On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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6 results for “fused-kernel”

QPyTorch: A Low-Precision Arithmetic Simulation Frameworkcs.LG

QPyTorch simplifies low-precision training simulations.

problem Empirical evaluation of low-precision training algorithms.

method Native PyTorch framework with efficient fused-kernel approach.

result Supports various low-precision configurations and rounding options.

low-precision training simulation PyTorch quantization fused-kernel

2019-10-09abs ↗pdf ↗

Scaling DoRA: High-Rank Adaptation via Factored Norms and Fused Kernelscs.LG

DoRA improves adaptation efficiency for large models by factoring norms and fusing kernels.

problem High-rank DoRA is computationally expensive and infeasible on common GPUs.

method Factored norms and fused Triton kernels to reduce memory and speed up computation.

result Fused implementation is up to 2.0x faster for inference and 1.9x faster for gradient computation.

DoRA weight-decomposition factored norms fused kernels vision-language models

2026-03-23abs ↗pdf ↗

Fast-Vollib: A Fast Implied Volatility Library for Pythonwith PyTorch, JAX, and CUDA Fused-Kernel Backendsq-fin.CP

Fast-vollib offers high-performance option pricing and IV computation.

problem Efficiently pricing and computing implied volatility for financial models.

method Open-source Python library with PyTorch, JAX, and CUDA backends, implementing Halley and LBR algorithms.

result High-performance option pricing and IV computation with vectorized implementations.

Python option pricing implied volatility Black-Scholes Halley method

2026-04-29abs ↗pdf ↗

A Deep Learning Approach To Multiple Kernel Fusionstat.ML

Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (MKL) attempt to learn the parameters for combining the kernels through sophisticated optimization procedures. In this paper, we propose an a…

2016-12-28abs ↗pdf ↗

cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transportcs.MS

cuRegOT accelerates GPU-based entropic OT solving.

problem Slow convergence and high computational cost of optimal transport on GPUs.

method High-performance GPU solver with algorithmic and architectural optimizations.

result Significant speedups over state-of-the-art solvers.

GPU Optimal Transport cuRegOT Sinkhorn Sparse-Plus-Low-Rank

2026-05-09abs ↗pdf ↗

Chronicals: A High-Performance Framework for LLM Fine-Tuning with 3.51x Speedup over Unslothcs.LG

Chronicals boosts LLM fine-tuning speed by 3.51x over Unsloth.

problem Memory bottleneck in fine-tuning large language models.

method Four optimizations: fused kernels, cross-entropy reduction, LoRA+, and sequence packing.

result 3.51x speedup on A100-40GB with Qwen2.5-0.5B.

large language models fine-tuning memory optimization fused kernels cross-entropy

2026-01-06abs ↗pdf ↗