Compare BM25 and LLM-based vector embeddings for information retrieval

A Comprehensive AnalysisIntroductionConcurrency control represents one of the most critical aspects of multi-threaded application development, directly impacting performance, resource utilization, and system scalability. Concurrency control is a crucial aspect of multi-threaded application development, directly impacting performance and resource utilization. (Ishanavi Jain 2024) As computing systems increasingly leverage multi-core architectures and distributed environments, the selection and implementation of appropriate concurrency control mechanisms becomes paramount for achieving optimal application performance.The landscape of concurrency control mechanisms has evolved significantly, encompassing traditional lock-based approaches, lock-free data structures, software transactional memory systems, and optimistic concurrency control strategies. Each approach presents distinct trade-offs in terms of execution time, CPU utilization, memory consumption, and scalability characteristics. Understanding these trade-offs is essential for developers and system architects making informed decisions about concurrency control strategies for specific application domains and workload characteristics.Traditional Lock-Based MechanismsMutex and Semaphore Performance CharacteristicsTraditional lock-based concurrency control mechanisms, particularly mutexes and semaphores, continue to play a fundamental role in multi-threaded application design. Conducted in a Python environment within Google Colab, the study aims to assess performance metrics such as execution time, cpu usage, and memory usage. (Ishanavi Jain 2024) Each mechanisms performance was evaluated through repeated trials, and the results were aggregated and averaged to ensure reliability.Recent empirical analysis reveals nuanced performance characteristics among these traditional mechanisms. The findings reveal that the queue mechanism provides the fastest execution time, the semaphore mechanism, on the other hand, exhibited the lowest CPU usage, and the mutex mechanism offered a balanced performance in both speed and CPU efficiency. (Ishanavi Jain 2024) This balanced performance profile of mutexes makes them particularly suitable for applications requiring moderate concurrency levels without extreme performance optimization requirements.The memory utilization patterns of these mechanisms also demonstrate interesting characteristics. Memory usage was similar in mutex and semaphore, but queue differed significantly from the other two. (Ishanavi Jain 2024) This similarity in memory consumption between mutexes and semaphores suggests that the choice between these mechanisms should primarily consider CPU efficiency and execution time requirements rather than memory constraints.Limitations and Scalability ConcernsDespite their widespread adoption, traditional lock-based mechanisms face significant challenges in highly concurrent environments. Writing concurrent programs for shared memory multiprocessor systems is a nightmare. (Ajay Singh et al. 2017) This hinders users to exploit the full potential of multiprocessors. These challenges become particularly pronounced as the number of concurrent threads increases and contention for shared resources intensifies.Lock-Free and Wait-Free ApproachesQueue-Based Lock-Free ImplementationsLock-free data structures, particularly queue-based implementations, have emerged as promising alternatives to traditional locking mechanisms. This research project investigates the impact of various concurrency control mechanisms on the performance of multi-threaded applications, specifically focusing on mutexes, semaphores, and lock-free data structures implemented using queues. (Ishanavi Jain 2024) The superior execution time performance of queue-based mechanisms demonstrates the potential benefits of eliminating lock contention entirely.The effectiveness of lock-free approaches extends beyond simple performance metrics to encompass broader system reliability and fault tolerance characteristics. By systematically evaluating these concurrency control mechanisms, the project seeks to understand how each approach affects the efficiency of applications in a multi-threaded context. (Ishanavi Jain 2024) Lock-free implementations can provide better fault tolerance by eliminating the risks associated with lock-holding thread failures and priority inversion scenarios.Memory Management in Massively Parallel SystemsAdvanced lock-free approaches have shown particular promise in massively parallel computing environments. This paper presents a novel approach to dynamic memory allocation that eliminates the need for a centralized data structure. (Pham M et al. 2025) Our proposed approach revolves around letting threads employ random search procedures to locate free pages. This randomized approach to resource allocation demonstrates how lock-free designs can scale effectively in environments with thousands of concurrent threads.The performance benefits of advanced lock-free techniques can be substantial. Building upon these advancements, our mathematical proofs and experimental results affirm that these advanced designs can yield an order of magnitude improvement over the basic design and consistently outperform the state-of-the-art by up to two orders of magnitude. (Pham M et al. 2025) Such dramatic performance improvements highlight the potential of well-designed lock-free algorithms in specific application domains.Software Transactional MemorySTM Protocol Comparison and PerformanceSoftware Transactional Memory (STM) represents a paradigmatic shift in concurrency control, offering a promising alternative to traditional locking mechanisms. STM (Software Transactional Memory) is a promising concurrent programming paradigm which addresses woes of programming for multiprocessor systems. (Ajay Singh et al. 2017) STM systems abstract away the complexities of explicit locking while providing composable and deadlock-free concurrency control.Multiple STM protocols have been developed and