Transactional Memory: A Comprehensive Review of Implementation, Applications, Performance, Challenges, Framework Comparisons, and Future Prospects
Keywords:
Concurrency Management, Nested Transactions, Parallel Programming, Software Transactional Memory (STM), Transactional Memory (TM)Abstract
Transactional Memory (TM) offers a high-level synchronization abstraction for parallel programming, improving scalability, reliability, and productivity. It addresses challenges in multicore and distributed systems, surpassing traditional methods like locks and semaphores. TM implementation strategies—Software Transactional Memory (STM), Hardware Transactional Memory (HTM), and Hybrid Transactional Memory (HyTM)—present trade-offs in performance, scalability, and adaptability, catering to diverse workloads. Advanced features, including Nested Transactions, enhance fault tolerance and minimize rollback costs through modular transaction management.TM’s lock-free synchronization finds applications in concurrent data structures, graph algorithms, scalable systems, and real-time computing, boosting reliability and system performance. Performance analyses of STM, HTM, and HyTM highlight their strengths and limitations in handling varying workloads. However, challenges persist, such as programming model integration, contention management, and efficiently managing large or nested transactions. Innovations like Dynamic STM, Adaptive Conflict Resolution, and extended HTM support tackle these issues, advancing TM capabilities. Frameworks such as TCC and LogTM, along with STM and HTM implementations, illustrate TM's evolution. Future research aims to overcome current limitations, ensuring TM’s role in high-performance computing, real-time systems, and large-scale data processing. TM simplifies synchronization, empowering parallel programming to meet modern and future system requirements efficiently.
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