Sequential Consistency Violations Research Articles

Pacifier

Record and Deterministic Replay (R&R) of multithreaded programs on relaxed-consistency multiprocessors with distributed directory protocol has been a long-standing open problem. The independently developed RelaxReplay [8] solves the problem by assuming write atomicity. This paper proposes Pacifier, the first R&R scheme to provide a solution without assuming write atomicity. R&R for relaxed-consistency multiprocessors needs to detect, record and replay Sequential Consistency Violations (SCV). Pacifier has two key components: (i) Relog, a general memory reordering logging and replay mechanism that can reproduce SCVs in relaxed memory models, and (ii) Granule, an SCV detection scheme in the record phase with good precision, that indicates whether to record with Relog. We show that Pacifier is a sweet spot in the design space with a reasonable trade-off between hardware and log overhead. An evaluation with simulations mof 16, 32 and 64 processors with Release Consistency (RC) running SPLASH-2 applications indicates that Pacifier incurs 3.9% ~ 16% larger logs. The slowdown of Pacifier during replay is 10.1% ~ 30.5% compared to native execution

Volition

Sequential Consistency (SC) is the most intuitive memory model, and SC Violations (SCVs) produce unintuitive, typically incorrect executions. Most prior SCV detection schemes have used data races as proxies for SCVs, which is highly imprecise. Other schemes that have targeted data-race cycles are either too conservative or are designed only for two-processor cycles and snoopy-based systems. This paper presents Volition, the first hardware scheme that detects SCVs in a relaxed-consistency machine precisely, in a scalable manner, and for an arbitrary number of processors in the cycle. Volition leverages cache coherence protocol transactions to dynamically detect cycles in memory-access orders across threads. When a cycle is about to occur, an exception is triggered. Volition can be used in both directory- and snoopy-based coherence protocols. Our simulations of Volition in a 64-processor multicore with directory-based coherence running SPLASH-2 and Parsec programs shows that Volition induces negligible traffic and execution overhead. In addition, it can detect SCVs with several processors. Volition is suitable for on-the-fly use.

Volition

Sequential Consistency (SC) is the most intuitive memory model, and SC Violations (SCVs) produce unintuitive, typically incorrect executions. Most prior SCV detection schemes have used data races as proxies for SCVs, which is highly imprecise. Other schemes that have targeted data-race cycles are either too conservative or are designed only for two-processor cycles and snoopy-based systems. This paper presents Volition, the first hardware scheme that detects SCVs in a relaxed-consistency machine precisely, in a scalable manner, and for an arbitrary number of processors in the cycle. Volition leverages cache coherence protocol transactions to dynamically detect cycles in memory-access orders across threads. When a cycle is about to occur, an exception is triggered. Volition can be used in both directory- and snoopy-based coherence protocols. Our simulations of Volition in a 64-processor multicore with directory-based coherence running SPLASH-2 and Parsec programs shows that Volition induces negligible traffic and execution overhead. In addition, it can detect SCVs with several processors. Volition is suitable for on-the-fly use.

PACER

Data races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only after deployment. The frequency and unpredictability of these bugs will only increase as software adds parallelism to exploit multicore hardware. Unfortunately, sound and precise race detectors slow programs by factors of eight or more and do not scale to large numbers of threads. This paper presents a precise, low-overhead sampling-based data race detector called Pacer. PACER makes a proportionality guarantee: it detects any race at a rate equal to the sampling rate, by finding races whose first access occurs during a global sampling period. During sampling, PACER tracks all accesses using the dynamically sound and precise FastTrack algorithm. In nonsampling periods, Pacer discards sampled access information that cannot be part of a reported race, and Pacer simplifies tracking of the happens-before relationship, yielding near-constant, instead of linear, overheads. Experimental results confirm our theoretical guarantees. PACER reports races in proportion to the sampling rate. Its time and space overheads scale with the sampling rate, and sampling rates of 1-3% yield overheads low enough to consider in production software. The resulting system provides a "get what you pay for" approach that is suitable for identifying real, hard-to-reproduce races in deployed systems.