Quantifying the impact of data replication on error propagation

Abstract

Various technological developments in the microprocessor world make modern computing systems more vulnerable to soft errors than in the past, and consequently fault tolerance techniques are becoming increasingly important in various application domains. While in general fault tolerance methods are known to achieve high levels of reliability, they can also introduce significant performance, energy, and memory overheads, which can be reduced by employing such techniques selectively, as opposed to indiscriminately. Data Replication is used to prevent error propagation across hardware components and application program data structures by replicating application program's data. When using data replication, many factors need to be taken into account, including which data structures/elements to replicate, how many times to replicate a given data element, and which threads to protect (in a multithreaded application). These and similar factors define what can be termed as ''replication space''. This study defines a replication space, and systematically explores protection techniques of various strengths/degrees, quantifying their impacts on memory consumption, performance, and error propagation. Our experimental analysis reveals that different degrees of protection levels bring different outcomes based on the application specifics. In particular, while error propagation is limited, to a certain extent, when employing data replication in multithreaded applications where the thread do not communicate/share data much, the speed of error propagation across threads can be quite fast in applications where threads are more tightly coupled. Additionally, our results indicate that in certain cases where error propagation is low, the effect of data replication on error propagation can be negligible.