Capabilities of information processing platforms have been rapidly increasing in the last four decades in the meaning of their speed and sizes. We are starting to reach the limits of possible capabilities of technologies on which current platforms are based. With their continuous minimization reliability of their behavior is questionable while quantum level will be reached. There are also many other problems that current technologies are facing, for example energy consumption, relatively high prices etc. These problems obstruct the evolution of computers from an era of desktop computing to an era of ubiquitous computing. Ever since humans have been using their “tools” as labor saving devices mainly top-down approach strategies have been employed, which means that tools are used in order to manipulate materials in manufacturing processes. Manufacturing processes employ big material and energy consumption and have a side effect of by-products which are unfit for use. Many great thinkers of our time point out that top-down approach is obsolete and that transition to synthetic approach (bottom-up approach) has to be established, which means that product will be built from basic entities using automatized procedures. Such development brings many new possible applications to information processing on the fields such as pharmacy, medicine, construction engineering, good production, etc. In order to solve the problems of current information processing technologies alternative processing platforms and methods are being intensively investigated and developed in the last years. During this course currently present alternative information processing platforms will be presented, such as quantum dot cellular automata, DNA based information processing platforms, nanotubes, optical information processing platforms etc. Moreover methods they employ will also be investigated, such as quantum processing, reversible processing, processing with more than two logical states, amorphous computing etc.

The aim of the course is to gain knowledge of the development of innovative cloud computing and cloud applications. Cloud computing is changing the way applications are developed. We will become familiar with concepts, architectures and cloud technologies - the cloud-native architecture.
In addition to gaining in-depth knowledge of cloud computing and all levels of service orientation (XaaS; Iaas, PaaS and SaaS), we will learn in detail the architectural model, patterns and best practices for the development of cloud-native applications, which includes a multitude of concepts.
We will begin with the microservice architecture and learn about the development patterns for microservice. We will combine this with Docker (and others) containers and micro-kernels. We will get familiar with container orchestration tools, especially Kubernetes (and others).
We will continue to explore the concepts of cloud-native architecture: services, asynchronous patterns of calling services, circuit breakers, reactive microservice development models, event streaming aka Apache Kafka, configuration, service discovery, health check, metrics, security, fault tolerance, and others.
The goal will be to understand and develop cloud applications that run on resilient and elastic cloud infrastructure and platforms and understand how these applications work and how to deploy them on different cloud services providers (Amazon AWS, Google AppEngine, Microsoft Azure, Cloud Foundry and others). As part of the course, we will get to know the most popular PaaS platforms. We will get familiar with the concepts of private, public and hybrid clouds. We will also set up our own private / hybrid computer cloud.
At the same time, we will become familiar with the DevOps practices that are essential for the development of cloud solutions and learn about the concepts and projects of the CNCF (Cloud Native Computing Foundation) such as Kubernetes, Prometheus, OpenTracing, Fluentd, Istio, Linkerd, gRPC and others. The course is practically oriented. Within the exercises, students in teams will develop innovative cloud solutions using framework such as Spring Boot, KumuluzEE, Node.js, Docker, Kubernetes, Prometheus and AWS, AppEngine, Azure clouds, and others. They will understand the importance of innovation in the cloud.
The best students will be able to participate in innovative projects.

  1. Parallel and distributed computing: need for parallelization
  2. Modern parallel architectures: shared-memory systems, distributed-memory systems, graphic processing units, modern coprocessors, FPGA circuits, heterogeneous systems
  3. Parallel languages and programming environments: OpenMP, OpenMPI, OpenCL
  4. Parallel algorithms, analysis and programming: data and functional parallelism, pipeline, scalability, programming strategies, patterns, concepts and examples, speedup analyis, scalability
  5. Implementation of typical scientific algorithms on mentioned architectures, choosing the right hardware architecture for an algorithm
  6. Parallel performance: load balancing, scheduling, communication overhead, cache effects, spatial and temporal locality, energy efficiency
  7. Using national high-performance computing infrastructure: access, computational power, working with data storage, environment setup, large-scale simulations
  8. Advanced topics: eksa-scale computing, FPGA programming, importance of data representation on speedup

This course is about computer hardware, with an emphasis on embedded systems connected to wireless networks on which we perform intelligent data processing. Input/output or peripheral devices are the most visible part of any computer system. They are connected to the computer in order to expand its functionality. They enable communication with the computer and more permanent data storage. However, since computer systems have become quite diverse, peripherals can also include sensors and actuators. Closely connection with peripheral devices are also buses used for connecting these devices. As buses are examples of electric transmission lines, we will look at the basic characteristics thereof, for example, phenomena such as reflections and crosstalk. We will also look at examples of drivers, i.e., programs that allow communication with peripherals. Edge computing devices are becoming more and more powerful (Edge computing), so we will consider examples of data capture and analysis with a Kalman filter and a convolutional neural network.