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Efficient Communication in Multi-GPU Systems
Špeťko, Matej ; Jaroš, Jiří (referee) ; Vaverka, Filip (advisor)
After the introduction of CUDA by Nvidia, the GPUs became devices capable of accelerating any general purpose computation. GPUs are designed as parallel processors which posses huge computation power. Modern supercomputers are often equipped with GPU accelerators. Sometimes the performance or the memory capacity of a single GPU is not enough for a scientific application. The application needs to be scaled into multiple GPUs. During the computation there is need for the GPUs to exchange partial results. This communication represents computation overhead. For this reason it is important to research the methods of the effective communication between GPUs. This means less CPU involvement, lower latency, shared system buffers. Inter-node and intra-node communication is examined. The main focus is on GPUDirect technologies from Nvidia and CUDA-Aware MPI. Subsequently k-Wave toolbox for simulating the propagation of acoustic waves is introduced. This application is accelerated by using CUDA-Aware MPI.
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Efficient Communication in Multi-GPU Systems
Špeťko, Matej ; Jaroš, Jiří (referee) ; Vaverka, Filip (advisor)
After the introduction of CUDA by Nvidia, the GPUs became devices capable of accelerating any general purpose computation. GPUs are designed as parallel processors which posses huge computation power. Modern supercomputers are often equipped with GPU accelerators. Sometimes single GPU performance is not enough for a scientific application and it needs to scale over multiple GPUs. During the computation, there is a need for the GPUs to exchange partial results. This communication represents computation overhead and it is important to research methods of the effective communication between GPUs. This means less CPU involvement, lower latency and shared system buffers. This thesis is focused on inter-node and intra-node GPU-to-GPU communication using GPUDirect technologies from Nvidia and CUDA-Aware MPI. Subsequently, k-Wave toolbox for simulating the propagation of acoustic waves is introduced. This application is accelerated by using CUDA-Aware MPI. Peer-to-peer transfer support is also integrated to k-Wave using CUDA Inter-process Communication.
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Simulation of Ultrasound Propagation in Bones
Kadlubiak, Kristián ; Vaverka, Filip (referee) ; Jaroš, Jiří (advisor)
It is estimated that mind-boggling 14.1 million new cases of cancer occurred worldwide in 2012 alone. This number is alarming. Although healthy lifestyle may reduce a risk of developing cancer, there is always some probability that cancer would develop even in an absolutely fit individual. There are two main conditions for successful treatment of cancer. Firstly, early diagnostic is absolutely crucial. Secondly, there is a need for suitable surgical methods for affected tissue removal. Ultrasound has a great potential to be used for both purposes as a non-invasive method. Photoacoustic spectroscopy is imaging method for tumor detection of great properties making the use of ultrasound while High-Intensity Focused Ultrasound (HIFU) is non-invasive surgical method. These methods would be impossible without precise ultrasound propagation simulations. The k-Wave is an open source MATLAB toolbox implementing such simulations. So, why are not these methods already deployed in treatment? Unfortunately, the simulation of ultrasound propagation is a very time consuming task, which makes it ineffective for medical purposes. However, there are a few options how to accelerate these simulations. The use of GPU is a very promising way to accelerate simulation. The main topic of this thesis is the acceleration of the simulation of soundwaves propagation in bones and hard tissue. The implementation developed as a part of this thesis was benchmarked on various supercomputers including Anselm in Ostrava and Piz Daint in Lugano. The implemented solution provides remarkable acceleration compared to the original MATLAB prototype. It was able to accelerate the simulation around 160 times in the best case. It means that the simulation, which would otherwise last for 6.5 days, can be now computed in one hour. This acceleration was achieved using an NVIDIA Tesla P100 to run the simulation with the domain size of 416x416x416 grid points. The thesis includes performance benchmarks on different GPUs to provide complex image acceleration capabilities of developed implementation and provides discussion about memory usage and numerical accuracy. Thanks to the implemented solution harnessing the power of modern GPUs, doctors and researchers all around the world have a powerful tool in hands.
