Michal Majchrowicz

Application of General-Purpose Computing on Graphics Processing Units for Acceleration of Basic Linear Algebra Operations and Principal Components Analysis Method

Nowadays speed of performed calculations can have significant impact not only on industrial application of developed solutions but also on conducted research, it is not uncommon for scientist to perform computations that may take hours or even days to complete. In this paper two different kinds of computations were analyzed: principal components analysis method and basic linear algebra operations. Authors present in this paper two different methods performing calculations using graphic cards. First one, used for PCA algorithm, uses single device, whereas second one, used in Landweber algorithm, uses multiple devices for conducting calculations.

Application of GPU Parallel Computing for Acceleration of Finite Element Method Based 3D Reconstruction Algorithms in Electrical Capacitance Tomography

Abstract With the increasing complexity and scale of industrial processes their visualization is becoming increasingly important. Especially popular are non-invasive methods, which do not interfere directly with the process. One of them is the 3D Electrical Capacitance Tomography. It possesses however a serious flaw - in order to obtain a fast and accurate visualization requires application of computationally intensive algorithms. Especially non-linear reconstruction using Finite Element Method is a multistage, complex numerical task, requiring many linear algebra transformations on very large data sets. Such process, using traditional CPUs can take, depending on the used meshes, up to several hours. Consequently it is necessary to develop new solutions utilizing GPGPU (General Purpose Computations on Graphics Processing Units) techniques to accelerate the reconstruction algorithm. With the developed hybrid parallel computing architecture, based on sparse matrices, it is possible to perform tomographic calculations much faster using GPU and CPU simultaneously, both with Nvidia CUDA and OpenCL.

Application of the Layered Model Management System in an Interactive Map of the University Campus

Abstract The paper presents a web application for navigation through the university campus, which is based on Global Positioning System GPS and OpenStreetMap. The application has a multilayer structure and multi-labelling support. The proposed solution ensures better management of visual data and more efficient image processing comparing to the other known methods. With the new search system, users can place a lot of information on one layer without losing the legibility of displayed data. All the information that was displayed on the map was grouped and assigned to the appropriate categories.Therefore a map contains a lot of related information that needs to be linked to each other. The system has been divided into modules that ensure the integrity of the displayed things. Presenting so much information at the same time is managed by modules. Their main job is to provide results that is then segregated and grouped. The system presented in this paper was applied for Lodz University of Technology.

Management of IOT Devices in a Smart Home Through the Application of an Interactive Mirror

Abstract Internet of Things (IoT) devices are big part of concept, which is called by electronic producers and many others Smart Home. Authors of this paper have decided to take a look at it and as a result of this analysis propose and implement (in a form of working prototype) a system that could manage different kinds of devices. The main objective of the project presented in this paper is a device that looks like a mirror and it is known to most people also is an interactive center, a place to obtain information of the devices that surround us and various parameters coming from sensors. Authors have prepared a prototype, that will be the central point of the apartment and will allow users to control all devices connected to this main system. Prototype allows to connect external sensors over GPIO (general purpose input/output) interface or Internet connection. There is also a possibility to add another communication interfaces such as 433MHz radio module, which is very cheap and works great in small areas or bluetooth module.

Acceleration of image reconstruction in 3D Electrical Capacitance Tomography in heterogeneous, multi-GPU system using sparse matrix computations and Finite Element Method

3D Electrical Capacitance Tomography provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra. Due to significant sizes of matrices used in ECT for image reconstruction and the fact that best image quality is achieved by using algorithms of which significant part is FEM and which are hard to parallelize or distribute. In order to solve these issues a new set of algorithms had to be developed.

Optimization of Distributed Multi-Node, Multi-GPU, Heterogeneous System for 3D Image Reconstruction in Electrical Capacitance Tomography

Abstract Electrical Capacitance Tomography is a non-invasive imaging technique, which allows visualization of the industrial processes interior and can be applied to many branches of the industry. Image reconstruction process, especially in case of 3D images, is a very time consuming task (when using classic processors and algorithms), which in turn leads to an unacceptable waiting time and currently limits the use of 3D Electrical Capacitance Tomography. Reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem must be able to transform capacitance data into images in a fraction of a second. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms, time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for further optimizations of previously developed algorithms.

Analysis of Application of Distributed Multi-Node, Multi-GPU Heterogeneous System for Acceleration of Image Reconstruction in Electrical Capacitance Tomography

Abstract 3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems.