Exascale computing platforms with millions of compute units and with thousands of nodes are predicted to experience frequent faults which interrupt applications' execution. In this context resilience against faults becomes important. We examine user and software level fault mitigation strategies in a distributed LOBPCG algorithm targeting nuclear CI calculations. In particular, we present and evaluate one strategy that keeps the total number of fault-Tolerant LOBPCG iterations close to that of the standard LOBPCG algorithm ran on a fault-free machine.

To accelerate the solution of large eigenvalue problems arising from many-body calculations in nuclear physics on distributed-memory parallel systems equipped with general-purpose Graphic Processing Units (GPUs), we modified a previously developed hybrid MPI/OpenMP implementation of an eigensolver written in Fortran 90 by using an OpenACC directives based programming model. Such an approach requires making minimal changes to the original code and enables a smooth migration of large-scale nuclear structure simulations from a distributed-memory many-core CPU system to a distributed GPU system. However, in order to make the OpenACC based eigensolver run efficiently on GPUs, we need to take into account the architectural differences between a many-core CPU and a GPU device. Consequently, the optimal way to insert OpenACC directives may be different from the original way of inserting OpenMP directives. We point out these differences in the implementation of sparse matrix–vector multiplications with multiple vectors, which constitutes the main cost of the eigensolver, as well as other differences in the preconditioning step and dense linear algebra operations. We compare the performance of the OpenACC based implementation executed on multiple GPUs with the performance on distributed-memory many-core CPUs, and demonstrate significant speedup achieved on GPUs compared to the on-node performance of a many-core CPU. We also show that the overall performance improvement of the eigensolver on multiple GPUs is more modest due to the communication overhead among different MPI ranks.

Nuclear structure and reaction theory is undergoing a major renaissance with advances in many-body methods, strong interactions with greatly improved links to Quantum Chromodynamics (QCD), the advent of high performance computing, and improved computational algorithms. Predictive power, with well-quantified uncertainty, is emerging from non-perturbative approaches along with the potential for guiding experiments to new discoveries. We present an overview of some of our recent developments and discuss challenges that lie ahead. Our foci include: (1) strong interactions derived from chiral effective field theory; (2) advances in solving the large sparse matrix eigenvalue problem on leadership-class supercomputers; (3) selected observables in light nuclei with the JISP16 interaction; (4) effective electroweak operators consistent with the Hamiltonian; and, (5) discussion of A=48 system as an opportunity for the no-core approach with the reintroduction of the core.