Offline Symbolic Analysis for Multi-Processor Execution Replay

Ability to replay a program’s execution on a multi-processor system can significantly help parallel programming. To replay a shared-memory multi-threaded program, existing solutions record its program input (I/O, DMA, etc.) and the shared-memory dependencies between threads. Prior processor based record-and-replay solutions are efficient, but they require non-trivial modifications to the coherency protocol and the memory sub-system for recording the shared-memory dependencies. In this paper, we propose a processor-based record-andreplay solution that does not require detecting and logging shared-memory dependencies to enable multi-processor execution replay. We show that a load-based checkpointing scheme, which was originally proposed for just recording program input, is also sufficient for replaying every thread in a multi-threaded program. Shared-memory dependencies between threads are reconstructed offline, during replay, using an algorithm based on an SMT solver. In addition to saving log space, the proposed solution significantly reduces the complexity of hardware support required for enabling replay

Holistic System Design for Deterministic Replay.

Deterministic replay systems record and reproduce the execution of a hardware or software system. While it is well known how to replay uniprocessor systems, it is much harder to provide deterministic replay of shared memory multithreaded programs on multiprocessors because shared memory accesses add a high-frequency source of non-determinism. This thesis proposes efficient multiprocessor replay systems: Respec, Chimera, and Rosa. Respec is an operating-system-based replay system. Respec is based on the observation that most program executions are data-race-free and for programs with no data races it is sufficient to record program input and the happens-before order of synchronization operations for replay. Respec speculates that a program is data-race-free and supports rollback and recovery from misspeculation. For racy programs, Respec employs a cheap runtime check that compares system call outputs and memory/register states of recorded and replayed processes at a semi-regular interval. Chimera uses a sound static data race detector to find all potential data races and instrument pairs of potentially racing instructions to transform an arbitrary program to make it data-race-free. Then, Chimera records only the non-deterministic inputs and the order of synchronization operations for replay. However, existing static data race detectors generate excessive false warnings, leading to high recording overhead. Chimera resolves this problem by employing a combination of profiling, symbolic analysis, and dynamic checks that target the sources of imprecision in the static data race detector. Rosa is a processor-based ultra-low overhead (less than one percent) replay solution that requires very little hardware support as it essentially only needs a log of cache misses to reproduce a multiprocessor execution. Unlike previous hardware-assisted systems, Rosa does not record shared memory dependencies at all. Instead, it infers them offline using a Satisfiability Modulo Theories (SMT) solver. Our offline analysis is capable of inferring interleavings that are legal under the Sequentially Consistency (SC) and Total Store Order (TSO) memory models.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102374/1/dongyoon_1.pd