arXiv AI June 10, 2026 By Mingyuan Chi, Shizheng Wen

torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch

arXiv:2601. 13994v3 Announce Type: replace-cross Abstract: Differentiable sparse linear algebra is foundational for scientific machine learning, yet PyTorch lacks a unified library for it: torch.

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efficiency

arXiv Machine Learning

Jul 21

FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers

arXiv:2607. 18020v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve PDEs by incorporating physical constraints into neural-network training, but large-scale problems are limited by automatic-differentiation memory overhead and inefficient execution of grid-based PDE operators.

By Peiyu Zang, Bosen Xie, Ruoxiang Xu, Yongqiang Cai

benchmarks

arXiv Machine Learning

Jul 24

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

arXiv:2607. 18020v2 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) solve PDEs by incorporating physical constraints into neural-network training, but large-scale problems are limited by automatic-differentiation memory overhead and inefficient execution of grid-based PDE operators.

By Peiyu Zang, Bosen Xie, Ruoxiang Xu, Yongqiang Cai

benchmarks

arXiv AI

Aug 24

HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

arXiv:2608.21157v1 Announce Type: cross Abstract: High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolve...

By Jinghao Wang, Qiqi Gu, Chenpeng Wu, Jianguo Yao, Haibing Guan, Xijun Li

llms

arXiv Machine Learning

Sep 10

GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra

arXiv:2609. 08136v1 Announce Type: new Abstract: This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA).

By Pratik Rathore, Zachary Frangella, Parth Nobel, Xuning Hu, Madeleine Udell

arXiv AI

Jul 13

Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices

arXiv:2607. 08786v1 Announce Type: cross Abstract: With the growing deployment of large language models (LLMs), LLM inference cost has become a key challenge.

By Tao Lu, Haoyu Wang, Zonghui Wang, Keshen Xiang, Jiaheng Zhang, Wenzhi Chen

llms efficiency

arXiv AI

Jul 29

At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference

arXiv:2607. 25504v1 Announce Type: cross Abstract: Fine-grained weight pruning and activation sparsification have emerged as effective approaches for reducing the compute and memory cost of inference for Transformer models.

By Bowen Wang, Chi Zhang, Diyou Shen, Renzo Andri, Navaneeth Kunhi Purayil, Luca Benini

llms efficiency

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FlashPDE: A Drop-in Fused Triton Operator Library for Neural PDE Solvers

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra

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At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference