Efficacy and efficiency of algorithm-based fault-tolerance on GPUs

Computer simulations drive innovations in science and industry, and they are gaining more and more importance. However, their high computational demand generates extraordinary challenges for computing systems. Typical high-performance computing systems, which provide sufficient performance and high reliability, are extremely expensive. Modern GPUs offer high performance at very low costs, and they enable simulation applications on the desktop. However, they are increasingly prone to transient effects and other reliability threats. To fulfill the strict reliability requirements in scientific computing and simulation technology, appropriate fault tolerance measures have to be integrated into simulation applications for GPUs. Algorithm-Based Fault Tolerance on GPUs has the potential to meet these requirements. In this work we investigate the efficiency and the efficacy of ABFT for matrix operations on GPUs. We compare ABFT against fault tolerance schemes that are based on redundant computations and we evaluate its error detection capabilities.