This thesis focus on memory-aware algorithms tailored for hierarchical memory architectures, found for instance within multicore processors. We first study the matrix product on multicore architectures. We model such a processor, and derive lower bounds on the communication volume. We introduce three ad hoc algorithms, and experimentally assess their performance.We then target a more complex operation: the QR factorization of tall matrices. We revisit existing algorithms to better exploit the parallelism of multicore processors. We thus study the critical paths of many algorithms, prove some of them to be asymptotically optimal, and assess their performance.In the next study, we focus on scheduling streaming applications onto a heterogeneous multicore platform, the QS 22. We introduce a model of the platform and use steady-state scheduling techniques so as to maximize the throughput. We present a mixed integer programming approach that computes an optimal solution, and propose simpler heuristics. We then focus on minimizing the amount of required memory for tree-shaped workflows, and target a classical two-level memory system. I/O represent transfers from a memory to the other. We propose a new exact algorithm, and show that there exist trees where postorder traversals are arbitrarily bad. We then study the problem of minimizing the I/O volume for a given memory, show that it is NP-hard, and provide a set of heuristics.Finally, we compare archival policies for BLUE WATERS. We introduce two archival policies and adapt the well known RAIT strategy. We provide a model of the tape storage platform, and use it to assess the performance of the three policies through simulation.