DYNAMIC DATA RACE DETECTION AND HEALING

Perpetual availability is an important operational goal in today’s computer systems. How-ever, achieving this goal is challenging because modern software systems contain faults that can cause them to fail. For example, multi-threading is widely used in modern software to fully utilize the computing capability of multicore processors. However, employing multi-threading can lead to concurrency faults such as deadlock and data race that are notoriously difficult to to isolate, detect, and repair.Data races, which involves two concurrent accesses to the same data where at least one is a write, are the most common concurrency faults. As our first step, we investigate the main sources of race detection overhead and find that a large effort is spent on repeatedly monitoring operations that cannot cause data races or have already been identified as causes of races. Based on these observations, we propose two orthogonal optimizations for race detection: Stationary Object Suppression (SOS) and Loop Iteration Sampling (LIS). SOS employs a dynamic program analysis technique to filter out Stationary Objects; which are read-only objects that can be shared by multiple threads. As such, they can never participate in data races. By eliminating monitoring operations on Stationary Objects, SO