evaluated, each with distinct performance characteristics. In this paper, we implement BTO (Basic Timestamp Ordering), SGT (Serialization Graph Testing) and MVTO(Multi-Version Time-Stamp Ordering) concurrency control protocols and build an STM(Software Transactional Memory) library to evaluate the performance of these protocols. (Ajay Singh et al. 2017) The systematic comparison of these protocols provides valuable insights into the trade-offs inherent in different STM design approaches.Performance Analysis of STM ProtocolsEmpirical analysis of STM protocols reveals significant performance variations across different configurations and workload characteristics. Our analysis shows that for a number of threads greater than 60 and update rate 70%, BTO takes (17% to 29%) and (6% to 24%) less CPU time per thread when compared against lazy-list and lock-coupling list respectively. (Ajay Singh et al. 2017) MVTO takes (13% to 24%) and (3% to 24%) less CPU time per thread when compared against lazy-list and lock-coupling list respectively.The comparative performance between different STM protocols also demonstrates interesting patterns. BTO and MVTO have similar per thread CPU time. (Ajay Singh et al. 2017) BTO and MVTO outperform SGT by 9% to 36%. These results suggest that timestamp-based ordering approaches (BTO and MVTO) generally provide superior performance compared to graph-based validation methods (SGT) in high-contention scenarios.Optimistic Concurrency ControlMulti-Version Concurrency Control (MVCC)Multi-Version Concurrency Control represents a sophisticated approach to managing concurrent access to shared data while maintaining consistency and isolation. Multi-Version Concurrency Control (MVCC) is a database management technique that enables concurrent access to a database while maintaining consistency and isolation between transactions. (Vipul Kumar Bondugula 2024) By maintaining multiple versions of records, MVCC allows readers to access older versions of data while writers update the current version, ensuring that each transaction gets a consistent snapshot of the data without being blocked by others.The fundamental advantage of MVCC lies in its ability to reduce lock contention and improve overall system throughput. This reduces the need for locks, allowing higher concurrency and better performance, especially in read-heavy environments. (Vipul Kumar Bondugula 2024) By eliminating the need for readers to block writers and vice versa, MVCC can significantly improve application responsiveness and scalability.Implementation Challenges and Trade-offsDespite its advantages, MVCC introduces specific implementation challenges and performance trade-offs. While MVCC provides high throughput and scalability, it introduces storage overhead due to the need to store multiple versions and can become complex as the number of versions grows. (Vipul Kumar Bondugula 2024) Additionally, MVCC can face issues like phantom reads and write skew, especially in transactions with complex interactions.The storage overhead associated with MVCC can become a significant concern in certain deployment scenarios. However, one downside is that the system must manage and track the various versions of each record, which can result in additional overhead for both storage and garbage collection. (Vipul Kumar Bondugula 2024) In some systems, when the number of versions becomes large, it may impact the systems overall performance. Effective garbage collection strategies and version management policies are crucial for maintaining MVCC system performance over time.Optimization Strategies for MVCCThe applicability and effectiveness of MVCC vary significantly based on workload characteristics and access patterns. MVCC is particularly effective in environments where read and write operations are frequent, as it minimizes contention between transactions. (Vipul Kumar Bondugula 2024) By enabling transactions to work with a snapshot of the data, it avoids conflicts that would otherwise arise from simultaneous read and write operations.Read-heavy workloads particularly benefit from MVCC implementations. It is especially beneficial in systems where many transactions read the same data concurrently while only a few perform updates. (Vipul Kumar Bondugula 2024) This characteristic makes MVCC an excellent choice for analytical workloads, reporting systems, and applications with predominantly read-based access patterns.Ongoing research continues to address MVCC limitations through improved implementation strategies. As database systems continue to evolve, improvements in MVCC implementations, such as optimized garbage collection, are helping to mitigate its limitations and enhance scalability. (Vipul Kumar Bondugula 2024) In conclusion, MVCC strikes a balance between concurrency and consistency, making it an ideal choice for certain high-performance, multi-user applications.Distributed Concurrency ControlTwo-Phase Commit Protocol EvolutionDistributed systems present unique challenges for concurrency control, requiring coordination across multiple nodes while maintaining consistency and performance. The two-phase commit (2PC) protocol is a key technique for achieving distributed transactions in storage systems such as relational databases and distributed databases. 2PC is a strongly consistent and centralized atomic commit protocol that ensures the serialization of the transaction execution order. (Pan Fan et al. 2020)However, traditional 2PC protocols face significant scalability limitations in modern distributed environments. However, it does not scale well to large and high-throughput systems, especially for applications with many transactional conflicts, such as microservices and cloud computing. (Pan Fan et al. 2020) Therefore, 2PC has a performance bottleneck for distributed transaction control across multiple microservices.Enhanced Distributed Concurrency ProtocolsRecent developments in distributed concurrency control have focused on addressing the scalability limitations of traditional protocols. In this paper, we propose 2PC*, a novel concurrency control protocol for distributed transactions that outperforms 2PC, allowing greater concurrency across multiple microservices. 