How to contact me:
My research interests include simulation of parallel and distributed systems, scientific workflows, scheduling, and working with scientists from other fields.
I am currently involved in the following projects:
In a previous life, I was assistant professor at the Université Henri Poincaré, Nancy 1
I made a lot of teaching there.
In 2018-2019, I also participate in the ASR7 module on concurrent programming of the Fall and Spring semesters at the University Claude Bernard, Lyon 1.
The complete list of my publications is available.
You may also want to search in the DBLP Computer Science Bibliography or on Google Scholar.
|Conference paper: Henri Casanova, Arnaud Legrand, Martin Quinson and Frédéric Suter. SMPI Courseware: Teaching Distributed-Memory Computing with MPI in Simulation. In the Workshop on Education for High-Performance Computing (EduHPC), November 2018. Best paper award.|
|Conference paper: Henri Casanova, Suraj Pandey, James Oeth, Ryan Tanaka, Frédéric Suter and Rafael Ferreira da Silva. WRENCH: Workflow Management System Simulation Workbench. In the 13th Workshop on Workflows in Support of Large-Scale Science (WORKS), November 2018.|
|Conference paper: Anchen Chai, Sorina Camarasu-Pop, Tristan Glatard, Hugues Benoit-Cattin and Frédéric Suter. Evaluation through Realistic Simulations of File Replication Strategies for Large Heterogeneous Distributed Systems. In the 16th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar), August 2018. Best workshop paper award.|
|Conference paper: Frédéric Azevedo, Luc Gombert and Frédéric Suter. Reducing the Human-in-the-Loop Component of the Scheduling of Large HTC Workloads. In the 22nd Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP), May 2018.|
|Journal paper: Augustin Degomme, Arnaud Legrand, George Markomanolis, Martin Quinson, Mark Stillwell, and Frédéric Suter. Simulating MPI applications: the SMPI approach. In IEEE Transactions on Parallel and Distributed Systems , 28(8):2387-2400, August 2017.|
|Conference paper: Tchimou N'takpé and Frédéric Suter. Don't Hurry be Happy: a Deadline-based Backfilling Approach. In the 21st Workshop on Job Scheduling Strategies for Parallel Processing (JSSPP), June 2017.|
|Conference paper: Anchen Chai, Mohammad-Mahdi Bazm, Sorina Camarasu-Pop, Tristan Glatard, Hugues Benoit-Cattin and Frédéric Suter. Modeling Distributed Platforms from Application Traces for Realistic File Transfer Simulation. In the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May 2017.|
|Journal paper: Henri Casanova, Anshul Gupta, and Frédéric Suter. Toward More Scalable Off-Line Simulations of MPI Applications. In Parallel Processing Letters , 25(3):1541002, September 2015.|
|Conference paper: Adrien Lèbre, Arnaud Legrand, Frédéric Suter and Pierre Veyre. Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API. In the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Shenzen, China, May 2015.|
|Journal paper: Henri Casanova, Frédéric Desprez, George Markomanolis, and Frédéric Suter. Simulation of MPI Applications with Time-Independent Traces. In Concurrency and Computation: Practice and Experience, 27(5):1145-1168, April 2015.|
|Journal paper: Henri Casanova, Arnaud Giersch, Arnaud Legrand, Martin Quindon, and Frédéric Suter. Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms. In Journal of Parallel and Distributed Computing, 74(10):2899-2917, October 2014.|
|Book chapter: Hamid Arabnejad, Jorge Barbosa and Frédéric Suter. High-Performance Computing on Complex Environments. Chapter Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems, pages 147-168. John Wiley & Sons, June 2014. ISBN: 978-1-118-71205-4|
Since 2009, I'm one of the core developpers of SimGrid toolkit.
SimGrid is a toolkit that provides core functionalities for the simulation of distributed applications in heterogeneous distributed environments. The simulation engine uses algorithmic and implementation techniques toward the fast simulation of large systems on a single machine. The models are theoretically grounded and experimentally validated. The results are reproducible, enabling better scientific practices. Its models of networks, cpus and disks are adapted to (Data)Grids, P2P, Clouds, Clusters and HPC, allowing multi-domain studies.
I'm in charge of the SimDag API, the simulation of storage systems, the off-line simulation of MPI applications, and am actively involved in the redesign of the SimGrid internals.
Some other interesting software development projects are available on my github page.