The arrival of a new batch of freshmen is a sure sign of autumn and the start of a new academic semester. I am thrilled to announce that this semester should also be my last one as a Ph.D. candidate because I officially submitted my dissertation for preliminary examination last week. In this post, I will look back on summer 2018 and recap the research projects that I completed during that time.

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Matrix diagonalization is a fundamental linear algebra operation with a wide range of applications in scientific and other fields of computing. At the same time, it is also one of the most expensive operations with a formal computational complexity of $\mathcal{O}(N^3)$, which can become a significant performance bottleneck as the size of the system grows. In this post, I will introduce the canonical algorithm for diagonalizing matrices in parallel computing to set the scene for today’s main topic: improving diagonalization performance. With the help of benchmark calculations, I will then demonstrate how a clever mathematical library choice can easily reduce the time needed to diagonalize a matrix by at least 50 %.

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Nico Holmberg

PhD in Computational Chemistry,
AI and Tech Enthusiast

Machine Learning Data Scientist,
Top Data Science Ltd

Espoo, Finland