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Kernel Tuning Toolkit (KTT) is an autotuning framework for CUDA, OpenCL and Vulkan kernels. KTT provides advanced autotuning features such as support for both dynamic (online) and offline tuning, and an ability to tune multiple kernels together with shared tuning parameters. Furthermore, it offers customization features that make integration into larger software suites possible. The framework handles all major steps required for autotuning implementation, including configuration space creation and exploration, kernel code execution and output validation. The public API is available natively in C++ and via bindings in Python.

We have developed several autotuning benchmarks in CUDA that take into account performance-relevant source-code parameters and reach near peak-performance on various GPU architectures. We have used them during the development and evaluation of a search method for tuning space proposed in [1]. With our framework Kernel Tuning Toolkit, freely available at Github, we measured computation times and hardware performance counters on several GPUs for the complete tuning spaces of five benchmarks. These data, which we provide here, might benefit research of search algorithms for the tuning spaces of GPU codes or research of relation between applied code optimization, hardware performance counters, and GPU kernels’ performance.Moreover, we describe the scripts we used for robust evaluation of our searcher and comparison to others in detail. In particular, the script that simulates the tuning, i.e., replaces time-demanding compiling and executing the tuned kernels with a quick reading of the computation time from our measured data, makes it possible to inspect the convergence of tuning search over a large number of experiments. These scripts, freely available with our other codes, make it easier to experiment with search algorithms and compare them in a robust and reproducible way.During our research, we generated models for predicting values of performance counters from values of tuning parameters of our benchmarks. Here, we provide the models themselves and describe the scripts we implemented for their training. These data might benefit researchers who want to reproduce or build on our research.

In this work author deals with the modeling of the e-platform as a decision-support tool for improving the accessibility of urban areas in the Republic of Serbia. This research systematizes, analyzes and compares existing national and international practices, regulations and norms on the topic of accessibility. Through the research of existing knowledge and good practices and through field research, a theoretical setting of the platform model and an algorithm for calculating the current state of physical accessibility are created, on the example of an adequate statistical theoretical sample taken in the Republic of Serbia (territory of the city municipality of Stari grad of the city of Belgrade). The method for priority decision-making, intervention and application of Universal Design principles in the urban and physical environment in Serbia is presented through interdisciplinary and multi-institutional use of e-platforms. The e-platform model formed in this way will serve as a decision-making aid for all stakeholders, and will enable the current state of accessibility to be displayed in real time, and the approaches and methods in urban and architectural planning and design to be adjusted for more efficient application of Universal Design. This research aims to prove that if we create an e-platform that is accessible to all actors and the public, and data is collected in real time on the spatial components of accessibility, but also on the subjective components of the actors, then conditions are created for the optimization of the management of actions in order to improve accessibility in urban areas.