We address two important concerns in the analysis of the behavior of applications in the presence of hardware errors: (1) when is it important to model how hardware faults lead to erroneous values (instruction-level errors) with high fidelity, as opposed to using simple bit-flipping models, and (2) how to enable fast high-fidelity error injection campaigns, in particular when error detectors are employed.

Single bit-flip has been the most popular error model for resilience studies with fault injection. We use RTL gate-level fault injection to show that this model fails to cover many realistic hardware faults. Specifically, single-event transients from combinational logic and single-event upsets in pipeline latches can lead to complex multi-bit errors at the architecture level. However, although accurate, RTL simulation is too slow to evaluate application-level resilience.

Unified Virtual Memory (UVM) was recently introduced on recent NVIDIA GPUs. Through software and hardware support, UVM provides a coherent shared memory across the entire heterogeneous node, migrating data as appropriate. The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments. Newer CUDA programs have started taking advantage of UVM for the same reasons of superior programmability that UNIX applications long ago switched to assuming the presence of virtual memory.

Intra-thread instruction duplication offers straightforward and effective pipeline error detection for data-intensive processors. However, software-enforced instruction duplication uses explicit checking instructions, roughly doubles program register usage, and doubles the number of arithmetic operations per thread, potentially leading to severe slowdowns. This paper investigates SwapCodes, a family of software-hardware cooperative mechanisms to accelerate intra-thread duplication in GPUs.

Training intelligent agents through reinforcement learning is a notoriously unstable procedure. Massive parallelization on GPUs and distributed systems has been exploited to generate a large amount of training experiences and consequently reduce instabilities, but the success of training remains strongly influenced by the choice of the hyperparameters.