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-Step CG主题从3个主题中进展最少，这个方向仍然没有探索。我们看了不同的稀疏方法，例如Sten库，矩阵分解，图形神经网络（GNNS），包括图形卷积网络（GCNS）。我们已经对ML训练中的稀疏性挑战有了深刻的了解，例如稀疏性迅速爬回了我们的体重（因为矩阵的总和和产物很容易变得很密集）以及密集梯度的问题。最有前途的方向是与Spin-Chemists的合作，以及与Spin-Chemists的协作和Radical Pair Pair Simulation的主题。我们已经开发了一个Python软件包/库，用于模拟激进对生产对。该软件包包括各种组件，可用于构建现场各种实验的模拟，例如在反应，动力学和放松机制中建模不同力的施工哈密顿量，实验方案，数据转换和绘制工具以及分子数据库，这些工具以及分子数据库可以轻松且方便的分子和方便的分子和同位素。

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]