other Amazon Machine Instances
- Up to 2.5 times IT costs saving
- Easy execution of experiments on the cloud within minutes*
Subscribe for our pilot setup and get preliminary access to more computing power for faster phylogenetics analysis.
Want to get started? See on our video how to start an AWS instance and how the ERDA hardware for RAxML accelerates kernel processing up to 6 times compared to the software-optimized version.
As shown in the figure below, RAxML’s most computationally intensive task (blue square) is offloaded to the hardware accelerator (step 1); the host CPU sends all input data to the FPGA (step 2), and as soon as processing is done, the host receives all experiment results back (step 3).
Recently published articles on research conducted with the RAxML tool
- Nikolaos Alachiotis, Panagiotis Skrimponis, Manolis Pissadakis, Sundeep Rangan, Dionisios Pnevmatikatos, “Near-memory Acceleration for Scalable Phylogenetic Inference”, The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
- Maria Vasilarou, Nikolaos Alachiotis, Joanna Garefalaki, Apostolos Beloukas, Pavlos Pavlidis, “Population genomics insights into the recent evolution of SARS-CoV-2”, bioRxiv, 1/1/2020
- Nikolaos Alachiotis, Charalampos Vatsolakis, Grigorios Chrysos, Dionisios Pnevmatikatos, “RAiSD-X: A Fast and Accurate FPGA System for the Detection of Positive Selection in Thousands of Genomes”, ACM Transactions on Reconfigurable Technology and Systems (TRETS), 2019
- Bastian Pfeifer, Nikolaos Alachiotis, Pavlos Pavlidis, Michael G Schimek, “Genome Scans for Selection and Introgression based on k-nearest Neighbor Techniques”, bioRxiv, 1/1/2019
- Nikolaos Alachiotis, Charalampos Vatsolakis, Grigorios Chrysos, Dionisios Pnevmatikatos, “Accelerated Inference of Positive Selection on Whole Genomes”, Field Programmable Logic and Applications, 2018. FPL 2018. International Conference on, 2019
- Nikolaos Alachiotis, Dimitris Theodoropoulos, Dionisios Pnevmatikatos, “Versatile deployment of FPGA accelerators in disaggregated data centers: A bioinformatics case study”, 2017 27th International Conference on Field Programmable Logic and Applications (FPL), 2017
- Pavlos Pavlidis, Daniel Živković, Alexandros Stamatakis, Nikolaos Alachiotis, “SweeD: Likelihood-based detection of selective sweeps in thousands of genomes”, Molecular Biology and Evolution, 2013
*For existing AWS users