Tensors have found utility in a wide range of applications, such as chemometrics, network traffic analysis, neuroscience, and signal processing. Many of these data science applications have increasingly large amounts of data to process and require high-performance methods to provide a reasonable turnaround time for analysts. Sparse tensor decomposition is a tool that allows analysts to explore a compact representation (low-rank models) of high-dimensional data sets, expose patterns that may not be apparent in the raw data, and extract useful information from the large amount of initial data. In this work, we consider decomposition of sparse count data using CANDECOMP-PARAFAC Alternating Poisson Regression (CP-APR).
Unlike the Alternating Least Square (ALS) version, CP-APR algorithm involves non-trivial constraint optimization of nonlinear and nonconvex function, which contributes to the slow adaptation to high performance computing (HPC) systems. The recent studies by Kolda et al. suggest multiple variants of CP-APR algorithms amenable to data and task parallelism together, but their parallel implementation involves several challenges due to the continuing trend toward a wide variety HPC system architecture and its programming models.
To this end, we have implemented a production-quality sparse tensor decomposition code, named SparTen, in C++ using Kokkos as a hardware abstraction layer. By using Kokkos, we have been able to develop a single code base and achieve good performance on each architecture. Additionally, SparTen is templated on several data types that allow for the use of mixed precision to allow the user to tune performance and accuracy for specific applications. In this presentation, we will use SparTen as a case study to document the performance gains, performance/accuracy tradeoffs of mixed precision in this application, development effort, and discuss the level of performance portability achieved. Performance profiling results from each of these architectures will be shared to highlight difficulties of efficiently processing sparse, unstructured data. By combining these results with an analysis of each hardware architecture, we will discuss some insights for improved use of the available cache hierarchy, potential costs/benefits of analyzing the underlying sparsity pattern of the input data as a preprocessing step, critical aspects of these hardware architectures that allow for improved performance in sparse tensor applications, and where remaining performance may still have been left on the table due to having single algorithm implementations on diverging hardware architectures.

Deep neural network classifiers typically require large labeled datasets to obtain high predictive performance. Obtaining such datasets can be time and cost prohibitive especially for applications where careful expert labeling is required, for instance, in healthcare and medicine. In this talk, we describe two algorithms using GANs that can help reduce this labeling burden. First, we describe a semi-supervised learning algorithm that utilizes GANs to perform manifold regularization. Our method achieves state-of-the-art performance amongst GAN-based semi-supervised learning methods while being much easier to implement. Second, we describe the Adversarially Learned Anomaly Detection (ALAD) algorithm (based on bi-directional GANs) for unsupervised anomaly detection. ALAD uses reconstruction errors based on adversarially learned features to determine if a data sample is anomalous. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

The Deep Learning 2.0 program is a multi-year A*STAR AI program, focused on capturing the next wave of deep learning.
The program is focused on
(a) 10x open problems in deep learning algorithmic research: thrusts include learning with 10x fewer labeled samples, compressing networks by 100x, incorporating knowledge graphs into deep learning, online deep learning, and white-box deep learning.
(b) Next generation hardware for deep learning: we are looking beyond GPUs and TPUs, and reimagining the entire hardware stack for deep learning from algorithms all the way down to silicon.
(c) New emerging enterprise applications for deep learning: ranging from personalized medicine, finance, health-care, IoT and advanced semiconductor manufacturing.
(d) Deep learning on encrypted data: the challenges lying at the intersection of deep learning and homomorphic encryption in making this technology closer to adoption.

Biological molecules such as proteins and nucleic acids are responsible for all life activities in the cells, and dysfunction of these molecules can cause severe diseases. These are complex systems consisting of as many as millions of atoms and performing biological functions through dynamical interactions between molecules. Information on the structures of these biological molecules and their dynamics is essential to understand the mechanism of their functions, which can have a huge impact in medicinal applications, particularly in design of new drugs. The structures and dynamics of large biological complexes can be determined using a variety of experimental techniques, each provides information at different resolution. X-ray crystallography has been providing a large amount of structural information at detailed atomic levels. With recent progress in experimental techniques, Cryo Electron Microscopy (cryo-EM) may be used to obtain 3D structural models near atomic-resolution. In addition, raw data from cryo-EM may now comprise millions of two-dimensional (2D) images of single molecules, which may represent distinct conformations of the molecule. Therefore, dynamics information could also be extracted from the 2D data. X-ray free electron laser (XFEL) is another exciting new technology that could significantly extend our structural knowledge of biological systems. The first XFEL began operation in 2009 at the SLAC National Accelerator followed by SACLA at RIKEN in 2011. Strong laser light from XFEL enables the measurement of single molecular complexes, without necessity of crystallization. However, for biological systems, due to their low diffraction power, signal to noise ratio is extremely low and interpretation of the data remains challenging. Given progresses in experimental techniques such as Cryo-EM and XFEL, new computational methods are also now needed to process and interpret data (millions of 2D images) obtained from these single particle experiments. We will discuss the development of hybrid computational methods that combine molecular mechanics and image data processing algorithms to derive structural and dynamical information from cryo-EM and XFEL data.

HPC Usability Research Team is aiming to realize easy parallel programming so that industry engineers can make parallel simulations. It is important because emerging innovative businesses, such as failure prediction service of production facilities, rely on simulation technologies. This talk covers a new programming framework that enables engineers to write parallel simulation programs with less programming effort than they do with MPI and OpenMP. After introducing our new concept: implementation-agnostic data type (IADT), this talk mention how this concept realizes easy parallel programming. This talk also covers the modeling effort of the cooling facility of the K computer, which a joint project of Operations and Computer Technologies Division and HPC Usability Research Team.

One of the most wealth fields in condensed matter physics is a kind of strongly correlated quantum systems where many-body interactions dominate determining fundamental physical properties. These systems include Hubbard-like models and frustrated quantum spin models, which are relevant, for example, to high-Tc cuprate superconductors and quantum spin liquids. The widely accepted consensus is that there is no ultimate numerical method at the present to solve a reasonably wide range of strongly correlated quantum systems in spatial dimensions higher than one dimension. In this talk, I will first explain what strongly correlated materials are and why we do have to care, and then introduce some of our team research activities, mostly focusing on quantum Monte Carlo and density matrix renormalization group simulations.

最近の量子コンピューター技術の発展により、近い将来量子コンピューターを用いてどのような計算が可能になるかという研究が活発に行われるようになってきました。現在の量子コンピューターに共通する問題点は実質的なコヒーレンス時間が短い事、そして誤り訂正を行うことができないことです。このため、短時間で計算を終わらせるためのアルゴリズムの開発が非常に重要になってきます。その一つとして注目を集めているのが、量子、古典両方のコンピューターを用いたハイブリッド型のアルゴリズムです。 今回の講演では前半に量子計算に関する現状のレビューを行い、後半で断熱量子計算におけるハイブリッドアルゴリズム(VanQver: Variational and Adiabatically Navigated Quantum Eigensolver)の紹介を行います。特に量子化学への応用に関しての計算結果について説明する予定です。