Tardis: Time Traveling Coherence Algorithm for Distributed Shared Memory

Yu, Xiangyao; Devadas, Srinivas

doi:10.1109/pact.2015.12

Cited by 20 publications

(14 citation statements)

References 32 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our proof instead uses a combination of the timestamp and physical time orders [38]. A transaction's logical commit time is its commit timestamp; its physical commit time is the physical time between a transaction's validation phase and write phase.…”

Section: Proof Ideamentioning

confidence: 99%

TicToc

Pavlo

Sánchez

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

Self Cite

103

View full text Add to dashboard Cite

Concurrency control for on-line transaction processing (OLTP) database management systems (DBMSs) is a nasty game. Achieving higher performance on emerging many-core systems is difficult. Previous research has shown that timestamp management is the key scalability bottleneck in concurrency control algorithms. This prevents the system from scaling to large numbers of cores. In this paper we present TicToc, a new optimistic concurrency control algorithm that avoids the scalability and concurrency bottlenecks of prior T/O schemes. TicToc relies on a novel and provably correct data-driven timestamp management protocol. Instead of assigning timestamps to transactions, this protocol assigns read and write timestamps to data items and uses them to lazily compute a valid commit timestamp for each transaction. TicToc removes the need for centralized timestamp allocation, and commits transactions that would be aborted by conventional T/O schemes. We implemented TicToc along with four other concurrency control algorithms in an in-memory, shared-everything OLTP DBMS and compared their performance on different workloads. Our results show that TicToc achieves up to 92% better throughput while reducing the abort rate by 3.3× over these previous algorithms.

show abstract

Section: Proof Ideamentioning

confidence: 99%

TicToc

Pavlo

Sánchez

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

Self Cite

103

View full text Add to dashboard Cite

show abstract

“…The original Tardis protocol [10] implements sequential consistency, which requires that "the result of any execution is the same as if the operations of all processors (cores) were executed in some sequential order, and the operations of each individual processor (core) appear in this sequence in the order specified by its program" [19].…”

Section: Sequential Consistency (Sc)mentioning

confidence: 99%

“…Directory: Full-map MESI directory coherence protocol. Base Tardis: Baseline Tardis [10] where lts self increments by 1 for every 100 memory accesses.…”

Section: Baselinesmentioning

confidence: 99%

“…PACT '16, September 11-15, 2016 As an alternative, a recently proposed cache coherence protocol called Tardis shows better scalability while maintaining simplicity and high performance [10]. The key insight behind Tardis is that it is sufficient but unnecessary to enforce the global memory order in physical time order, which is what traditional coherence protocols do.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tardis 2.0

Liu²,

Zou

et al. 2016

Proceedings of the 2016 International Conference on Parallel Architectures and Compilation

Self Cite

View full text Add to dashboard Cite

Cache coherence scalability is a big challenge in shared memory systems. Traditional protocols do not scale due to the storage and traffic overhead of cache invalidation. Tardis, a recently proposed coherence protocol, removes cache invalidation using logical timestamps and achieves excellent scalability. The original Tardis protocol, however, only supports the Sequential Consistency (SC) memory model, limiting its applicability. Tardis also incurs extra network traffic on some benchmarks due to renew messages, and has suboptimal performance when the program uses spinning to communicate between threads.In this paper, we address these downsides of Tardis protocol and make it significantly more practical. Specifically, we discuss the architectural, memory system and protocol changes required in order to implement the TSO consistency model on Tardis, and prove that the modified protocol satisfies TSO. We also describe modifications for Partial Store Order (PSO) and Release Consistency (RC). Finally, we propose optimizations for better leasing policies and to handle program spinning. On a set of benchmarks, optimized Tardis improves on a full-map directory protocol in the metrics of performance, storage and network traffic, while being simpler to implement.

show abstract

“…The DBMS dynamically computes a transaction's commit timestamp, which must overlap with the leases of all tuples accessed by the transaction. Prior work has shown that logical leases are effective in improving performance and concurrency for both hardware cache coherence [52] and multicore concurrency control protocols [53]. To the best of our knowledge, this paper is the first to apply logical leases to a distributed system, and the first to seamlessly combine concurrency control and caching in a shared-nothing database.…”

Section: Introductionmentioning

confidence: 99%

Sundial

Xia

Pavlo

et al. 2018

Proc. VLDB Endow.

Self Cite

View full text Add to dashboard Cite

Distributed transactions suffer from poor performance due to two major limiting factors. First, distributed transactions suffer from high latency because each of their accesses to remote data incurs a long network delay. Second, this high latency increases the likelihood of contention among distributed transactions, leading to high abort rates and low performance.We present Sundial, an in-memory distributed optimistic concurrency control protocol that addresses these two limitations. First, to reduce the transaction abort rate, Sundial dynamically determines the logical order among transactions at runtime, based on their data access patterns. Sundial achieves this by applying logical leases to each data element, which allows the database to dynamically calculate a transaction's logical commit timestamp. Second, to reduce the overhead of remote data accesses, Sundial allows the database to cache remote data in a server's local main memory and maintains cache coherence. With logical leases, Sundial integrates concurrency control and cache coherence into a simple unified protocol. We evaluate Sundial against state-of-the-art distributed concurrency control protocols. Sundial outperforms the next-best protocol by up to 57% under high contention. Sundial's caching scheme improves performance by up to 4.6× in workloads with high access skew. PVLDB Reference Format:

show abstract

Tardis: Time Traveling Coherence Algorithm for Distributed Shared Memory

Cited by 20 publications

References 32 publications

TicToc

TicToc

Tardis 2.0

Sundial

Contact Info

Product

Resources

About