Performance analysis and acceleration of nuclear physics application on high-performance computing platforms using GPGPUs and topology-aware mapping techniques

The number of nodes on current generation of high performance computing (HPC) platforms increases with a steady rate, and nodes of these computing platforms support multiple/many core hardware designs. As the number of cores per node increase, either CPU or accelerator based, we need to make use of all those cores. Thus, one has to use the accelerators as much as possible inside scientific applications. Furthermore, with the increase of the number of nodes, the communication time between nodes is likely to increase, which necessitates application specific network topology-aware mapping techniques for efficient utilization of these platforms. In addition, one also needs to construct network models in order to study the benefits of specific network mapping. The specific topology-aware mapping techniques will help to distribute the computational tasks so that the communication patterns make optimal use of the underlying network hardware. This research will mainly focus on the Many Fermion Dynamics nuclear (MFDn) application developed at Iowa State University, a computational tool for low-energy nuclear physics, which utilizes the so-called Lanczos algorithm (LA), an algorithm for diagonalization of sparse matrices that is widely used in the scientific parallel computing domain. We present techniques applied to this application which enhance its performance with the utilization of general purpose graphics processing units (GPGPUs). Additionally, we compare the performance of the sparse matrix vector multiplication (SpMVM), the main computationally intensive kernel in the LA, with other efficient approaches presented in the literature. We compare results for the total HPC platforms\u27 resources needed for different SpMVM implementations, present and analyze the implementation of communication and computation overlapping method, and extend a model for the analysis of network topology presented in the literature. Finally, we present network topology-aware mapping techniques, focused at the LA stage, for IBM Blue Gene/Q (BG/Q) supercomputers, which enhance the performance as compared to the default mapping, and validate the results of our test using the network model

Ab Initio No Core Shell Model with Leadership-Class Supercomputers

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.Comment: 23 pages, 7 figures, Conference Proceedings online at http://ntse.khb.ru/files/uploads/2016/proceedings/Vary.pd

Ab Initio No Core Shell Model - Recent Results and Further Prospects

There has been significant recent progress in solving the long-standing problems of how nuclear shell structure and collective motion emerge from underlying microscopic inter-nucleon interactions. We review a selection of recent significant results within the ab initio No Core Shell Model (NCSM) closely tied to three major factors enabling this progress: (1) improved nuclear interactions that accurately describe the experimental two-nucleon and three-nucleon interaction data; (2) advances in algorithms to simulate the quantum many-body problem with strong interactions; and (3) continued rapid development of high-performance computers now capable of performing $20 \times 10^{15}$ floating point operations per second. We also comment on prospects for further developments.Comment: Invited paper presented at NTSE-2014 and published online in the proceedings (see footnote on p.1

Performance analysis and acceleration of nuclear physics application on high-performance computing platforms using GPGPUs and topology-aware mapping techniques

The number of nodes on current generation of high performance computing (HPC) platforms increases with a steady rate, and nodes of these computing platforms support multiple/many core hardware designs. As the number of cores per node increase, either CPU or accelerator based, we need to make use of all those cores. Thus, one has to use the accelerators as much as possible inside scientific applications. Furthermore, with the increase of the number of nodes, the communication time between nodes is likely to increase, which necessitates application specific network topology-aware mapping techniques for efficient utilization of these platforms. In addition, one also needs to construct network models in order to study the benefits of specific network mapping. The specific topology-aware mapping techniques will help to distribute the computational tasks so that the communication patterns make optimal use of the underlying network hardware. This research will mainly focus on the Many Fermion Dynamics nuclear (MFDn) application developed at Iowa State University, a computational tool for low-energy nuclear physics, which utilizes the so-called Lanczos algorithm (LA), an algorithm for diagonalization of sparse matrices that is widely used in the scientific parallel computing domain. We present techniques applied to this application which enhance its performance with the utilization of general purpose graphics processing units (GPGPUs). Additionally, we compare the performance of the sparse matrix vector multiplication (SpMVM), the main computationally intensive kernel in the LA, with other efficient approaches presented in the literature. We compare results for the total HPC platforms' resources needed for different SpMVM implementations, present and analyze the implementation of communication and computation overlapping method, and extend a model for the analysis of network topology presented in the literature. Finally, we present network topology-aware mapping techniques, focused at the LA stage, for IBM Blue Gene/Q (BG/Q) supercomputers, which enhance the performance as compared to the default mapping, and validate the results of our test using the network model.</p

Fault-Tolerant LOBPCG for Nuclear CI Calculations

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

Ab Initio No Core Shell Model - Recent Resultsand Further Prospects

There has been significant recent progress in solving the long-standing problems of how nuclear shell structure and collective motion emerge from underlying microscopic inter-nucleon interactions. We review a selection of recent significant results within the ab initio No Core Shell Model (NCSM) closely tied to three major factors enabling this progress: (1) improved nuclear interactions that accurately describe the experimental two-nucleon and three-nucleon interaction data; (2) advances in algorithms to simulate the quantum manybody problem with strong interactions; and (3) continued rapid development of high-performance computers now capable of performing 20×1015 floating point operations per second. We also comment on prospects for further developments