Parallel and Distributed Algorithms

Computer Science and ICT, Data, AI

About this course

With the advent of multicore processors (and now many-core processors with several dozens of execution units), expressing parallelism is mandatory to enable high performance on different kinds of applications (scientific computing, big-data...). In this context, this course details multiple parallel programming paradigms to help exploiting such a large number of cores on different target architectures (regular CPUs and GPUs).It includes distributed-memory model (MPI), shared-memory model (OpenMP) and heterogeneous model (CUDA). All these approaches would allow leveraging the performance of differents computers (from small servers to large supercomputers listed in Top500).

Required level: INF431 or equivalent
Evaluation modalities: Project
Langue du cours : English
ECTS credits: 4

Expected learning outcomes