Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Compiler-Managed Replication of Cuda Kernels for Reliable Execution of Gpgpu Applications(World Scientific, 2024) Kaya,E.; Öz,I.As Graphics Processing Units (GPUs) evolve for general-purpose computations besides inherently fault-tolerant graphics programs, soft error reliability becomes a first-class citizen in program design. Especially, safety-critical systems utilizing GPU devices need to employ fault-tolerance techniques to recover from errors in hardware components. While software-level redundancy approaches, based on the replication of the application code, offer high reliability for safe program execution, it is essential to perform redundancy by utilizing parallel execution units in the target architecture not to hurt performance with redundant computations. In this work, we propose redundancy approaches using the parallel GPU cores and implement a compiler-level redundancy framework that enables the programmer to configure the target GPGPU program for redundant execution. We run redundant executions for GPGPU programs from the PolyBench benchmark suite by applying our kernel-level redundancy approaches and evaluate their performance by considering the parallelism level of the programs. Our results reveal that redundancy approaches utilizing parallelism offered by GPU cores yield higher performance for redundant executions, while the programs that already make use of parallel GPU cores in their original form suffer from overhead caused by contention among redundant threads. © World Scientific Publishing Company.Article Performance and Accuracy Predictions of Approximation Methods for Shortest-Path Algorithms on Gpus(Elsevier, 2022) Aktılav, Busenur; Öz, IşılApproximate computing techniques, where less-than-perfect solutions are acceptable, present performance-accuracy trade-offs by performing inexact computations. Moreover, heterogeneous architectures, a combination of miscellaneous compute units, offer high performance as well as energy efficiency. Graph algorithms utilize the parallel computation units of heterogeneous GPU architectures as well as performance improvements offered by approximation methods. Since different approximations yield different speedup and accuracy loss for the target execution, it becomes impractical to test all methods with various parameters. In this work, we perform approximate computations for the three shortest-path graph algorithms and propose a machine learning framework to predict the impact of the approximations on program performance and output accuracy. We evaluate random predictions for both synthetic and real road-network graphs, and predictions of the large graph cases from small graph instances. We achieve less than 5% prediction error rates for speedup and inaccuracy values.