Indiana University-Purdue University Indianapolis (IUPUI) / Performance models are useful as mathematical models to reason about the behavior of different computer systems while running various applications. In this thesis, we aim to provide two distinct performance models: one for distributed-memory high performance computing systems with network communication, and one for deep neural networks. Our main goal for the first model is insight and simplicity, while for the second we aim for accuracy in prediction. The first model is generalized for networked multi-core computer systems, while the second is specific to deep neural networks on a shared-memory system.

Performance models are useful as mathematical models to reason about the behavior of different computer systems while running various applications. In this thesis, we aim to provide two distinct performance models: one for distributed-<br>memory high performance computing systems with network communication, and one for deep neural networks. Our main goal for the first model is insight and simplicity, while for the second we aim for accuracy in prediction. The first model is generalized for networked multi-core computer systems, while the second is specific to deep neural networks on a shared-memory system.<br>

Understanding the performance of applications on modern multi- and manycore platforms is a difficult task and involves complex measurement, analysis, and modeling. The Roofline model is used to assess an application's performance on a given architecture. Not much work has been done with the Roofline model using real measurements. Because it can be a very useful tool for understanding application performance on a given architecture, in this thesis we demonstrate the use of architectural roofline data with measured data for analyzing the performance of different benchmarks. We first explain how to use different toolkits to measure the performance of a program. Next, these data are used to generate the roofline plots, based on which we can decide how can we make the application more efficient and remove bottlenecks. Our results show that this can be a powerful tool for analyzing performance of applications over different architectures and different code versions.

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V této práci představujeme blokovou metodu pro zlepšení lokality v cache paměti u výpočtů typu stencil a dva nástroje, Pluto a PATUS, které tuto metodu používají ke generování optimalizovaného kódu. Provádíme různá měření a zkoumáme zrychlení výpočtu při použití různých optimalizací. Nakonec implementujeme vyhlazovací krok v multigridu s různými optimalizacemi a zkoumáme jak se tyto optimalizace projeví na výkonu multigridu.