Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 3CompreCity: Accelerating the Traveling Salesman Problem on GPU With Data Compression(Elsevier, 2025-05) Yalcin, Salih; Usul, Hamdi Burak; Yalcin, GulayTraveling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it is involved in many computing problems such as networks, genome analysis, logistics etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from the host device would reduce the performance in execution time. In this study, we applied three data compression methodologies to represent cities in the TSP such as (1) Using Greatest Common Divisor (2) Shift Cities to the Origin (3) Splitting Surface to Grids. Therefore, we include more cities in GPU memory and reduce the number of data transfers from the host device. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and The Lin-Kernighan-Helsgaun (LKH) Algorithm. We show that our implementation presents more than 25% performance improvement for both algorithms.Conference Object Citation - WoS: 8Citation - Scopus: 10CRC-Based Memory Reliability for Task-Parallel HPC Applications(IEEE, 2016-05) Subasi, Omer; Unsal, Osman; Labarta, Jesus; Yalcin, Gulay; Cristal, AdrianMemory reliability will be one of the major concerns for future HPC and Exascale systems. This concern is mostly attributed to the expected massive increase in memory capacity and the number of memory devices in Exascale systems. For memory systems Error Correcting Codes (ECC) are the most commonly used mechanism. However state-of-the art hardware ECCs will not be sufficient in terms of error coverage for future computing systems and stronger hardware ECCs providing more coverage have prohibitive costs in terms of area, power and latency. Software-based solutions are needed to cooperate with hardware. In this work, we propose a Cyclic Redundancy Checks (CRCs) based software mechanism for task-parallel HPC applications. Our mechanism incurs only 1.7% performance overhead with hardware acceleration while being highly scalable at large scale. Our mathematical analysis demonstrates the effectiveness of our scheme and its error coverage. Results show that our CRCbased mechanism reduces the memory vulnerability by 87% on average with up to 32-bit burst (consecutive) and 5-bit arbitrary error correction capability.Article Citation - WoS: 9Citation - Scopus: 13A Methodology for Comparing the Reliability of GPU-Based and CPU-Based HPCS(Assoc Computing Machinery, 2020-02-06) Cini, Nevin; Yalcin, GulayToday, GPUs are widely used as coprocessors/accelerators in High-Performance Heterogeneous Computing due to their many advantages. However, many researches emphasize that GPUs are not as reliable as desired yet. Despite the fact that GPUs are more vulnerable to hardware errors than CPUs, the use of GPUs in HPCs is increasing more and more. Moreover, due to native reliability problems of GPUs, combining a great number of GPUs with CPUs can significantly increase HPCs' failure rates. For this reason, analyzing the reliability characteristics of GPU-based HPCs has become a very important issue. Therefore, in this study we evaluate the reliability of GPU-based HPCs. For this purpose, we first examined field data analysis studies for GPU-based and CPU-based HPCs and identified factors that could increase systems failure/error rates. We then compared GPU-based HPCs with CPU-based HPCs in terms of reliability with the help of these factors in order to point out reliability challenges of GPU-based HPCs. Our primary goal is to present a study that can guide the researchers in this field by indicating the current state of GPU-based heterogeneous HPCs and requirements for the future, in terms of reliability. Our second goal is to offer a methodology to compare the reliability of GPU-based HPCs and CPU-based HPCs. To the best of our knowledge, this is the first survey study to compare the reliability of GPU-based and CPU-based HPCs in a systematic manner.