Biblio

MPI includes all processes in MPI\_COMM\_WORLD; this is untenable for reasons of scale, resiliency, and overhead. This paper offers a new approach, extending MPI with a new concept called Sessions, which makes two key contributions: a tighter integration with the underlying runtime system; and a scalable route to communication groups. This is a fundamental change in how we organise and address MPI processes that removes well-known scalability barriers by no longer requiring the global communicator MPI\_COMM\_WORLD.

This paper presents IPAS, an instruction duplication technique that protects scientific applications from silent data corruption (SDC) in their output. The motivation for IPAS is that, due to natural error masking, only a subset of SDC errors actually affects the output of scientific codes—we call these errors silent output corruption (SOC) errors. Thus applications require duplication only on code that, when affected by a fault, yields SOC. We use machine learning to learn code instructions that must be protected to avoid SOC, and, using a compiler, we protect only those vulnerable instructions by duplication, thus significantly reducing the overhead that is introduced by instruction duplication. In our experiments with five workloads, IPAS reduces the percentage of SOC by up to 90% with a slowdown that ranges between 1.04x and 1.35x, which corresponds to as much as 47% less slowdown than state-of-the-art instruction duplication techniques.