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.

Are you using Jupyter notebooks regularly in your machine learning or data science projects? Did you know that you can work on notebooks inside a free cloud-based environment with a GPU accelerator? In this post, I will introduce you to Google Colaboratory and show you in a few simple steps how to integrate this platform into your daily workflow.

This tutorial will teach you how to create a custom Google Maps based map for visualizing geographic statistical data. Such maps can be a useful tool when developing machine learning models. As a specific example case, we will create a map for visualizing the population density and median household income of postal code areas in Finland.

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 %.

The 2018 edition of the CP2K UK user meeting was held two Fridays ago at the University of Lincoln, UK. I will be recaping my experiences of the event in this post.

I will be giving an introductory how-to talk on constrained DFT at the 2018 CP2K UK User meeting at the University of Lincoln, UK, on Friday January 12 2018.

In my previous post, I discussed the benefits of using the message passing interface (MPI) to parse large data files in parallel over multiple processors. In today’s post, I will demonstrate how MPI I/O operations can be further accelerated by introducing the concept of hints. The second topic I will discuss is the emergence of solid-state drives in high-performance computing systems to resolve I/O bottlenecks. These topics will be illustrated by benchmark calculations using a parallel writer routine that I will implement to the task described previously.

Lately, parsing volumetric data from large (> 300 MB) text files has been a computational bottleneck in my simulations. Because I expect to be processing hundreds of these files, I decided to parallelize the parser routine by leveraging the message passing interface (MPI). I will describe my first experience with MPI I/O in this post by going through the synthesis process of the parallelized parser routine. I will also examine the performance of the parallel parser.

Compiling a CP2K binary – the massively parallel, open-source quantum chemistry and solid state physics program written in Fortran 2003 – can be a daunting task if you’ve never built a large project using maketools. This post aims to demystify the build process by showcasing a full CP2K build on a Cray XC40 supercomputer (codename Sisu) including dependencies.

Greetings and welcome! You have stumbled upon the personal website and blog of Nico Holmberg, a 26 year old Finn pursuing a doctoral degree in Computational Quantum Chemistry. Feel free to check out my profile for more information about me, my professional projects and other interests!