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GridEngine Reporting Tool
Rožek, František ; Chalupníček, Kamil (referee) ; Kašpárek, Tomáš (advisor)
The aim of this work is to build a tool that will reflect the utilization of the computing cluster, built on Grid Engine technology. Data are processed using PHP and Shell scripts and then stored in MySQL, or RRD databases. The work created a system that handles huge amounts of data and provides a comprehensive view on the utilization of the entire cluster, but also its specific components, or statistics of individual users. Created solution provides current and long-term data. The result of this work allows you to watch computing cluster from a single tool, which was not possible before.
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Development and Programming of Low Power Cluster
Hradecký, Michal ; Nikl, Vojtěch (referee) ; Jaroš, Jiří (advisor)
This thesis deals with the building and programming of a low power cluster composed of Hardkernel Odroid XU4 kits based on ARM Cortex A15 and Cortex A7 chips. The goal was to design a simple cluster composed of multiple kits and run a set of benchmarks to analyze performance and power consumption. The test set consisted of HPL and Stream benchmarks and various tests for the MPI interface. The overall performance of the cluster composed of four kits in HPL benchmark was measured 23~GFLOP/s in double-precision. During this test, the cluster showed power efficiency about 0.58~GFLOP/W. The work also describes the installation of PBS Torque scheduler and HPC software build and installation framework EasyBuild on 32-bit ARM platform. The comparison with Anselm supercomputer showed that Odroid cluster is as effiecient as large supercomputer but with slightly higher price.
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Captured Communication Processing on Distributed System
Hvězda, Matěj ; Lichtner, Ondrej (referee) ; Pluskal, Jan (advisor)
When you need to assess or troubleshoot network by analysing capture file, you want it done as fast as possible and you do not always have a high-performance computer. Here comes the distributed system, which allows you to use his high computing power and lot of available memory. I introduce distributed application, which is scalable, extensible and capable of processing captured network communication and is developed for Windows platform. That provides technology, like Microsoft HPC Pack and Windows Communication foundation. The application supports multiple capture formats. In parallel system (cluster), exists database in order to save statistics and data of captured communication in order to save user's computer memory so client's application can be used for low-performance computers or make data available to a client after distributed processing.
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Particle Swarm Optimization on GPUs
Záň, Drahoslav ; Petrlík, Jiří (referee) ; Jaroš, Jiří (advisor)
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.
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Using GPU for HPC
Máček, Branislav ; Szőke, Igor (referee) ; Kašpárek, Tomáš (advisor)
Recently there was a significant grow in building HPC systems. Nowadays they are building from mainstream computer components. One of them is graphics accelerators with GPU. This thesis deals with description of graphics accelerators. It examines possibilities usage. GPU chip has hundreds simple processors. This thesis examine possibilities how to benefit from these parallel processors. It contains description of several testing applications, discuss results from experiments and compares them with another components used for HPC.
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Efficient Implementation of High Performance Algorithms on Intel Xeon Phi
Šimek, Dominik ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
This thesis is dedicated to the implementation of high performance algorithms on the Intel Xeon Phi coprocessor. The Xeon phi was introduced by Intel as a new MIC (Many Integrated Core) architecture in 2012. The theoretical part of the thesis is focused on the architecture of the coprocessor (with peak performance of 2 tFLOPS for a single precision data) and on the procedure of algorithms implementation and optimization. The theoretical knowledge is then applied to a practical examples with demonstration of the implementation and the optimization of algorithms and work with the coprocessor. In the practical part of the thesis, simple benchmarks such as a vector matrix multiplication and a matrix multiplication are explained and implemented. In the first benchmark 6.5% of theoretical coprocessor performance was achieved, in the second it was much more. In following chapter a more complex benchmark - simulation of a particles system (N-Body), that reached more than 35% of coprocessor performance (725 gFLOPS), is discussed. The following section is dedicated to some interesting problems such as optimization of a MATLAB module k-Wave (propagation of the ultrasound waves), extraction of I-vector (speech processing), cross-compilation of existing libraries, modules and programs. In the conclusion of the thesis the usage the potential of the Intel Xeon Phi is evaluated.
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Installation and configuration of Octave computation cluster
Mikulka, Zdeněk ; Hasmanda, Martin (referee) ; Sysel, Petr (advisor)
This diploma thesis contains detailed design of high-performance cluster, primarely focused for parallel computing in Octave application. Each of component of this cluster is described along with instructions for installation and configuration. Cluster is based on GNU/Linux operating system and Message Parsing Interface. Design alllows implementation of this cluster in computers of schoolroom with active lessons.
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