Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Community Detection on Gpu: a Comprehensive Analysis, Unified Memory Enhancement, and Memory Access Optimization(2023) Dinçer, Emre; Öz, IşılRecent years have experienced a slowdown in the development of traditional systems that use only the Central Processing Unit (CPU). However, significant progress has been made in the development of heterogeneous systems utilizing not only the CPU but also the Graphics Processing Unit (GPU). NVIDIA, one of the GPU manufacturers, through its CUDA platform, has increased the interest of many researchers in heterogeneous systems by providing a means to program GPUs more easily. The ease of application development provided by the CUDA platform and the performance gains offered by these heterogeneous systems have encouraged many researchers to develop algorithms and applications that operate on these systems. One such algorithm that is frequently used in data analysis is the community detection algorithm. Although there are applications that implement this algorithm to run on GPUs, and while these applications work efficiently for many datasets, they either fail to work or experience significant performance loss for large datasets that exceed the GPU's memory capacity. In this thesis, we analyzed Rundemanen, which is one of the community detection applications running on GPU. We also made enhancements that enable Rundemanen to process datasets larger than the GPU's memory capacity by utilizing CUDA's Unified Memory. Lastly, we tested various optimization methods to use Unified Memory more efficiently. By using our memory-access advises, in comparison to the naive version, we obtained up to 62x and 8x performance gain with artificial oversubscription scenarios and for datasets that already do not fit into GPU memory, respectively.Master Thesis Performance-Reliability Tradeoff Analysis for Safety-Critical Systems With Gpus(2023) Sezgin, Yağızcan; Öz, IşılGPUs were mostly used for image processing purposes when they were first introduced. These applications can be considered non-critical, and they were not given sufficient importance for reliability. Due to the evolving nature of GPUs, they offer highly parallelized architecture and provide extremely powerful computation, they become one of the most crucial parts of the systems that have complex applications in safety-critical domains such as automotive and space to fulfill the high computational demand. In this thesis, we evaluate the performance and reliability tradeoff in the safety-critical domain. We propose software-based redundancy schemes with different spheres of replications on the GPU4S benchmark in the safety-critical domain. Our proposal includes profiling the baseline application without any redundancy, applying fault injection using NVBitFI and changing implementation manually according to proposed redundancy schemes, measuring performance metrics such as execution time, memory copy operations, and power consumption on the real hardware that is widely used on target domain instead of using well-known GPU simulators to see actual performance. We reveal that our proposed redundancy schemes are managed to eliminate all the soft errors in the cases if we apply full redundancy for single-kernel benchmarks, for the reliability evaluation with the cost of performance degradation, depending on the application. We show that most soft errors can be eliminated using partial redundancy for complex applications, with a small performance impact.