Fast and efficient commits for Lazy-Lazy hardware transactional memory

Ingeniería, Industria y Construcció

Transactional memory on heterogeneous architectures

Tesis Leida el 9 de Marzo de 2018.Si observamos las necesidades computacionales de hoy, y tratamos de predecir las necesidades del mañana, podemos concluir que el procesamiento heterogéneo estará presente en muchos dispositivos y aplicaciones. El motivo es lógico: algoritmos diferentes y datos de naturaleza diferente encajan mejor en unos dispositivos de cómputo que en otros. Pongamos como ejemplo una tecnología de vanguardia como son los vehículos inteligentes. En este tipo de aplicaciones la computación heterogénea no es una opción, sino un requisito. En este tipo de vehículos se recolectan y analizan imágenes, tarea para la cual los procesadores gráficos (GPUs) son muy eficientes. Muchos de estos vehículos utilizan algoritmos sencillos, pero con grandes requerimientos de tiempo real, que deben implementarse directamente en hardware utilizando FPGAs. Y, por supuesto, los procesadores multinúcleo tienen un papel fundamental en estos sistemas, tanto organizando el trabajo de otros coprocesadores como ejecutando tareas en las que ningún otro procesador es más eficiente. No obstante, los procesadores tampoco siguen siendo dispositivos homogéneos. Los diferentes núcleos de un procesador pueden ofrecer diferentes características en términos de potencia y consumo energético que se adapten a las necesidades de cómputo de la aplicación. Programar este conjunto de dispositivos es una tarea compleja, especialmente en su sincronización. Habitualmente, esta sincronización se basa en operaciones atómicas, ejecución y terminación de kernels, barreras y señales. Con estas primitivas de sincronización básicas se pueden construir otras estructuras más complejas. Sin embargo, la programación de estos mecanismos es tediosa y propensa a fallos. La memoria transaccional (TM por sus siglas en inglés) se ha propuesto como un mecanismo avanzado a la vez que simple para garantizar la exclusión mutua

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Compilation Optimizations to Enhance Resilience of Big Data Programs and Quantum Processors

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), exist, though they can be difficult to implement, expensive, and may be limited to only protecting against errors in specific locations. Researchers have been exploring software detection and correction methods as an alternative, commonly trading either overhead in execution time or memory usage to protect against faults. Quantum computers, a relatively recent advancement in computing technology, experience similar errors on a much more severe scale. The errors are more frequent, costly, and difficult to detect and correct. Error correction algorithms like Shor’s code promise to completely remove errors, but they cannot be implemented on current noisy intermediate-scale quantum (NISQ) systems due to the low number of available qubits. Until the physical systems become large enough to support error correction, researchers instead have been studying other methods to reduce and compensate for errors. In this work, we present two methods for improving the resilience of classical processes, both single- and multi-threaded. We then introduce quantum computing and compare the nature of errors and correction methods to previous classical methods. We further discuss two designs for improving compilation of quantum circuits. One method, focused on quantum neural networks (QNNs), takes advantage of partial compilation to avoid recompiling the entire circuit each time. The other method is a new approach to compiling quantum circuits using graph neural networks (GNNs) to improve the resilience of quantum circuits and increase fidelity. By using GNNs with reinforcement learning, we can train a compiler to provide improved qubit allocation that improves the success rate of quantum circuits

Strong Memory Consistency For Parallel Programming

Correctly synchronizing multithreaded programs is challenging, and errors can lead to program failures (e.g., atomicity violations). Existing memory consistency models rule out some possible failures, but are limited by depending on subtle programmer-defined locking code and by providing unintuitive semantics for incorrectly synchronized code. Stronger memory consistency models assist programmers by providing them with easier-to-understand semantics with regard to memory access interleavings in parallel code. This dissertation proposes a new strong memory consistency model based on ordering-free regions (OFRs), which are spans of dynamic instructions between consecutive ordering constructs (e.g. barriers). Atomicity over ordering-free regions provides stronger atomicity than existing strong memory consistency models with competitive performance. Ordering-free regions also simplify programmer reasoning by limiting the potential for atomicity violations to fewer points in the program’s execution. This dissertation explores both software-only and hardware-supported systems that provide OFR serializability

