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Sparse Iterative Solvers Using High-Precision Arithmetic with Quasi Multi-Word Algorithms
Daichi Mukunoki, Katsuhisa Ozaki
To obtain accurate results in numerical computation, high-precision arithmetic is a straightforward approach. However, most processors lack hardware support for floating-point form…
3Dify: a Framework for Procedural 3D-CG Generation Assisted by LLMs Using MCP and RAG
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +2
This paper proposes "3Dify," a procedural 3D computer graphics (3D-CG) generation framework utilizing Large Language Models (LLMs). The framework enables users to generate 3D-CG co…
VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs
Shun-ichiro Hayashi, Koki Morita, Daichi Mukunoki +2
In this study, we propose VibeCodeHPC, a multi-agent system based on large language models (LLMs) for the automatic tuning of high-performance computing (HPC) programs on supercomp…
DGEMM without FP64 Arithmetic - Using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme
Daichi Mukunoki
As the demand for AI computation rapidly increases, more hardware is being developed to efficiently perform the low-precision matrix multiplications required by such workloads. How…
Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
Tatsuro Hanyu, Takahiro Katagiri, Daichi Mukunoki +1
Coherent Ising Machines (CIMs) have recently gained attention as a promising computing model for solving combinatorial optimization problems. In particular, the Chaotic Amplitude C…
Performance Evaluation of General Purpose Large Language Models for Basic Linear Algebra Subprograms Code Generation
Daichi Mukunoki, Shun-ichiro Hayashi, Tetsuya Hoshino +1
Generative AI technology based on Large Language Models (LLM) has been developed and applied to assist or automatically generate program codes. In this paper, we evaluate the capab…