Cédric Prigent. With Gabriel Antoniu (Advisor) and Alexandru Costan. Ph.D. Thesis, INRIA Rennes, 2021-2024
Distributed Machine Learning, Multi-GPU and Heterogeneous Resources.
Cédric Prigent. Master Thesis, Université de Bretagne Occidentale / DGA MI, 2021
Erwan Lenormand. With Henri-Pierre Charles (Advisor). Ph.D. Thesis, Université Paris-Saclay / CEA, 2018
Future computers in high-performance and embedded systems lead to complex memory hierarchies. Hundreds of computing nodes will have to be connected to tera-bytes of memories. In such systems, both the processing units (CPU, GPU, DSP, FPGA) and the memories (DRAM, NVRAM,FLASH) can be heterogeneous. Several architectures exist (distributed memory, shared memory, NUMA) with different hardware implementations (cache coherence, communication protocols), software implementations (thread parallelism, OpenMP, transactions) and communication technologies between processing units and memory (MPI, RDMA, RoCE, CCIX, GenZ). None of the approaches above offer a simple, unified programming model and memory model for parallel applications. The purpose of this Ph.D. Thesis is to study the possibility of using emerging technologies related to computing units, hybrid memories (persistent or not) and remote communication standards in order to accelerate data sharing onto heterogeneous platforms and provide a convenient programming model.
Application Sizing and Task Placement Exploration for Heterogeneous Architectures.
Rihab Bennour. With Kods Trabelsi. Master Thesis, Université de Paris-Sud / CEA, 2018
Attribute-Based Encryption applied to Distributed Shared Memory.
Louis Syoen. With Oana Stan. Master Thesis, Université Paris Diderot (Paris 7) / CEA, 2017
Modèles et protocoles de cohérence de données, décision et optimisation à la compilation pour des architectures massivement parallèles.
Safae Dahmani. With Guy Gogniat (Advisor) Ph.D. Thesis, Université de Bretagne Sud / CEA, 2012-2015.
Numerous works explored consistency mechanisms designed for highly parallel architectures. They lead to the conclusion that there won’t exist one protocol that fits to all applications and hardware contexts. In order to deal with consistency issues for this kind of architectures, we propose in this work a multi-protocol compilation toolchain, in which shared data of the application can be managed by different protocols. Protocols are chosen and configured at compile time, following the application behavior and the targeted architecture specifications. The protocols configuration relies on a genetic-based engine that allows to instantiate each protocol with appropriate parameters values according to multiple performance objectives. We also propose a cooperative cache consistency protocol improving the cache miss rate by sliding data to less stressed neighbors.
Consistency Protocol Decision Based on Shared Data Access Analysis for Massively Parallel Architectures.
Tien Thanh Nguyen. With Safae Dahmani. Master Thesis, Université de Paris-Sud / CEA, 2015
Shared Data Access Analysis Applied to Place and Route.
Hayfa Alaya Ben Salem. With Selma Azaiez. Master Thesis, Ecole Nationale des Sciences de l'Informatique, Tunisia / CEA, 2015
Network Contention-aware Method to Evaluate Data Consistency Protocols within a Compilation Toolchain.
Cédric Maignan. With Safae Dahmani, Guy Gogniat and Martha Johanna Sepulveda. Master Thesis, LabSTICC, Université de Bretagne-Sud, 2015
Hamza Chaker. With Safae Dahmani, Guy Gogniat and Martha Johanna Sepulveda. Master Thesis, LabSTICC, Université de Bretagne-Sud, 2014
Safae Dahmani. Master Thesis, Ecole normale supérieure de Cachan / CEA, 2012