Efficient Management of Short-Lived Data

Motivated by the increasing prominence of loosely-coupled systems, such as mobile and sensor networks, which are characterised by intermittent connectivity and volatile data, we study the tagging of data with so-called expiration times. More specifically, when data are inserted into a database, they may be tagged with time values indicating when they expire, i.e., when they are regarded as stale or invalid and thus are no longer considered part of the database. In a number of applications, expiration times are known and can be assigned at insertion time. We present data structures and algorithms for online management of data tagged with expiration times. The algorithms are based on fully functional, persistent treaps, which are a combination of binary search trees with respect to a primary attribute and heaps with respect to a secondary attribute. The primary attribute implements primary keys, and the secondary attribute stores expiration times in a minimum heap, thus keeping a priority queue of tuples to expire. A detailed and comprehensive experimental study demonstrates the well-behavedness and scalability of the approach as well as its efficiency with respect to a number of competitors.Comment: switched to TimeCenter latex styl

AVL Trees With Relaxed Balance

AVL trees with relaxed balance were introduced with the aim of improving runtime per formance by allowing a greater degree of concurrency. This is obtained by uncoupling updating from rebalancing. An additional benefit is that rebalancing can be controlled separately. In particular, it can be postponed completely or partially until after peak working hours.We define a new collection of rebalancing operations which allows for a significantly greater degree of concurrency than the original proposal. Additionally, in contrast to the original proposal, we prove the complexity of our algorithm.If N is the maximum size of the tree, we prove that each insertion gives rise to at most I_ log_Phi(N + 3/2) + log_Phi(squareroot{5}) - 3 _I rebalancing operations and that each deletion gives rise to at most I_ log_Phi(N + 3/2) + log_Phi(squareroot{5}) - 4 _I rebalancing operations, where Phi is the golden ratio

Search Tree Data Structures and Their Applications

This study concerns the discussion of search tree data structures and their applications. The thesis presents three new top-down updating algorithms for the concurrent data processing environment.Computing and Information Scienc

Parallel Working-Set Search Structures

In this paper we present two versions of a parallel working-set map on p processors that supports searches, insertions and deletions. In both versions, the total work of all operations when the map has size at least p is bounded by the working-set bound, i.e., the cost of an item depends on how recently it was accessed (for some linearization): accessing an item in the map with recency r takes O(1+log r) work. In the simpler version each map operation has O((log p)^2+log n) span (where n is the maximum size of the map). In the pipelined version each map operation on an item with recency r has O((log p)^2+log r) span. (Operations in parallel may have overlapping span; span is additive only for operations in sequence.) Both data structures are designed to be used by a dynamic multithreading parallel program that at each step executes a unit-time instruction or makes a data structure call. To achieve the stated bounds, the pipelined data structure requires a weak-priority scheduler, which supports a limited form of 2-level prioritization. At the end we explain how the results translate to practical implementations using work-stealing schedulers. To the best of our knowledge, this is the first parallel implementation of a self-adjusting search structure where the cost of an operation adapts to the access sequence. A corollary of the working-set bound is that it achieves work static optimality: the total work is bounded by the access costs in an optimal static search tree.Comment: Authors' version of a paper accepted to SPAA 201

Concurrent Data Structures Using Multiword Compare and Swap

To maximize the performance of concurrent data structures, researchers have turned to highly complex fine-grained techniques. Resulting algorithms are often extremely difficult to understand and prove correct, allowing for highly cited works to contain correctness bugs that go undetected for long periods of time. This complexity is perceived as a necessary sacrifice: simpler, more general techniques cannot attain competitive performance with these fine-grained implementations. To challenge this perception, this work presents three data structures created using multi-word compare-and-swap (KCAS), version numbering, and double-collect searches that showcase the power of using a more coarse-grained approach. First, a novel lock-free binary search tree (BST) is presented that is both fully-internal and balanced, which is able to achieve competitive performance with the state-of-the-art fine-grained concurrent BSTs while being significantly simpler. Next, the first concurrent implementation of an Euler-tour data-structure is outlined, solving fully-dynamic graph connectivity. Finally, a KCAS variant of an (a,b)-tree implementation is presented, which shows significant performance improvements in certain workloads when compared to the original

A Template for Implementing Fast Lock-free Trees Using HTM

Algorithms that use hardware transactional memory (HTM) must provide a software-only fallback path to guarantee progress. The design of the fallback path can have a profound impact on performance. If the fallback path is allowed to run concurrently with hardware transactions, then hardware transactions must be instrumented, adding significant overhead. Otherwise, hardware transactions must wait for any processes on the fallback path, causing concurrency bottlenecks, or move to the fallback path. We introduce an approach that combines the best of both worlds. The key idea is to use three execution paths: an HTM fast path, an HTM middle path, and a software fallback path, such that the middle path can run concurrently with each of the other two. The fast path and fallback path do not run concurrently, so the fast path incurs no instrumentation overhead. Furthermore, fast path transactions can move to the middle path instead of waiting or moving to the software path. We demonstrate our approach by producing an accelerated version of the tree update template of Brown et al., which can be used to implement fast lock-free data structures based on down-trees. We used the accelerated template to implement two lock-free trees: a binary search tree (BST), and an (a,b)-tree (a generalization of a B-tree). Experiments show that, with 72 concurrent processes, our accelerated (a,b)-tree performs between 4.0x and 4.2x as many operations per second as an implementation obtained using the original tree update template