Data locality for sparse matrices via advanced optimisations in large-scale scientific programs

通过大规模科学项目中的高级优化实现稀疏矩阵的数据局部性

• 批准号: 22K17900
• 负责人: Vatai Emil
• 金额: $ 2.91万
• 依托单位: Institute of Physical and Chemical Research
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K17900/
• 关键词: sparsity; data locality; spin chemistry; machine learning; NLP; linear solvers; numerical methods; sparse matrix; iterative methods; graph algorithms

The s-step CG topic progressed the least from the 3 topics and this direction remains mostly unexplored.: with an intern we merely made minor progress in implementing s-step CG and combining it with DMPK.We've started working on sparsity and explainability in NLP recently with another student. We have looked different approaches to sparsifycation, such as the STEN library, matrix decompositions, graph neural networks (GNNs) including graph convolutional networks (GCNs). We have gained a comprehensive understanding of the challenges of sparsity in ML training, such as sparsity crawling back quickly into our the weights (because the sum and product of matrices becomes dense easily) as well as the problem of dense gradients.Finally, the most promising direction is a collaboration with spin-chemists and the topic of radical pair simulations. We have developed a python package/library for simulating radical-pair called RadicalPy. The package includes various components which can be used to construct simulations of various experiments in the field, such as construction Hamiltonians modelling different forces in the reactions, kinetics and relaxation mechanisms, experiment schemes, data conversion and plotting tools and molecule database which enables easy and convenient loading of molecules and isotopes.

s步CG主题在3个主题中进展最少，这个方向仍然是未探索的。在一个实习生的帮助下，我们在实现s-step CG并将其与DMPK结合方面只取得了很小的进展。我们最近开始与另一个学生一起研究NLP中的稀疏性和可解释性。我们已经研究了不同的稀疏化方法，例如STEN库，矩阵分解，图神经网络（GNN），包括图卷积网络（GCN）。我们已经对稀疏性在ML训练中的挑战有了全面的了解，例如稀疏性快速爬回我们的权重（因为矩阵的和与积很容易变得稠密）以及稠密梯度的问题。最后，最有前途的方向是与自旋化学家的合作以及自由基对模拟的主题。我们已经开发了一个python包/库，用于模拟称为RadicalPy的根对。该软件包包括各种组件，可用于构建现场各种实验的模拟，例如构建反应中不同力的哈密顿量，动力学和弛豫机制，实验方案，数据转换和绘图工具以及分子数据库，可以轻松方便地加载分子和同位素。

项目成果

RadicalPy [source code]

RadicalPy [源代码]

RadicalPy [documentation]

RadicalPy [文档]

RadicalPy: radical pair spin dynamics for the masses

RadicalPy：大众的自由基对自旋动力学

• 发表时间: 2022
• 作者: Antill Lewis ;Vatai Emil
• 通讯作者: Vatai Emil

RadicalPy [pypi package]

RadicalPy [pypi 包]