Natural language processing is a fast-growing field, with a rapid evolution of approaches and models. In the last 6 years, we have come a long way from word embeddings to contextualized representations to pre-trained transformers, with numerous success stories for NLP system performance on question answering, text classification, machine translation and other tasks.
Despite the successes, we are still very far from reliable verbal reasoning, and one of the unresolved issues is semantic compositionality. It is not only a practical challenge, but also a theoretical one, as there is still no consensus on what a compositional representation of morphologically complex word, phrase or a sentence should be like.
This talk provides an introduction to our research endeavor in the field of NLP, briefly introduces basics of NLP models and aspects of linguistic theory in which they are explicitly or implicitly grounded, particularly compositionality.

Compared with all-atom force fields, early coarse-grained (CG) models were once considered as "toys" to study qualitative properties of biophsical processes such as protein folding. However, as people's interest increases in large-scale biological phenomenons, such as genome organization, liquid-liquid phase separation, and virus capsid assembly, CG models begin to show their advantage in lower computational cost. On the other hand, recent development of the CG models also gained great success in improving the accuracy. We first review the advances of the latest CG models for protein, DNA, and RNA. We then describe the currenet progress of the implementation of these models in the MD software GENESIS. At last, we show some simple applications of the CG simulations with GENESIS.

The quantum many-body systems give us a rich field which potentially yields unexpected phenomena. Especially, their dynamics have attracted much attention due to the recent progress of the experimental realizations. The striking examples are the quantum computations since both of the quantum annealing and the gate-type quantum devices can be regarded as the controlled dynamics of the quantum many-body systems. In this talk, I would like to discuss our recent studies on the dynamics for quantum many-body systems, including the photo-induced superconductivity as well as the simulations of the gate-type quantum computations.

This presentation focuses on a scalable Eulerian finite volume method for structure and fluid-structure interaction (FSI) problems that are difficult to simulate in industry. In conventional simulation methods used in industry, structure problems are computed with Lagrangian methods, while fluid problems are computed with Eulerian methods. However, this approach poses three computational problems.
Firstly, we have to spend lots of labor and time to generate a computational mesh and modify the low-quality mesh for complex geometries. Secondly, data mapping/transfer between different mesh/solvers are required in fluid-structure interaction problems. Thirdly, Lagrangian methods using unstructured mesh are challenging to obtain good scalability in the massively parallel environment.
Considering the background as mentioned above, in my work, an Eulerian finite volume method using a scalable hierarchical Cartesian mesh method has been developed. The present method has the following three advantages. The first one is good scalability in a massively parallel computing environment. Secondly, we can quickly generate the computational mesh of complex geometries, such as a car body. In my talk, I will demonstrate the structural simulation of a car body, which is spatially discretized by approximately 200 million cells and was computed using 104,520 cores on the K computer. Thirdly, the proposed Eulerian method can also compute fluid-structure interaction problems in a unified manner.
In future work, we plan to conduct car crash simulations using many patterns of multi-material vehicle structures to study ultralight vehicle structures, and fluid-structure interaction simulation of buildings under high-wind conditions for disaster prevention and mitigation.

Matrix multiplication requires computer systems have huge computing capability and data throughputs as problem size is increased. In this research, an OpenCL-based matrix multiplier with task parallelism is designed and implemented by using the FPGA board DE5a-NET to improve computation throughput and energy efficiency. The matrix multiplier is based on the systolic array architecture with 10 × 16 processing elements (PEs). When data are single-precision floating-point, the proposed matrix multiplier averagely achieves about 785 GFLOPs in computation throughput and 66.75 GFLOPs/W in energy efficiency. Compared with the Intel’s OpenCL example with data parallelism on FPGA, the SGEMM routines in the Intel MKL and OpenBLAS libraries executed on a desktop with 32 GB DDR4 RAMs and an Intel i7-6800K processor running at 3.4 GHz, the proposed matrix multiplier averagely outperforms by 3.2 times, 1.3 times, and 1.6 times in omputation throughput, and by 2.9 times, 10.5 times, and 11.8 times in energy efficiency, respectively, even though the fabrication technology is 20 nm in the FPGA while it is 14 nm in the CPU.

