Graphics processing units (GPUs) have become essential general-purpose computing platforms to accelerate a wide range of workloads, such as deep learning, scientific, and high-performance computing (HPC) applications. However, recent memory corruption attacks, such as buffer overflow, exposed security vulnerabilities in GPUs. We demonstrate that out-of-bounds writes are reproducible on an Nvidia GPU, which can enable other security attacks.

When designing, implementing and executing large-scale distributed simulation codes it is well-known the so-called optimistic time synchronization methods often lead to better overall performance as compared to the so-called conservative methods. Particularly when the model being simulated suffers from small lookahead between two or more of the logical processes working on the overall simulation execution. However, the design and implementation of simulation model codes intended to be executed using optimistic approach is often daunting, especially when working with existing codes which were designed and implemented to be used in a conservative approach. When executing simulation events, optimistic simulation models must first save any existing state in the model prior to executing codes that change that state, and furthermore must implement rollback code which restores that state to the original value. The necessity for such extra software functionality is often a barrier to the complete implementation of an optimistic-based simulation model and the corresponding improved performance.