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

s-step CG主题在3个主题中进展最少，并且该方向仍未被探索。在一个实习生的帮助下，我们在执行s-step CG并将其与DMPK结合方面只取得了很小的进展。我们最近开始和另一个学生一起研究NLP中的稀疏性和可解释性。我们研究了不同的稀疏化方法，如STEN库、矩阵分解、包括图卷积网络（GCNs）在内的图神经网络（gnn）。我们已经对ML训练中稀疏性的挑战有了全面的了解，比如稀疏性会快速爬回我们的权重（因为矩阵的和和积很容易变得密集）以及密集梯度的问题。最后，最有希望的方向是与自旋化学家和自由基对模拟的主题合作。我们已经开发了一个python包/库来模拟激进对，称为RadicalPy。该软件包包括各种组件，可用于构建各种实验领域的模拟，例如构建哈密顿量，模拟反应中的不同力，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，使分子和同位素轻松方便地加载。

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.