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

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。

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.