Automatic track mixing

By using state-of-the-art algorithms for beat tracking and structure analysis, our goal is to create a continuous flow of music with seamless transitions between tracks.

The objective is a fully automated mixing system with focus on electronic dance music.

Contact: Mickaël Zehren

Experimental Linear Algebra Performance Studies (ELAPS)

The ELAPS Framework is a multi-platform open source environment for fast yet powerful experimentation with dense linear algebra kernels, algorithms, and libraries. (more)

Get ELAPS now!

IEEE/ACM George Michael Memorial HPC Fellowship

1. Linda Gesenhues and Markus Höhnerbach Named Recipients of 2018 ACM-IEEE CS George Michael Memorial HPC Fellowships
   ACM Media Center

Intel® Parallel Computing Center at RWTH

The IPCC @ RWTH aims to optimize the most important computational kernels in the LAMMPS molecular dynamics package for Intel® architectures. (more)

Contact: Markus Höhnerbach, Rodrigo Canales

Linear Algebra

Non-Equilibirum Green's Function (NEGF)

A highly efficient and optimized implementation of quantum transport in mesoscopic systems was introduced for simulating novel nano- transistors and quantum photovoltaic devices. These simulations are based on software developed within the Non-Equilibrium Green's Functions (NEGF) framework, which is an advanced approach that allows for treatment of out-of-equilibrium transport phenomena.

Contact: Sebastian Achilles

Supercomputing 2017

1. A01: GEMM-Like Tensor-Tensor Contraction (GETT)
   Paul Springer and Paolo Bientinesi
2. A04: Optimization of the AIREBO Many-Body Potential for KNL
   Markus Höhnerbach and Paolo Bientinesi

Supercomputing 2018

1. PotC: Many-Body Potential Implementations à La Carte
   Markus Höhnerbach and Paolo Bientinesi

Algorithm Generation

1. On the Performance Prediction of BLAS-based Tensor Contractions
   Elmar Peise, Diego Fabregat-Traver and Paolo Bientinesi
   High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, Lecture Notes in Computer Science, Volume 8966, pp. 193-212, Springer International Publishing, April 2015.

   @inproceedings{Peise2015:380,
       author    = "Elmar Peise and Diego Fabregat-Traver and Paolo Bientinesi",
       title     = "On the Performance Prediction of BLAS-based Tensor Contractions",
       booktitle = "High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation",
       year      = 2015,
       editor    = "Jarvis, Stephen A. and Wright, Steven A. and Hammond, Simon D.",
       volume    = 8966,
       series    = "Lecture Notes in Computer Science",
       pages     = "193-212",
       month     = apr,
       publisher = "Springer International Publishing",
       url       = "http://arxiv.org/pdf/1409.8608v1"
   }

   Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is known to be a challenging task. While for operations on one- and two-dimensional tensors there exist standardized interfaces and highly-optimized libraries (BLAS), for higher dimensional tensors neither standards nor highly-tuned implementations exist yet. In this paper, we consider contractions between two tensors of arbitrary dimensionality and take on the challenge of generating high-performance implementations by resorting to sequences of BLAS kernels. The approach consists in breaking the contraction down into operations that only involve matrices or vectors. Since in general there are many alternative ways of decomposing a contraction, we are able to methodically derive a large family of algorithms. The main contribution of this paper is a systematic methodology to accurately identify the fastest algorithms in the bunch, without executing them. The goal is instead accomplished with the help of a set of cache-aware micro-benchmarks for the underlying BLAS kernels. The predictions we construct from such benchmarks allow us to reliably single out the best-performing algorithms in a tiny fraction of the time taken by the direct execution of the algorithms.

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Many-Body Potential Optimization

Optimizing core many-body potentials for molecular dynamics simulations in materials science.