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Many distributed applications rely on the strong guarantees of sequential consistency to ensure program correctness. Replication systems or frameworks that support such applications typically implement sequential consistency by em- ploying voting schemes among replicas. However, such schemes suffer dramatic performance loss when deployed globally due to increased long-haul message latency between replicas in separate data centers. One approach to overcome this challenge involves deploying distinct instances of a service in each geographic cluster, then loosely coupling those services. Unfortunately, the consistency guarantees of the individual replication system in- stances do not compose when coupled this way, sacrificing overall sequential consistency. We propose an alternative approach, the consistent, propagatable partition tree (CoPPar Tree), a data structure that spans multiple data centers and data partitions, and that realizes sequential consistency using divide-and-conquer. By leveraging the geospatial affinity of data used in global services, CoPPar Tree can localize reads and writes in a sequentially consistent manner, improving the overall performance of a sequentially consistent service deployed at global scale. Our work allows clients to access local data and fully run SMR protocols locally without additional overhead. We implemented CoPPar Tree by enhancing ZooKeeper with an extension called ZooTree, which can be deployed without changing existing ZooKeeper clusters, and which achieves a speedup of 100×for reads and up to 10× for writes over prior work. 

Sparse matrix-matrix multiplication (SpMM) is a critical computational kernel in numerous scientific and machine learning applications. SpMM involves massive irregular memory accesses and poses great challenges to conventional cache-based computer architectures. Recently dedicated SpMM accelerators have been proposed to enhance SpMM performance. However, current SpMM accelerators still face challenges in adapting to varied sparse patterns, fully exploiting inherent parallelism, and optimizing cache performance. To address these issues, we introduce ACES, a novel SpMM accelerator in this study. First, ACES features an adaptive execution flow that dynamically adjusts to diverse sparse patterns. The adaptive execution flow balances parallel computing efficiency and data reuse. Second, ACES incorporates locality-concurrency co-optimizations within the global cache. ACES utilizes a concurrency-aware cache management policy, which considers data locality and concurrency for optimal replacement decisions. Additionally, the integration of a non-blocking buffer with the global cache enhances concurrency and reduces computational stalls. Third, the hardware architecture of ACES is designed to integrate all innovations. The architecture ensures efficient support across the adaptive execution flow, advanced cache optimizations, and fine-grained parallel processing. Our performance evaluation demonstrates that ACES significantly outperforms existing solutions, providing a 2.1× speedup and marking a substantial advancement in SpMM acceleration. 

Cache management is a critical aspect of computer architecture, encompassing techniques such as cache replacement, bypassing, and prefetching. Existing research has often focused on individual techniques, overlooking the potential benefits of joint optimization. Moreover, many of these approaches rely on static and intuition-driven policies, limiting their performance under complex and dynamic workloads. To address these challenges, this paper introduces CHROME, a novel concurrencyaware cache management framework. CHROME takes a holistic approach by seamlessly integrating intelligent cache replacement and bypassing with pattern-based prefetching. By leveraging online reinforcement learning, CHROME dynamically adapts cache decisions based on multiple program features and applies a reward for each decision that considers the accuracy of the action and the system-level feedback information. Our performance evaluation demonstrates that CHROME outperforms current state-of-the-art schemes, exhibiting significant improvements in cache management. Notably, CHROME achieves a remarkable performance boost of up to 13.7% over the traditional LRU method in multi-core systems with only modest overhead.