arXiv subjects

Kengo Nakajima

Publications and source records attributed to Kengo Nakajima.

5 recordsLinked to original sources

arXiv2026

A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations

This work aims to predict blood flow with non-Newtonian viscosity in stenosed arteries using convolutional neural network (CNN) surrogate models. An alternating Schwarz domain decomposition method is proposed which uses CNN-based subdomain solvers. A universal subdomain solver (USDS) is trained on a single, fixed geometry and then applied for each subdomain solve in the Schwarz method. Results for two-dimensional stenotic arteries of varying shape and length for different inflow conditions are presented and statistically evaluated. One key finding, when using a limited amount of training data, is that incorporating a physics-aware constraint, as, in our case, flow rate conservation, into the USDS improves the prediction accuracy and convergence behavior of the Schwarz method compared to a purely data-driven USDS. As the USDS is a data-driven, inexact subdomain solver, admissible parameter ranges for the geometry and inflow configurations must be defined and tested.

math.NA↗

arXiv2025

Observability Architecture for Quantum-Centric Supercomputing Workflows

Quantum-centric supercomputing (QCSC) workflows often involve hybrid classical-quantum algorithms that are inherently probabilistic and executed on remote quantum hardware, making them difficult to interpret and limiting the ability to monitor runtime performance and behavior. The high cost of quantum circuit execution and large-scale high-performance computing (HPC) infrastructure further restricts the number of feasible trials, making comprehensive evaluation of execution results essential for iterative development. We propose an observability architecture tailored for QCSC workflows that decouples telemetry collection from workload execution, enabling persistent monitoring across system and algorithmic layers and retaining detailed execution data for reproducible and retrospective analysis, eliminating redundant runs. Applied to a representative workflow involving sample-based quantum diagonalization, our system reveals solver behavior across multiple iterations. This approach enhances transparency and reproducibility in QCSC environments, supporting infrastructure-aware algorithm design and systematic experimentation.

quant-ph↗

arXiv2024

Xabclib:A Fully Auto-tuned Sparse Iterative Solver

In this paper, we propose a general application programming interface named OpenATLib for auto-tuning (AT). OpenATLib is designed to establish the reusability of AT functions. By using OpenATLib, we develop a fully auto-tuned sparse iterative solver named Xabclib. Xabclib has several novel run-time AT functions. First, the following new implementations of sparse matrix-vector multiplication (SpMV) for thread processing are implemented:(1) non-zero elements; (2) omission of zero-elements computation for vector reduction; (3) branchless segmented scan (BSS). According to the performance evaluation and the comparison with conventional implementations, the following results are obtained: (1) 14x speedup for non-zero elements and zero-elements computation omission for symmetric SpMV; (2) 4.62x speedup by using BSS. We also develop a "numerical computation policy" that can optimize memory space and computational accuracy. Using the policy, we obtain the following: (1) an averaged 1/45 memory space reduction; (2) avoidance of the "fault convergence" situation, which is a problem of conventional solvers.

cs.MS↗

arXiv2018

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained.

physics.comp-ph↗

arXiv2017

Implicit Low-Order Unstructured Finite-Element Multiple Simulation Enhanced by Dense Computation using OpenACC

In this paper, we develop a low-order three-dimensional finite-element solver for fast multiple-case crust deformation analysis on GPU-based systems. Based on a high-performance solver designed for massively parallel CPU based systems, we modify the algorithm to reduce random data access, and then insert OpenACC directives. The developed solver on ten Reedbush-H nodes (20 P100 GPUs) attained speedup of 14.2 times from 20 K computer nodes, which is high considering the peak memory bandwidth ratio of 11.4 between the two systems. On the newest Volta generation V100 GPUs, the solver attained a further 2.45 times speedup from P100 GPUs. As a demonstrative example, we computed 368 cases of crustal deformation analyses of northeast Japan with 400 million degrees of freedom. The total procedure of algorithm modification and porting implementation took only two weeks; we can see that high performance improvement was achieved with low development cost. With the developed solver, we can expect improvement in reliability of crust-deformation analyses by many-case analyses on a wide range of GPU-based systems.

cs.DC↗