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Secure architectures are becoming an increasingly common demand. This is due in large part to the rise of cloud computing, as users are trusting their data with hardware that they do not own. Unfortunately, many secure computation and isolation techniques are still susceptible to side-channel attacks. While various defenses to side-channel attacks exist, each tends to be targeted to a specific vulnerability and comes with a high runtime overhead, making it difficult to combine these defenses together in a performant manner.This work proposes an efficient design for preventing a large range of cache side-channel attacks by leveraging a near-memory processing (NMP) architecture. Specifically, the proposed design stores all sensitive data in isolated NMP vaults and performs all computation involving that sensitive data on NMP cores. Our approach eliminates possible cache side-channels while also minimizing runtime overhead when the parallelizability of NMP architecture is leveraged. Simulation results from a cycle-accurate architecture model shows that offloading secure computation to NMP cores can have as little as 0.26% overhead.
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HybriDS: Cache-Conscious Concurrent Data Structures for Near-Memory Processing Architectures
In recent years, the ever0increasing impact of memory access bottlenecks has brought forth a renewed interest in near-memory processing (NMP) architectures. In this work, we propose and empirically evaluate hybrid data structures, which are concurrent data structures custom-designed for these new NMP architectures. We focus on cache-optimized data structures, such as skiplists and B+ trees, that are often used as index structures in online transaction processing (OLTP) systems to enable fast key-based lookups. These data structures are hierarchical, where lookups begin at a small number of top-level nodes and diverge to many different node paths as they move down the hierarchy, such that nodes in higher levels benefit more from caching. Our proposed hybrid data structures split traditional hierarchical data structures into a host-managed portion consisting of higher-level nodes and an NMP-managed portion consisting of the remaining lower-level nodes, thus retaining and further enhancing the cache-conscious optimizations of their conventional implementations. Although the idea might seem relatively simple, the splitting of the data structure prompts new synchronization problems, and careful implementation is required to ensure high concurrency and correctness. We provide implementations of a hybrid skiplist and a hybrid B+ tree, and we empirically evaluate them on a cycle-accurate full-system architecture simulator. Our results show that the hybrid data structures have the potential to improve performance by more than 2X compared to state-of-the-art concurrent data structures.
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- PAR ID:
- 10346864
- Date Published:
- Journal Name:
- ACM Symposium on Parallelism in Algorithms and Architectures
- Page Range / eLocation ID:
- 321 to 332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3490148.3538591
