Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs

Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution. We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all

Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and Distributed System

Energy Efficiency of Software Transactional Memory in a Heterogeneous Architecture

Hardware vendors make an important effort creating low-power CPUs that keep battery duration and durability above acceptable levels. In order to achieve this goal and provide good performance-energy for a wide variety of applications, ARM designed the big.LITTLE architecture. This heterogeneous multi-core architecture features two different types of cores: big cores oriented to performance and little cores, slower and aimed to save energy consumption. As all the cores have access to the same memory, multi-threaded applications must resort to some mutual exclusion mechanism to coordinate the access to shared data by the concurrent threads. Transactional Memory (TM) represents an optimistic approach for shared-memory synchronization. To take full advantage of the features offered by software TM, but also benefit from the characteristics of the heterogeneous big.LITTLE architectures, our focus is to propose TM solutions that take into account the power/performance requirements of the application and what it is offered by the architecture. In order to understand the current state-of-the-art and obtain useful information for future power-aware software TM solutions, we have performed an analysis of a popular TM library running on top of an ARM big.LITTLE processor. Experiments show, in general, better scalability for the LITTLE cores for most of the applications except for one, which requires the computing performance that the big cores offer.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis, classification and comparison of scheduling techniques for software transactional memories

Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated

HeTM: Transactional Memory for Heterogeneous Systems

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. Unfortunately, though, developing applications that can take full advantage of the potential of heterogeneous systems is a notoriously hard task. This work takes a step towards reducing the complexity of programming heterogeneous systems by introducing the abstraction of Heterogeneous Transactional Memory (HeTM). HeTM provides programmers with the illusion of a single memory region, shared among the CPUs and the (discrete) GPU(s) of a heterogeneous system, with support for atomic transactions. Besides introducing the abstract semantics and programming model of HeTM, we present the design and evaluation of a concrete implementation of the proposed abstraction, which we named Speculative HeTM (SHeTM). SHeTM makes use of a novel design that leverages on speculative techniques and aims at hiding the inherently large communication latency between CPUs and discrete GPUs and at minimizing inter-device synchronization overhead. SHeTM is based on a modular and extensible design that allows for easily integrating alternative TM implementations on the CPU's and GPU's sides, which allows the flexibility to adopt, on either side, the TM implementation (e.g., in hardware or software) that best fits the applications' workload and the architectural characteristics of the processing unit. We demonstrate the efficiency of the SHeTM via an extensive quantitative study based both on synthetic benchmarks and on a porting of a popular object caching system.Comment: The current work was accepted in the 28th International Conference on Parallel Architectures and Compilation Techniques (PACT'19

Prompt Application-Transparent Transaction Revalidation in Software Transactional Memory

Software Transactional Memory (STM) allows encapsulating shared-data accesses within transactions, executed with atomicity and isolation guarantees. The assessment of the consistency of a running transaction is performed by the STM layer at specific points of its execution, such as when a read or write access to a shared object occurs, or upon a commit attempt. However, performance and energy efficiency issues may arise when no shared-data read/write operation occurs for a while along a thread running a transaction. In this scenario, the STM layer may not regain control for a considerable amount of time, thus not being able to early detect if such transaction has become inconsistent in the meantime. To tackle this problem we present an STM architecture that, thanks to a lightweight operating system support, is able to perform a fine-grain periodic (hence prompt) revalidation of running transactions. Our proposal targets Linux and x86 systems and has been integrated with the open source TinySTM package. Experimental results with a port of the TPC-C benchmark to STM environments show the effectiveness of our solution