GPUs 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.