Biodegradable plastics are attracting attention to reduce environmental impact and achieve Sustainable Development Goals, SDGs. We, the computational molecular science research team, are developing theoretical tools to estimate biodegradability of plastics to offer design guideline, by applying materials informatics. I will talk about multi-scale simulators of biodegradation. Changes of molecular weight distributions under degradation are estimated by a macroscopic Monte Carlo simulation. Hydrolysis reaction pathways of polyesters are investigated by nudged elastic band, NEB, as implemented in NTChem, a quantum chemistry package developed by us, and activation energies and heats of reaction are obtained. We are developing models to predict these values from monomer structures.

Simple model is expected to make reliable simulations, but it is not always available for social phenomena and complicated adhoc models are often used. Traffic simulation is an example. Various models are proposed and used for traffic phenomena, In this talk, traffic phenomena and their models are briefly reviewed.

The continuous improvement in processing speed in high-performance computer systems has been enabled by transistor scaling known as Moore's law. However, this trend is predicted to end in the near future. It is vital to research and develop new, more efficient high performance architectures to continue realizing high performance computing systems. One of the ways to improve performance of computer systems in the post-Moore era is utilizing domain specific architectures. In this talk, we briefly introduce our recent research efforts on a domain specific architecture for graph processing. In this research, we focus on the edge-centric graph processing model and propose a dedicated cache architecture for exploiting data locality. We have evaluated our cache architecture by a light weight cache simulation and the results showed that it can reduce the number of LLC cache misses by up to 89.9%.

In recent years, in the automotive industry, weight reductions are indispensable for complying with carbon dioxide emission regulations. Although automotive companies have been mainly using steel sheets, they want to employ multi-material structures including extrusions, castings, or 3D printings of aluminum alloy or resin to achieve weight reductions. However, the structural design will be more complex because multi-material structures have a higher degree of geometric freedom than sheet metal structures. Therefore, numerical simulations need to play a more critical role in designing optimal vehicle structures.
For the last several decades, a Lagrangian finite element method (FEM) using mainly shell formulation has been the de facto standard in the automotive industry. However, shell formulation cannot numerically model the multi-material structures mentioned above because they do not have a constant thickness. Thus, the continuum formulation has to be applied, but this approach poses two computational problems.
The first problem is that an enormous number of finite elements using continuum formulation is required to discretize the multi-material structures spatially. A scalable method in a massively parallel environment is indispensable for this simulation. Secondly, we need to spend more than a month to generate the finite element mesh of a car body. Therefore, it is challenging to investigate many patterns of vehicle structures.
Considering the background as mentioned above, we focus on a Eulerian finite volume method (FVM) [1] based on continuum formulation [2] using a scalable hierarchical Cartesian mesh method [1]. This Eulerian FVM [1] has the following three advantages. The first one is good scalability [1] in a massively parallel computing environment. Secondly, we can easily generate the computational mesh of a car body only within 10 minutes. We will demonstrate the stiffness analysis of a body-in-white structure, which is spatially discretized by approximately 200 million cells and was computed using 104,520 cores on the K computer. Thirdly, the proposed Eulerian method is easy to couple a conventional finite volume fluid solver.
In future work, we plan to conduct car crash simulations using many patterns of multi-material vehicle structures to study ultralight vehicle structures.
[1] K. Nishiguchi 2019 https://doi.org/10.1002/nme.5954 [2] K. Nishiguchi 2018 https://doi.org/10.1002/nme.5790

In this talk, I will introduce some of my previous work including my Ph.D. thesis, new OS features and middleware to make use of emerging hardware technologies. Emerging hardware devices/features (e.g., FPGA/ASICs, non-volatile memories, Intel SGX) have been widely studied due to strong demands on performance improvement, energy efficiency, and data protection of computer systems. However, due to a lack of system software support, existing computer systems cannot fully utilize these new devices. I have proposed new operating system functions and middleware, which are useful to improve the performance/usability of the devices or analyze an application behavior with them.