Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing

Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal

A fine-grain time-sharing Time Warp system

Although Parallel Discrete Event Simulation (PDES) platforms relying on the Time Warp (optimistic) synchronization protocol already allow for exploiting parallelism, several techniques have been proposed to further favor performance. Among them we can mention optimized approaches for state restore, as well as techniques for load balancing or (dynamically) controlling the speculation degree, the latter being specifically targeted at reducing the incidence of causality errors leading to waste of computation. However, in state of the art Time Warp systems, events’ processing is not preemptable, which may prevent the possibility to promptly react to the injection of higher priority (say lower timestamp) events. Delaying the processing of these events may, in turn, give rise to higher incidence of incorrect speculation. In this article we present the design and realization of a fine-grain time-sharing Time Warp system, to be run on multi-core Linux machines, which makes systematic use of event preemption in order to dynamically reassign the CPU to higher priority events/tasks. Our proposal is based on a truly dual mode execution, application vs platform, which includes a timer-interrupt based support for bringing control back to platform mode for possible CPU reassignment according to very fine grain periods. The latter facility is offered by an ad-hoc timer-interrupt management module for Linux, which we release, together with the overall time-sharing support, within the open source ROOT-Sim platform. An experimental assessment based on the classical PHOLD benchmark and two real world models is presented, which shows how our proposal effectively leads to the reduction of the incidence of causality errors, as compared to traditional Time Warp, especially when running with higher degrees of parallelism

Performance models of concurrency control protocols for transaction processing systems

Transaction processing plays a key role in a lot of IT infrastructures. It is widely used in a variety of contexts, spanning from database management systems to concurrent programming tools. Transaction processing systems leverage on concurrency control protocols, which allow them to concurrently process transactions preserving essential properties, as isolation and atomicity. Performance is a critical aspect of transaction processing systems, and it is unavoidably affected by the concurrency control. For this reason, methods and techniques to assess and predict the performance of concurrency control protocols are of interest for many IT players, including application designers, developers and system administrators. The analysis and the proper understanding of the impact on the system performance of these protocols require quantitative approaches. Analytical modeling is a practical approach for building cost-effective computer system performance models, enabling us to quantitatively describe the complex dynamics characterizing these systems. In this dissertation we present analytical performance models of concurrency control protocols. We deal with both traditional transaction processing systems, such as database management systems, and emerging ones, as transactional memories. The analysis focuses on widely used protocols, providing detailed performance models and validation studies. In addition, we propose new modeling approaches, which also broaden the scope of our study towards a more realistic, application-oriented, performance analysis

Adaptive thread scheduling techniques for improving scalability of software transactional memory

Software transactional memory (STM) enhances both ease-of-use and concurrency, and is considered state-of-the-art for parallel applications to scale on modern multi-core hardware. However, there are certain situations where STM performs even worse than traditional locks. Upon hotspots where most threads contend over a few pieces of shared data, going transactional will result in excessive conflicts and aborts that adversely degrade performance. We present a new design of adaptive thread scheduler that manages concurrency when the system is about entering and leaving hotspots. The scheduler controls the number of threads spawning new transactions according to the live commit throughput. We implemented two feedback-control policies called Throttle and Probe to realize this adaptive scheduling. Performance evaluation with the STAMP benchmarks shows that enabling Throttle and Probe obtain best-case speedups of 87.5% and 108.7% respectively.postprintThe 10th IASTED International Conference on Parallel and Distributed Computing and Networks (PDCN 2011), Innsbruck, Austria, 15-17 February 2011. In Proceedings of the 10th IASTED-PDCN, 2011, p. 91-9

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system

Evaluation of Hadoop/Mapreduce Framework Migration Tools

In distributed systems, database migration is not an easy task. Companies will encounter challenges moving data including legacy data to the big data platform. This paper reviews some tools for migrating from traditional databases to the big data platform and thus suggests a model, based on the review

Performance Optimization Strategies for Transactional Memory Applications

This thesis presents tools for Transactional Memory (TM) applications that cover multiple TM systems (Software, Hardware, and hybrid TM) and use information of all different layers of the TM software stack. Therefore, this thesis addresses a number of challenges to extract static information, information about the run time behavior, and expert-level knowledge to develop these new methods and strategies for the optimization of TM applications

Estimating resource bounds for software transactions

We present an effect based static analysis to calculate upper and lower bounds on the memory resource consumption in a transactional calculus. The calculus is a concurrent variant of Featherweight Java extended by transactional constructs. The model supports nested and concurrent transactions. The analysis is compositional and takes into account implicit join synchronizations that arise when more than one thread perfom a join-synchronization when jointly committing a transaction. Central for a compositional and precise analysis is to capture as part of the effects a tree-representation of the future resource consumption and synchronization points (which we call joining commit trees). We show the soundness of the analysis

ESTIMA: Extrapolating ScalabiliTy of In-Memory Applications

This paper presents ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. ESTIMA is designed to perform a simple, yet important task: given the performance of an application on a small machine with a handful of cores, ESTIMA extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying ESTIMA is the use of stalled cycles (e.g. cycles that the processor spends waiting for various events, such as cache misses or waiting on a lock). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. ESTIMA can be effectively used to predict the scalability of in-memory applications. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation on a large number of in-memory benchmarks shows that ESTIMA has generally low prediction errors