Optimizing Persistent Memory Transactions

Byte-addressable, non-volatile, random access memory (NVM) has the potential to dramatically accelerate the performance of storage-intensive workloads. For applications with irregular data access patterns, and applications that rely on ad-hoc data structures, the most promising model for interacting...

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Vydáno v:Proceedings / International Conference on Parallel Architectures and Compilation Techniques s. 219 - 231
Hlavní autoři: Zardoshti, Pantea, Zhou, Tingzhe, Liu, Yujie, Spear, Michael
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2019
Témata:
Concurrency
Hardware
Non volatile Memory
Nonvolatile memory
Optimization
Persistence
Programming
Random access memory
Scalability
Synchronization
Throughput
Transactional Memory
ISSN:2641-7936
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Abstract Byte-addressable, non-volatile, random access memory (NVM) has the potential to dramatically accelerate the performance of storage-intensive workloads. For applications with irregular data access patterns, and applications that rely on ad-hoc data structures, the most promising model for interacting with NVM is a transactional model. However, the specifics of the model matter significantly. We introduce two models for programming persistent transactions. We show how to build concurrent persistent transactional memory from traditional software transactional memories. We then introduce general and model-specific optimizations that can substantially improve the performance of persistent transactions. Our evaluation shows a substantial improvement in the both the latency and scalability of persistent transactions.
AbstractList Byte-addressable, non-volatile, random access memory (NVM) has the potential to dramatically accelerate the performance of storage-intensive workloads. For applications with irregular data access patterns, and applications that rely on ad-hoc data structures, the most promising model for interacting with NVM is a transactional model. However, the specifics of the model matter significantly. We introduce two models for programming persistent transactions. We show how to build concurrent persistent transactional memory from traditional software transactional memories. We then introduce general and model-specific optimizations that can substantially improve the performance of persistent transactions. Our evaluation shows a substantial improvement in the both the latency and scalability of persistent transactions.
Author Zardoshti, Pantea
Zhou, Tingzhe
Liu, Yujie
Spear, Michael
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  organization: Lehigh University
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StartPage 219
SubjectTerms Concurrency
Hardware
Non volatile Memory
Nonvolatile memory
Optimization
Persistence
Programming
Random access memory
Scalability
Synchronization
Throughput
Transactional Memory
Title Optimizing Persistent Memory Transactions
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