Hi there! My name is Adrian Kummerländer. I am a software developer by vocation and a mathematician by degree, currently pursuing a doctorate at LBRG in the intersection between HPC and LBM. On these pages you will find articles covering some of my experiences in software development and related tinkerings as well as repositories and information on some of my personal projects. If you have any comments or questions feel free to reach out. I hope you will find something here worth your time.

» Reproducible development environment for Teensy

October 11, 2021 | Adrian Kummerländer

So for a change of scenery I recently started to mess around with microcontrollers again. Since the last time that I had any real contact with this area was probably around a decade ago — programming an ASURO robot — I started basically from scratch. Driven by the goal of building and programming a fancy mechanical keyboard (as it seems to be the trendy thing to do) I chose the Arduino-compatible Teensy 4.0 board. While I appreciate the rich and accessible software ecosystem for this platform, I don't really want to use some special IDE, applying amongst other things1 weird non-standard preprocessing to my code. In this vein it would also be nice to use my accustomed Nix-based toolchain which leads me to this article.

» Noise and Ray Marching

September 26, 2021 | Adrian Kummerländer

LiterateLB's volumetric visualization functionality relies on a simple ray marching implementation to sample both the 3D textures produced by the simulation side of things and the signed distance functions that describe the obstacle geometry. While this produces surprisingly nice looking results in many cases, some artifacts of the visualization algorithm are visible depending on the viewport and sample values. Extending the ray marching code to utilize a noise function is one possibility of mitigating such issues that I want to explore in this article.

» Working with tuples using swallowing and generic lambdas

May 26, 2020 | Adrian Kummerländer

Suppose you have some kind of list of types. Such a list can by itself be used to perform any compile time computation one might come up with. So let us suppose that you additionally want to construct a tuple from something that is based on this list. i.e. you want to connect the compile time only type list to a run time object. In such a case you might run into new question such as: How do I call constructors for each of my tuple values? How do I offer access to the tuple values using only the type as a reference? How do I call a function for each value in the tuple while preserving the connection to the compile time list? If such questions are of interest to you, this article might possibly also be.

» Submitted my first paper

August 4, 2021 at 15:30 | Adrian Kummerländer

Today I submitted the full paper of my talk at the 32nd ParCFD conference for publication.

There we consider the LBM algorithm’s propagation step as a transformation of the space filling curve used as the memory bijection. Specifically, a neighborhood distance invariance property is utilized to derive the existing Shift-Swap-Streaming (SSS) scheme as well as a new Periodic Shift (PS) pattern.

A special focus is placed on SIMD friendly implementation via virtual memory mapping on both CPU and GPU targets.

Both patterns are evaluated in detailed benchmarks. PS is found to provide consistent bandwidth-related performance while imposing minimal restrictions on the collision implementation.

The preprint is available on Researchgate as well as directly (PDF).

» Molecular Dynamics Simulation of the Benzene Dimer

March 31, 2021 at 20:05 | Adrian Kummerländer

One of the last lectures for my master’s degree was on numerical simulation in molecular dynamics. For the examination project I developed a GPU MD code capable of reproducing certain preferred configurations the benzene dimer.

» Just-in-time volumetric CFD visualization

July 24, 2020 at 14:15 | Adrian Kummerländer

For my seminar talk I wrote another LBM solver as a literate Org-document using CUDA and SymPy. The main focus was on just-in-time volumetric visualizations of the simulations performed by this code. While this is not ready to publish yet, check out the following impressions:

Flow around a rotating shaft

Further videos are available on my YouTube channel. e.g. a Taylor-Couette flow:

Taylor-Couette flow

» Visualizing the velocity distribution of a hard sphere gas

March 24, 2020 at 21:42 | Adrian Kummerländer

The velocity distribution of a system of colliding hard sphere particles quickly evolves into the Maxwell-Boltzmann distribution. One example of this surprisingly quick process can be seen in the following video:

Simulation of hard sphere gas

» Visualize the sky for arbitrary time and space coordinates on earth

January 11, 2020 at 21:12 | firmament | 071bc3 | Adrian Kummerländer

The primary color of the sky is caused by Rayleigh and Mie scattering of light in the atmosphere. While attending the lecture on Mathematical Modelling and Simulation at KIT I implemented a ray marcher to visualize this. Together with a model for calculating the sun direction for given coordinates and times this allows for generating interesting plots:

Sky in August

For more details check out Firmament.

» Started working on a framework for generating LBM kernels

October 27, 2019 at 22:35 | Adrian Kummerländer

During the past exam season I now and then continued to play around with my GPU LBM code symlbm_playground. While I mostly focused on generating various real-time visualizations using e.g. volumetric ray marching, the underlying principle of generating OpenCL kernels from a symbolic description has not lost its promise.

This is why I have now started to extract the generator part of this past project into a more general framework. Currently boltzgen targets C++ and OpenCL using a shared symbolic description while providing various configuration options:

λ ~/p/d/boltzgen (boltzgen-env) ● ./boltzgen.py --help
usage: boltzgen.py [-h] --lattice LATTICE --layout LAYOUT --precision
                   PRECISION --geometry GEOMETRY --tau TAU [--disable-cse]
                   [--functions FUNCTIONS [FUNCTIONS ...]]
                   [--extras EXTRAS [EXTRAS ...]]

Generate LBM kernels in various languages using a symbolic description.

positional arguments:
  language              Target language (currently either "cl" or "cpp")

optional arguments:
  -h, --help            show this help message and exit
  --lattice LATTICE     Lattice type (D2Q9, D3Q7, D3Q19, D3Q27)
  --layout LAYOUT       Memory layout ("AOS" or "SOA")
  --precision PRECISION
                        Floating precision ("single" or "double")
  --geometry GEOMETRY   Size of the block geometry ("x:y(:z)")
  --tau TAU             BGK relaxation time
  --disable-cse         Disable common subexpression elimination
  --functions FUNCTIONS [FUNCTIONS ...]
                        Function templates to be generated
  --extras EXTRAS [EXTRAS ...]
                        Additional generator parameters

The goal is to build upon this foundation to provide an easy way to generate efficient code using a high level description of various collision models and boundary conditions. This should allow for easy comparision between various streaming patterns and memory layouts.