Mechanisms for Unbounded, Conflict-Robust Hardware Transactional Memory

Conventional lock implementations serialize access to critical sections guarded by the same lock, presenting programmers with a difficult tradeoff between granularity of synchronization and amount of parallelism realized. Recently, researchers have been investigating an emerging synchronization mechanism called transactional memory as an alternative to such conventional lock-based synchronization. Memory transactions have the semantics of executing in isolation from one another while in reality executing speculatively in parallel, aborting when necessary to maintain the appearance of isolation. This combination of coarse-grained isolation and optimistic parallelism has the potential to ease the tradeoff presented by lock-based programming. This dissertation studies the hardware implementation of transactional memory, making three main contributions. First, we propose the permissions-only cache, a mechanism that efficiently increases the size of transactions that can be handled in the local cache hierarchy to optimize performance. Second, we propose OneTM, an unbounded hardware transactional memory system that serializes transactions that escape the local cache hierarchy. Finally, we propose RetCon, a novel mechanism for detecting conflicts that reduces conflicts by allowing transactions to commit with different values than those with which they executed as long as dataflow and control-flow constraints are maintained

Lock-free dynamic hash tables with open addressing

We present an efficient lock-free algorithm for parallel accessible hash tables with open addressing, which promises more robust performance and reliability than conventional lock-based implementations. “Lock-free” means that it is guaranteed that always at least one process completes its operation within a bounded number of steps. For a single processor architecture our solution is as efficient as sequential hash tables. On a multiprocessor architecture this is also the case when all processors have comparable speeds. The algorithm allows processors that have widely different speeds or come to a halt. It can easily be implemented using C-like languages and requires on average only constant time for insertion, deletion or accessing of elements. The algorithm allows the hash tables to grow and shrink when needed. Lock-free algorithms are hard to design correctly, even when apparently straightforward. Ensuring the correctness of the design at the earliest possible stage is a major challenge in any responsible system development. In view of the complexity of the algorithm, we turned to the interactive theorem prover PVS for mechanical support. We employ standard deductive verification techniques to prove around 200 invariance properties of our algorithm, and describe how this is achieved with the theorem prover PVS.

Fast Arrays: Atomic Arrays with Constant Time Initialization

Some algorithms require a large array, but only operate on a small fraction of its indices. Examples include adjacency matrices for sparse graphs, hash tables, and van Emde Boas trees. For such algorithms, array initialization can be the most time-consuming operation. Fast arrays were invented to avoid this costly initialization. A fast array is a software implementation of an array, such that the entire array can be initialized in just constant time. While algorithms for sequential fast arrays have been known for a long time, to the best of our knowledge, there are no previous algorithms for concurrent fast arrays. We present the first such algorithms in this paper. Our first algorithm is linearizable and wait-free, uses only linear space, and supports all operations - initialize, read, and write - in constant time. Our second algorithm enhances the first to additionally support all the read-modify-write operations available in hardware (such as compare-and-swap) in constant time

Parallel and Distributed Data Series Processing on Modern and Emerging Hardware

This paper summarizes state-of-the-art results on data series processing with the emphasis on parallel and distributed data series indexes that exploit the computational power of modern computing platforms. The paper comprises a summary of the tutorial the author delivered at the 15th International Conference on Management of Digital EcoSystems (MEDES'23).Comment: This paper will appear in the Proceedings of the 15th International Conference on Management of Digital EcoSystems (MEDES'23

Crafting Concurrent Data Structures

Concurrent data structures lie at the heart of modern parallel programs. The design and implementation of concurrent data structures can be challenging due to the demand for good performance (low latency and high scalability) and strong progress guarantees. In this dissertation, we enrich the knowledge of concurrent data structure design by proposing new implementations, as well as general techniques to improve the performance of existing ones.The first part of the dissertation present an unordered linked list implementation that supports nonblocking insert, remove, and lookup operations. The algorithm is based on a novel ``enlist\u27\u27 technique that greatly simplifies the task of achieving wait-freedom. The value of our technique is also demonstrated in the creation of other wait-free data structures such as stacks and hash tables.The second data structure presented is a nonblocking hash table implementation which solves a long-standing design challenge by permitting the hash table to dynamically adjust its size in a nonblocking manner. Additionally, our hash table offers strong theoretical properties such as supporting unbounded memory. In our algorithm, we introduce a new ``freezable set\u27\u27 abstraction which allows us to achieve atomic migration of keys during a resize. The freezable set abstraction also enables highly efficient implementations which maximally exploit the processor cache locality. In experiments, we found our lock-free hash table performs consistently better than state-of-the-art implementations, such as the split-ordered list.The third data structure we present is a concurrent priority queue called the ``mound\u27\u27. Our implementations include nonblocking and lock-based variants. The mound employs randomization to reduce contention on concurrent insert operations, and decomposes a remove operation into smaller atomic operations so that multiple remove operations can execute in parallel within a pipeline. In experiments, we show that the mound can provide excellent latency at low thread counts.Lastly, we discuss how hardware transactional memory (HTM) can be used to accelerate existing nonblocking concurrent data structure implementations. We propose optimization techniques that can significantly improve the performance (1.5x to 3x speedups) of a variety of important concurrent data structures, such as binary search trees and hash tables. The optimizations also preserve the strong progress guarantees of the original implementations

The SkipTrie: low-depth concurrent search without rebalancing

To date, all concurrent search structures that can support predecessor queries have had depth logarithmic in m, the number of elements. This paper introduces the SkipTrie, a new concurrent search structure supporting predecessor queries in amortized expected O(log log u + c) steps, insertions and deletions in O(c log log u), and using O(m) space, where u is the size of the key space and c is the contention during the recent past. The SkipTrie is a probabilistically-balanced version of a y-fast trie consisting of a very shallow skiplist from which randomly chosen elements are inserted into a hash-table based x-fast trie. By inserting keys into the x-fast-trie probabilistically, we eliminate the need for rebalancing, and can provide a lock-free linearizable implementation. To the best of our knowledge, our proof of the amortized expected performance of the SkipTrie is the first such proof for a tree-based data structure.National Science Foundation (U.S.) (Grant CCF-1217921)United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Grant ER26116/DE-SC0008923)Oracle CorporationIntel Corporatio