Modern Approaches to Exact Diagonalization and Selected Configuration Interaction with the Adaptive Sampling CI Method.

Recent advances in selected configuration interaction methods have made them competitive with the most accurate techniques available and, hence, creating an increasingly powerful tool for solving quantum Hamiltonians. In this work, we build on recent advances from the adaptive sampling configuration interaction (ASCI) algorithm. We show that a useful paradigm for generating efficient selected CI/exact diagonalization algorithms is driven by fast sorting algorithms, much in the same way iterative diagonalization is based on the paradigm of matrix vector multiplication. We present several new algorithms for all parts of performing a selected CI, which includes new ASCI search, dynamic bit masking, fast orbital rotations, fast diagonal matrix elements, and residue arrays. The ASCI search algorithm can be used in several different modes, which includes an integral driven search and a coefficient driven search. The algorithms presented here are fast and scalable, and we find that because they are built on fast sorting algorithms they are more efficient than all other approaches we considered. After introducing these techniques, we present ASCI results applied to a large range of systems and basis sets to demonstrate the types of simulations that can be practically treated at the full-CI level with modern methods and hardware, presenting double- and triple-ζ benchmark data for the G1 data set. The largest of these calculations is Si2H6 which is a simulation of 34 electrons in 152 orbitals. We also present some preliminary results for fast deterministic perturbation theory simulations that use hash functions to maintain high efficiency for treating large basis sets

Investigation of the quasifission process by theoretical analysis of experimental data of fissionlike reaction products

The fusion excitation function is the important quantity in planning experiments for the synthesis of superheavy elements. Its values seem to be determined by the experimental study of the hindrance to complete fusion by the observation of mass, angular and energy distributions of the fissionlike fragments. There is ambiguity in establishment of the reaction mechanism leading to the observed binary fissionlike fragments. The fissionlike fragments can be produced in the quasifission, fast fission, and fusion-fission processes which have overlapping in the mass (angular, kinetic energy) distributions of fragments. The branching ratio between quasifission and complete fusion strongly depends on the characteristics of the entrance channel. In this paper we consider a wide set of reactions (with different mass asymmetry and mass symmetry parameters) with the aim to explain the role played by many quantities on the reaction mechanisms. We also present the results of study of the $^{48}$Ca+$^{249}$Bk reaction used to synthesize superheavy nuclei with Z = 117 by the determination of the evaporation residue cross sections and the effective fission barriers $$ of excited nuclei formed along the de-excitation cascade of the compound nucleus.Comment: 21 pages, 15 figures, 2 table

AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained. To overcome this limitation, new gradient compression techniques are needed that are computationally friendly, applicable to a wide variety of layers seen in Deep Neural Networks and adaptable to variations in network architectures as well as their hyper-parameters. In this paper we introduce a novel technique - the Adaptive Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized selection of gradient residues and automatically tunes the compression rate depending on local activity. We show excellent results on a wide spectrum of state of the art Deep Learning models in multiple domains (vision, speech, language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers (SGD with momentum, Adam) and network parameters (number of learners, minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate end-to-end compression rates of ~200X for fully-connected and recurrent layers, and ~40X for convolutional layers, without any noticeable degradation in model accuracies.Comment: IBM Research AI, 9 pages, 7 figures, AAAI18 accepte

Subthreshold antiproton production in proton-carbon reactions

Data from KEK on subthreshold antiproton as well as on pi(+-) and K(+-) production in proton-nucleus reactions are described at projectile energies between 3.5 and 12.0 GeV. We use a model which considers a hadron-nucleus reaction as an incoherent sum over collisions of the projectile with a varying number of target nucleons. It samples complete events and allows thus for the simultaneous consideration of all particle species measured. The overall reproduction of the data is quite satisfactory. It is shown that the contributions from the interaction of the projectile with groups of several target nucleons are decisive for the description of subthreshold production. Since the collective features of subthreshold production become especially significant far below the threshold, the results are extrapolated down to COSY energies. It is concluded that an antiproton measurement at ANKE-COSY should be feasible, if the high background of other particles can be efficiently suppressed.Comment: 15 pages, 5 figures, gzipped tar file, submitted to J. Phys. G v2: Modification of text due to demands of referee