Distributing Context-Aware Shared Memory Data Structures: A Case Study on Singly-Linked Lists
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915604832714752 |
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| author | Ravishankar, Raaghav Kulkarni, Sandeep Peri, Sathya Sharma, Gokarna |
| author_facet | Ravishankar, Raaghav Kulkarni, Sandeep Peri, Sathya Sharma, Gokarna |
| contents | In this paper, we study the partitioning of a context-aware shared memory data structure so that it can be implemented as a distributed data structure running on multiple machines. By context-aware data structures, we mean that the result of an operation not only depends upon the value of the shared data but also upon the previous operations performed by the same client. While there is substantial work on designing distributed data structures, designing distributed context-aware data structures has not received much attention.
We focus on singly-linked lists as a case study of the context-aware data structure. We start with a shared memory context-aware lock-free singly-linked list and show how it can be transformed into a distributed lock-free context-aware singly-linked list. The main challenge in such a transformation is to preserve properties of client-visible operations of the underlying data structure. We present two protocols that preserve these properties of client-visible operations of the linked list. In the first protocol, the distribution is done in the background as a low priority task, while in the second protocol the client-visible operations help the task of distribution without affecting client latency. In both protocols, the client-visible operations remain lock-free. Also, our transformation approach does not utilize any hardware primitives (except a compare-and-swap operation on a single word). We note that our transformation is generic and can be used for other lock-free context-aware data structures that can be constructed from singly-linked lists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributing Context-Aware Shared Memory Data Structures: A Case Study on Singly-Linked Lists Ravishankar, Raaghav Kulkarni, Sandeep Peri, Sathya Sharma, Gokarna Distributed, Parallel, and Cluster Computing In this paper, we study the partitioning of a context-aware shared memory data structure so that it can be implemented as a distributed data structure running on multiple machines. By context-aware data structures, we mean that the result of an operation not only depends upon the value of the shared data but also upon the previous operations performed by the same client. While there is substantial work on designing distributed data structures, designing distributed context-aware data structures has not received much attention. We focus on singly-linked lists as a case study of the context-aware data structure. We start with a shared memory context-aware lock-free singly-linked list and show how it can be transformed into a distributed lock-free context-aware singly-linked list. The main challenge in such a transformation is to preserve properties of client-visible operations of the underlying data structure. We present two protocols that preserve these properties of client-visible operations of the linked list. In the first protocol, the distribution is done in the background as a low priority task, while in the second protocol the client-visible operations help the task of distribution without affecting client latency. In both protocols, the client-visible operations remain lock-free. Also, our transformation approach does not utilize any hardware primitives (except a compare-and-swap operation on a single word). We note that our transformation is generic and can be used for other lock-free context-aware data structures that can be constructed from singly-linked lists. |
| title | Distributing Context-Aware Shared Memory Data Structures: A Case Study on Singly-Linked Lists |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2404.10151 |