Rerun: Exploiting Episodes for Lightweight Memory Race Recording

Abstract

Multiprocessor deterministic replay has many potential uses in the era of multicore computing, including enhanced debugging, fault tolerance, and intrusion detection. While sources of nondeterminism in a uniprocessor can be recorded efficiently in software, it seems likely that hardware support will be needed in a multiprocessor environment where the outcome of memory races must also be recorded. We develop a memory race recording mechanism, called Rerun, that uses small hardware state (~166 bytes/core), writes a small race log (~4 bytes/kilo- instruction), and operates well as the number of cores per system scales (e.g., to 16 cores). Rerun exploits the dual of conventional wisdom in race recording: Rather than record information about individual memory accesses that conflict, we record how long a thread executes without conflicting with other threads. In particular, Rerun passively creates atomic episodes. Each episode is a dynamic instruction sequence that a thread happens to execute without interacting with other threads. Rerun uses Lamport Clocks to order episodes and enable replay of an equivalent execution.