Soft error resilient QR factorization for hybrid system with GPGPU

As the general purpose graphics processing units (GPGPU) are increasingly deployed for scientific computing for its raw perfor-mance advantages compared to CPUs, the fault tolerance issue has started to become more of a concern than before when they were exclusively used for graphics applications. The pairing of GPUs with CPUs to form a hybrid computing systems for better flexi-bility and performance creates a massive amounts of computations that have a higher possibility to be affected by transient error – a soft error that silently modifies data causing errors to pass unno-ticed. This is despite the fact that the newest Fermi generation of GPUs from NVIDIA are equipped with error correcting units to protect their memories. This problem is particularly serious for ap-plications that employ numerical linear algebra since large sections of data are often modified between steps, and therefore even a sin