Contributors to date (with affiliations at time of contribution)
- Philip Carns, Argonne National Laboratory
- Misbah Mubarak, Argonne National Laboratory
- Shane Snyder, Argonne National Laboratory
- Jonathan P. Jenkins, Argonne National Laboratory
- Noah Wolfe, Rensselaer Polytechnic Institute
- Nikhil Jain, Lawrence Livermore National Laboratory
- Jens Domke, Univ. of Dresden
- Giorgis Georgakoudis, Lawrence Livermore National Laboratory
- Matthieu Dorier, Argonne National Laboratory
- Caitlin Ross, Rennselaer Polytechnic Institute
- Xu Yang, Illinois Institute of Tech.
- Jens Domke, Tokyo Institute of Tech.
- Xin Wang, Illinois Institute of Tech.
- Neil McGlohon, Rensselaer Polytechnic Institute
- Elsa Gonsiorowski, Rensselaer Polytechnic Institute
- Justin M. Wozniak, Argonne National Laboratory
- Robert B. Ross, Argonne National Laboratory
- Lee Savoie, Univ. of Arizona
- Ning Liu, Rensselaer Polytechnic Institute
- Jason Cope, Argonne National Laboratory
- Kevin A. Brown, Argonne National Laboratory
- Elkin Cruz, Rensselaer Polytechnic Institute
Contributions:
Misbah Mubarak (ANL) - Introduced 1-D dragonfly and enhanced torus network model. - Added quality of service in dragonfly and megafly network models. - Added MPI simulation layer to simulate MPI operations. - Updated and merged burst buffer storage model with 2-D dragonfly. - Added and validated 2-D dragonfly network model. - Added multiple workload sources including MPI communication, Scalable Workload Models, DUMPI communication traces. - Added online simulation capability with Argobots and SWMs. - Instrumented the network models to report time-stepped series statistics. - Bug fixes for network, storage and workload models with CODES.
Neil McGlohon (RPI) - Introduced Dragonfly Plus/Megafly network model. - Merged 1-D dragonfly and 2-D dragonfly network models. - Updated adaptive routing in megafly and 1-D dragonfly network models. - Extended slim fly network model's dual-rail mode to arbitrary number of rails (pending). - Implemented Quality of Service (QoS) in 1-D dragonfly network. - Implemented changes needed to allow ROSS's tiebreaker mechanism.
Nikhil Jain, Abhinav Bhatele (LLNL) - Improvements in credit-based flow control of CODES dragonfly and torus network models. - Addition of direct scheme for setting up dragonfly network topology. - Network configuration setup for custom dragonfly model. - Topology generations scripts for custom dragonfly model. - Bug fix for virtual channel deadlocks in custom dragonfly model. - Bug reporter for CODES network models. - Fat tree network setup and adaptive routing. - Pending: Merging Express mesh model to master.
Jens Domke (U. of Dresden) - Static routing in fat tree network model including ground work for dumping the topology and reading the routing tables.
John Jenkins - Introduced storage models in a separate codes-storage-repo. - Enhanced the codes-mapping APIs to map advanced combinations on PEs. - Bug fixing with network models. - Bug fixing with MPI simulation layer.
Xu Yang (IIT) - Added support for running multiple application workloads with CODES MPI Simulation layer, along with supporting scripts and utilities.
Noah Wolfe (RPI): - Added a slim fly network model based on the topology proposed by Besta, Hoefler et al. - Added a fat tree network model that supports full and pruned fat tree network. - Added a multi-rail implementation for the fat tree networks (pending). - Added a dual-rail implementation for slim fly networks (pending). - Bug reporter for CODES network models.
Caitlin Ross (RPI): - Added instrumentation so that network models can report sampled statistics over virtual time (pending). - Bug reporter for CODES models.
Elkin Cruz (RPI) - Added network surrogate for 1-D Dragonfly model (dragonfly-dally). - Added application surrogate for MPI replay (model-net-mpi-replay). - Implemented API to allow network and application surrogates to switch as simulation runs (aka, hybrid simulation). - Added network and application level directors, which coordinate data transference between model and predictor. - Added simple averaged-based network and application predictors (they are given simulation data and are in charge of predicting future states of the simulation, skipping computation). - Implemented necessary scaffolding to check for bugs in reversible computation (to be used with SEQUENTIAL_ROLLBACK_CHECK option in ROSS). - Fixed reversible computation bugs on 1-D Dragonfly network.