2PC* can greatly reduce overhead because locks are held throughout the transaction process. (Pan Fan et al. 2020)The performance improvements achieved by enhanced distributed concurrency protocols can be substantial. When the contention becomes high, the experimental results show that 2PC* achieves at most a 3.3x improvement in throughput and a 67% reduction in latency, which proves that our scheme can easily support distributed transactions with multi-microservice modules. (Pan Fan et al. 2020) These improvements are particularly significant in high-contention scenarios where traditional approaches struggle to maintain acceptable performance levels.Concurrency Defect Management and RepairAutomated Concurrency Defect DetectionThe complexity of concurrent programming often leads to subtle defects that can significantly impact application reliability and performance. Traditional repair methods for concurrency defects may introduce new issues such as deadlocks, original semantic destruction, and high performance overhead. (Zhao J et al. 2025) Automated approaches to concurrency defect detection and repair represent an important complementary aspect of effective concurrency control.Modern defect repair techniques employ sophisticated strategies to address various types of concurrency issues. Then, ESfix optimizes the interrupt disable/enable strategies and lock strategies to repair data race and reduce bugs in information transmission, thereby reducing system entropy and improving data certainty and reliability. (Zhao J et al. 2025) Finally, ESfix repairs atomicity violation defects using the reordering repair strategy, reducing information entropy by adjusting the order of information to ensure its integrity and consistency.The importance of maintaining semantic correctness during automated repairs cannot be overstated. ESfix conducts semantic analysis by analyzing the dependency graph in the control flow graph (CFG) to ensure that no new defects are introduced during the repair process, and to maintain the efficiency and accuracy of information transmission between different parts of the code. (Zhao J et al. 2025) This approach demonstrates the potential for automated tools to enhance concurrency control effectiveness while maintaining application correctness.Performance Analysis and Comparative EvaluationApplication-Specific ConsiderationsThe effectiveness of different concurrency control mechanisms is highly dependent on specific application requirements and system characteristics. These results show that the choice of concurrency control mechanism should be guided by the specific requirements of the application, considering factors such as workload nature and system architecture. (Ishanavi Jain 2024) No single concurrency control approach provides optimal performance across all scenarios, necessitating careful analysis of application-specific requirements.The evaluation methodology employed in concurrency control studies must account for multiple performance dimensions to provide meaningful insights. By systematically evaluating these concurrency control mechanisms, the project seeks to understand how each approach affects the efficiency of applications in a multi-threaded context. (Ishanavi Jain 2024) Comprehensive evaluation requires consideration of execution time, CPU utilization, memory consumption, scalability characteristics, and fault tolerance properties.Scalability and Future ConsiderationsThe scalability characteristics of different concurrency control mechanisms become increasingly important as computing systems continue to evolve toward higher core counts and more complex distributed architectures. The ability of various approaches to maintain performance as concurrency levels increase represents a crucial factor in long-term system design decisions.Advanced techniques in specific domains demonstrate the potential for continued innovation in concurrency control. The system shows no slowing with high use loads, but rather increases performance due to graphical processing unit cache control mechanisms and is "future-proof" due to near unlimited parallel scalability. (Landvater RE and Balis U 2024) Such characteristics highlight the importance of considering architectural compatibility and scalability potential when selecting concurrency control mechanisms.ConclusionThe analysis of different concurrency control mechanisms reveals a complex landscape of trade-offs and application-specific considerations. Traditional lock-based approaches such as mutexes and semaphores continue to provide balanced performance for moderate concurrency scenarios, while lock-free implementations demonstrate superior execution times at the cost of increased memory complexity. Software Transactional Memory offers compositional benefits and deadlock freedom, with timestamp-based protocols generally outperforming graph-based validation approaches.Optimistic concurrency control through MVCC provides excellent performance for read-heavy workloads but introduces storage overhead and version management complexity. Distributed concurrency control continues to evolve, with enhanced protocols achieving significant throughput and latency improvements over traditional approaches. The integration of automated defect detection and repair capabilities represents an important complementary aspect of comprehensive concurrency control strategies.The effectiveness of any concurrency control mechanism ultimately depends on careful analysis of application requirements, workload characteristics, and system constraints. As computing architectures continue to evolve, the development of new concurrency control approaches and the refinement of existing techniques will remain critical areas of research and development. Future work should focus on developing adaptive concurrency control systems that can dynamically select and optimize mechanisms based on runtime characteristics and evolving application requirements.