Signi cant improvements were brought to the second version of P . Some were single-core optimizations, and others focused on the algorithm adaptation to largescale parallelism (load balancing in particular). Currently, the code has a parallel e ciency that enables routinely to realize runs on roughly 2 000 CPU cores, with tens of millions of determinants in the reference space. Moreover, we have been able to push up to 12 288 cores (256 nodes) on GENCI's supercomputer Irene. Such a gain in e ciency has and will lead to many more challenging chemical applications. e Davidson diagonalization, which is at the center of sCI and FCI methods, su ers from the impossibility to fully store the Hamiltonian in the memory of a single node. e solution we adopted was to resort to direct methods, i.e., recomputing on the y the matrix elements at each iteration. While an extremely fast method was already available to detect zero matrix elements, 152 the former implementation still had to search over the O(N 2 det ) matrix elements for interacting de- terminant pairs. Now, determinants are split in disjoint sets entirely disconnected from each other. us, only a small fraction of the matrix elements need to be explored, and an algorithm with O(N 3/2 det ) scaling was proposed. While the parallelization of this method was somewhat challenging due to the extremely unbalanced nature of the elementary tasks, a distributed implementation was realized with satisfying parallel speedups (typically 35 for 50 nodes) with respect to the 48-core single-node reference.