Over the last years Transactional Memory (TM) gained growing popularity as a simpler, attractive alternative to classic lock-based synchronization schemes. Recently, the TM landscape has been profoundly changed by the integration of Hardware TM (HTM) in Intel commodity processors, raising a number of questions on the future of TM.We seek answers to these questions by conducting the largest study on TM to date, comparing different locking techniques, hardware and software TMs, as well as different combinations of these mechanisms, from the dual perspective of performance and power consumption.Our study sheds a mix of light and shadows on currently available commodity HTM: on one hand, we identify workloads in which HTM clearly outperforms any alternative synchronization mechanism; on the other hand, we show that current HTM implementations suffer of restrictions that narrow the scope in which these can be more effective than state of the art software solutions. Thanks to the results of our study, we identify a number of compelling research problems in the areas of TM design, compilers and self-tuning.

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Abstract-This paper addresses the issue of maximizing the efficiency and scalability of distributed transactional platforms, by introducing Bumper, a set of innovative techniques to minimize aborts of transactions in high-contention scenarios. At its core, Bumper relies on two key ideas: (1) sparing update transactions from spurious aborts when they access concurrently updated data, by attempting to serialize them in the past via a novel distributed concurrency control scheme that we call Distributed Time-Warping (DTW); and (2) avoiding aborts due to contention hot spots (that cannot be tackled by DTW) via a novel programming abstraction, called delayed actions, which allows to efficiently serialize, in an abort-free fashion, the execution of conflict-prone data manipulations.The techniques used in Bumper can be applied to a wide variety of transactional replication protocols to enhance their performance in contention intensive workloads. In this paper we show how they can be integrated with SCORe, a recent, highly-scalable genuine partial replication protocol. By means of an extensive evaluation using well-known benchmarks and a cluster of 160 nodes, we show that Bumper can boost performance up to 3x in conflict-intensive workloads, while imposing negligible (2.5%) overheads in uncontended scenarios.

Hyperspace hashing is a recent multi-dimensional indexing technique for distributed key-value stores that aims at supporting efficient queries using multiple objects' attributes. However, the advantage of supporting complex queries comes at the cost of a complex configuration. In this paper we address the problem of automating the configuration of this innovative distributed indexing mechanism. We first show that a misconfiguration may significantly affect the performance of the system. We then derive a performance model that provides key insights on the behaviour of hyperspace hashing. Based on this model, we derive a technique to automatically and dynamically select the best configuration. 1

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The notion of permissiveness in Transactional Memory (TM) translates to only aborting a transaction when it cannot be accepted in any history that guarantees correctness criterion. This property is neglected by most TMs, which, in order to maximize implementation's efficiency, resort to aborting transactions under overly conservative conditions.In this paper we seek to identify a sweet spot between permissiveness and efficiency by introducing the Time-Warp Multi-version algorithm (TWM). TWM is based on the key idea of allowing an update transaction that has performed stale reads (i.e., missed the writes of concurrently committed transactions) to be serialized by "committing it in the past", which we call a time-warp commit. At its core, TWM uses a novel, lightweight validation mechanism with little computational overheads. TWM also guarantees that readonly transactions can never be aborted. Further, TWM guarantees Virtual World Consistency, a safety property that is deemed as particularly relevant in the context of TM. We demonstrate the practicality of this approach through an extensive experimental study, where we compare TWM with four other TMs, and show an average performance improvement of 65% in high concurrency scenarios.

Scheduling concurrent transactions to minimize contention is a well known technique in the Transactional Memory (TM) literature, which was largely investigated in the context of software TMs. However, the recent advent of Hardware Transactional Memory (HTM), and its inherently restricted nature, pose new technical challenges that prevent the adoption of existing schedulers: unlike software implementations of TM, existing HTMs provide no information on which data item or contending transaction caused abort.We propose Seer, a scheduler that addresses precisely this restriction of HTM by leveraging on an on-line probabilistic inference technique that identifies the most likely conflict relations, and establishes a dynamic locking scheme to serialize transactions in a fine-grained manner. Our evaluation shows that Seer improves the performance of the Intel TSX HTM by up to 2.5×, and by 62% on average, in TM benchmarks with 8 threads. These performance gains are not only a consequence of the reduced aborts, but also of the reduced activation of the HTM's pessimistic fall-back path.

In this article we present STI-BT, a highly scalable, transactional index for Distributed Key-Value (DKV) stores. STI-BT is organized as a distributed B+Tree and adopts an innovative design that allows to achieve high efficiency in large-scale, elastic DKV stores. We have implemented STI-BT on top of a mainstream open-source DKV store and deployed it on a public cloud infrastructure. Our extensive experimental study reveals the efficiency of our solution with demonstrable scalability in a cluster of 100 commodity machines, and speed ups with respect to state of the art solutions of up to 5.4x.