Project reference: 1915 A Radix Sort is an array sorting algorithm that can run in O(N) time, compared with typical sorting algorithms like QuickSort which run in O(Nlog(N)) time. Typical sorting algorithms sort elements using pairwise comparisons to determine ordering, …
Project reference: 1925 With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). ML is in a nutshell “the science …
Performance analysis of Distributed and Scalable Deep Learning Read More »
Project reference: 1924 Energy consumption is one of the largest problems faced by modern supercomputing in the race to build Exaflop/s capable systems. In the past decade hardware design focus has shifted from obtaining the best possible performance to improving …
Project reference: 1923 The project will consist of: Getting to know Hadoop and RHadoop; Defining big data source related to Industry 4.0; Creating and storing big data files (BD); Preparing BD for basic analysis; Defining predictive model and writing RHadoop …
Project reference: 1922 Gyrokinetic simulations are essential for better understanding of the plasma turbulence. For that purpose, a variety of non-linear gyrokinetic codes are being used and further developed, such as GENE, GYRO, ELMFIRE etc. These codes differ in numerics, …
Project reference: 1919 Simulations of classical or quantum field theories often rely on a lattice discretized version of the underlying theory. For example, simulations of Lattice Quantum Chromodynamics (QCD, the theory of quarks and gluons) are used to study properties …
Project reference: 1918 Today’s supercomputing hardware provides a tremendous amount of floating point operations (FLOPs). While CPUs are designed to minimize the latency of a stream of individual operations, GPUs try to maximize the throughput. However, GPU FLOPs can only …
Project reference: 1917 Deep neural networks (DNNs) are evaluated and used in many areas today, to replace or complement traditional technologies. In this project, the student will develop a DNN to detect and localize selected objects in images. The work …
Object Detection Using Deep Neural Networks – AI from HPC to the Edge Read More »
Project reference: 1916 The objective of this project is to present the scalability of CFD simulations on IT4Innovations parallel platforms and calculating external aerodynamics around the VSB-TUO formula student car (lift and drag coefficient calculation). Formula Student is a student …
Computational Fluid Dynamics Simulations of Formula Student Car Using HPC Read More »
Project reference: 1914 Deep learning is an algorithmic technique that has found rapid adoption and application in a wide range of domains. In the deep learning workflow, the inference is one of the final steps where the trained models are …
Project reference: 1913 DPD is a stochastic particle method for mesoscale simulations of complex fluids. It is based on a coarse approximation of the molecular structure of soft materials, with beads that can represent large agglomerates or unions of simpler …
Project reference: 1912 The focus of this project will be on enhancing further hybrid (e.g. stochastic/ deterministic) methods for Linear Algebra adding Deep Learning techniques. The focus is on Monte Carlo hybrid methods and algorithms for matrix inversion and solving …
Project reference: 1908 Python is widely used in scientific research for tasks such as data processing, analysis, and visualisation. However, it is not yet widely used for large-scale modelling and simulation on high-performance computers due to its poor performance – …
Performance of Python programs on new HPC architectures Read More »
Project reference: 1907 EPCC has developed a small, portable Raspberry-pi based cluster which is taken to schools, science festivals etc. to illustrate how parallel computers work. It is called “Wee ARCHIE” (in fact there are two versions in existence) because …
Project reference: 1906 Anomalies detection is one of the timeliest problems in managing HPC facilities. Clearly, this problem involves many technological issues, including big data analysis, machine learning, virtual machines manipulations and authentication protocols. Our research group already prepared a …
Anomaly detection of system failures on HPC machines using Machine Learning Techniques Read More »
Project reference: 1905 I/O is recognized to be the main bottleneck to achieve the Exascale computing, which is up to 1000x faster than current Petascale systems. The main cause can be identified in the disproportion of the rate of change between …
IN SItu/Web visualizatioN of CFD Data Using OpenFOAM Read More »
Project reference: 1904 In calculations of nanotubes prevail methods based on a one-dimensional translational symmetry using a huge unit cell. A pseudo two-dimensional approach, when the inherent helical symmetry of general chirality nanotubes is exploited, has been limited to simple …
Project reference: 1903 The goal of the project is to demonstrate that HPC tools are (at least) as good, or even better, for big data processing as the popular JVM-based technologies, such as the Hadoop MapReduce or Apache Spark. The …
Project reference: 1902 Supercomputers are a key tool for professionals from many disciplines to address society challenges, enabling them to perform, e. g., climate change simulations or genome analysis. European Commission’s high-performance computing (HPC) Strategy, implemented in the Horizon 2020 …
Project reference: 1901 Supercomputers are a key tool for professionals from many disciplines to address society challenges, enabling them to perform, e. g., climate change simulations or genome analysis. European Commission’s high-performance computing (HPC) Strategy, implemented in the Horizon 2020 …
Analysing effects of profiling in heterogeneous memory data placement Read More »
