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NinaXander: Feasibility and Limits of Composing Frozen Language Models Across Architecture Families via a Shared Latent Space
Takanori Kotama, Shun-ichiro Hayashi, Daichi Mukunoki +2
In this paper we propose NinaXander, a series of composed language models obtained by connecting layers of frozen language models from different architecture families with a single…
AWE: Adaptive Weight Encoding for Exact Integer Matrix Products with Fewer GEMMs on FP4 Tensor Cores
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1
Emulation of high-accuracy floating-point matrix multiplication, as in the Ozaki scheme, splits the inputs into low-precision components and multiplies them pairwise. These product…
Validation-Centric AI-Assisted GPU Porting of a 250,000+ Line Legacy Weather Simulation Code
Tetsuya Hoshino, Masaya Kato, Kazuhisa Tsuboki +3
Recent advances in large language models have made CLI-based AI agents a practical tool for accelerating GPU porting of large legacy scientific applications. Such applications, how…
DGEMM with Ozaki Scheme I/II on FP4 Tensor Cores: A Base-13 E2M1 Limb Representation
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1
This paper proposes a method and its implementation for emulating FP64 matrix multiplication (DGEMM) by constructing, on FP4 (E2M1; 2 exponent bits and 1 mantissa bit) Tensor Cores…
ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1
The attention score with rotary position embeddings (RoPE) decomposes exactly into a sum over its 2D-rotation frequency pairs, and each pair's wavelength limits how far it can disc…
Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers
Naoya Mizuki, Takahiro Katagiri, Daichi Mukunoki +1
This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers. In an annealing-b…
LLM-Based Porting of Optimized C++ to CUDA Through Deoptimization and Reoptimization
Daichi Mukunoki, Ryo Mikasa, Shunichiro Hayashi +2
When porting high-performance computing (HPC) code from CPU to GPU, CPU-oriented optimizations may obstruct LLM-based CUDA translation. We design and evaluate a Deopt-Reopt workflo…
Layer-wise MoE Routing Locality under Shared-Prefix Code Generation: Token-Identity Decomposition and Compile-Equivalent Fork Redundancy
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1
In LLM-based code generation, multiple code candidates are often generated in parallel from the same prompt -- for example, in best-of-N sampling or multi-candidate code completion…
Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards
Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki +2
Large language models (LLMs) have demonstrated strong code generation capabilities, yet the runtime performance of generated code is not guaranteed, and there have been few attempt…
Learning-Augmented Performance Model for Tensor Product Factorization in High-Order FEM
Xuanzhengbo Ren, Yuta Kawai, Tetsuya Hoshino +4
Accurate performance prediction is essential for optimizing scientific applications on modern high-performance computing (HPC) architectures. Widely used performance models primari…