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

Boosting performance of transactional memory through transactional read tracking and set associative locks

Multi-core processors have become so prevalent in server, desktop, and even embedded systems that they are considered the norm for modem computing systems. The trend is likely toward many-core processors with many more than just 2, 4, or 8 cores per CPU. To benefit from the increasing number of cores per chip, application developers have to develop parallel programs [1]. Traditional lock-based programming is too difficult and error prone for most of programmers and is the domain of experts. Deadlock, race, and other synchronization bugs are some of the challenges of lock-based programming. To make parallel programming mainstream, it is necessary to adapt parallel programming by the majority of programmers and not just experts, and thus simplifying parallel programming has become an important challenge. Transactional Memory (TM) is a promising programming model for managing concurrent accesses to the shared memory locations. Transactional memory allows a programmer to specify a section of a code to be "'transactional", and the underlying system guarantees atomic execution of the code. This simplifies parallel programming and reduces the possibility of synchronization bugs. This thesis develops several software- and hardware-based techniques to improve performance of existing transactional memory systems. The first technique is Transactional Read Tracking (TRT). TRT is a software-based approach that employs a locking mechanism for transactional read and write operations. The performance of TRT depends on memory access patterns of applications. In some cases, TRT falls behind the baseline scheme. To further improve performance of TRT, we introduce two hybrid methods that dynamically switches between TRT and the baseline scheme based on applications’ behavior. The second optimization technique is Set Associative Lock (SAL). Memory locations are mapped to a lock table in order to synchronize accesses to the shared memory locations. Direct mapped lock tables usually result in collision which leads to false aborts. In SAL, we increase associativity of the lock table to reduce false abort. While SAL improves performance in most of the applications, in some cases, it increases execution time due to overhead of lock tables in software. To cope with this problem, we propose Hardware-SAL (HW-SAL) which moves the set associative lock table to the hardware. As such, true power of set associativity will be harnessed without sacrificing performance

Ab initio RNA folding

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, experimental determination of RNA structures through X-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure

Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process

Texture coefficient is one of the most influential parameters in rock engineering specifications in various projects including drilling, cutting, permeability of all-section drilling devices, etc. Meanwhile, investigating and forecasting the energy consumption of saw cutters are one of the most important factors in estimating the cutting costs. The present study aims to investigate the relationship between rock texture characteristics and the amount of energy consumption of the gang saw machine in the process of cutting carbonate rocks. To evaluate the effects of texture on the rocks' engineering specifications, 14 carbonate rock samples were studied. A microscopic thin section was made from each rock specimen. Then, five digital images were taken from each section under a microscope and the values of area, environment, the largest diameter and the smallest diameter of all grains in each image were determined. Using these specifications, the coefficient of texture of all rock samples was calculated and the relationship between the texture coefficient and the rate of energy consumption of the gang saw machine was investigated for the studied samples. The study results indicated that there was a significant relation between the texture coefficient and energy consumption rate in the three groups of carbonate rocks