Concurrency in Blockchain Based Smartpool with Transactional Memory

Blockchain is the buzzword in today\u27s modern technological world. It is an undeniably ingenious invention of the 21st century. Blockchain was first coined and used by a cryptocurrency namedBitcoin. Since then bitcoin and blockchain are so popular that every single person is taking on bitcoin these days and the price of bitcoin has leaped to a staggering price in the last year and so.Today several other cryptocurrencies have adapted the blockchain technology. Blockchain in cryptocurrencies is formed by chaining of blocks. These blocks are created by the nodes called miners through the process called Proof of Work(PoW). Mining Pools are formed as a collection of miners which collectively tries to solve a puzzle. However, most of the mining pools are centralized. P2Pool is the first decentralized mining pool in Bitcoin but is not that popular as the number of messages exchanged among the miners is a scalar multiple of the number of shares. SmartPool is a decentralized mining pool with the throughput equal to that of the traditional pool. However, the verification of blocks is done in a sequential manner. We propose a non-blocking concurrency mechanism in a decentralized mining pool for the verification of blocks in a blockchain. Smart contract in SmartPool is concurrently executed using a transactional memory approach without the use of locks. Since the SmartPool mining implemented in ethereum can be applied to Bitcoin, this concurrency method proposed in ethereum smart contracts can be applicable in Bitcoin as well

Blockchain for secured IoT and D2D applications over 5G cellular networks : a thesis by publications presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Author's Declaration: "In accordance with Sensors, SpringerOpen, and IEEE’s copyright policy, this thesis contains the accepted and published version of each manuscript as the final version. Consequently, the content is identical to the published versions."The Internet of things (IoT) is in continuous development with ever-growing popularity. It brings significant benefits through enabling humans and the physical world to interact using various technologies from small sensors to cloud computing. IoT devices and networks are appealing targets of various cyber attacks and can be hampered by malicious intervening attackers if the IoT is not appropriately protected. However, IoT security and privacy remain a major challenge due to characteristics of the IoT, such as heterogeneity, scalability, nature of the data, and operation in open environments. Moreover, many existing cloud-based solutions for IoT security rely on central remote servers over vulnerable Internet connections. The decentralized and distributed nature of blockchain technology has attracted significant attention as a suitable solution to tackle the security and privacy concerns of the IoT and device-to-device (D2D) communication. This thesis explores the possible adoption of blockchain technology to address the security and privacy challenges of the IoT under the 5G cellular system. This thesis makes four novel contributions. First, a Multi-layer Blockchain Security (MBS) model is proposed to protect IoT networks while simplifying the implementation of blockchain technology. The concept of clustering is utilized to facilitate multi-layer architecture deployment and increase scalability. The K-unknown clusters are formed within the IoT network by applying a hybrid Evolutionary Computation Algorithm using Simulated Annealing (SA) and Genetic Algorithms (GA) to structure the overlay nodes. The open-source Hyperledger Fabric (HLF) Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The quantitative arguments demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported methods. The proposed lightweight blockchain model is also better suited to balance network latency and throughput compared to a traditional global blockchain. Next, a model is proposed to integrate IoT systems and blockchain by implementing the permissioned blockchain Hyperledger Fabric. The security of the edge computing devices is provided by employing a local authentication process. A lightweight mutual authentication and authorization solution is proposed to ensure the security of tiny IoT devices within the ecosystem. In addition, the proposed model provides traceability for the data generated by the IoT devices. The performance of the proposed model is validated with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results indicate that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios. Despite the increasing development of blockchain platforms, there is still no comprehensive method for adopting blockchain technology on IoT systems due to the blockchain's limited capability to process substantial transaction requests from a massive number of IoT devices. The Fabric comprises various components such as smart contracts, peers, endorsers, validators, committers, and Orderers. A comprehensive empirical model is proposed that measures HLF's performance and identifies potential performance bottlenecks to better meet blockchain-based IoT applications' requirements. The implementation of HLF on distributed large-scale IoT systems is proposed. The performance of the HLF is evaluated in terms of throughput, latency, network sizes, scalability, and the number of peers serviceable by the platform. The experimental results demonstrate that the proposed framework can provide a detailed and real-time performance evaluation of blockchain systems for large-scale IoT applications. The diversity and the sheer increase in the number of connected IoT devices have brought significant concerns about storing and protecting the large IoT data volume. Dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. A layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system is proposed to mitigate these challenges by using the HLF platform for distributed ledger solutions. The need for a centralized server and third-party auditor is eliminated by leveraging HLF peers who perform transaction verification and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata is stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of throughput, latency, communication, and computation costs. The results indicate the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the big data ecosystem using the HLF blockchain

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Digital Twins and Blockchain for IoT Management

Security and privacy are primary concerns in IoT management. Security breaches in IoT resources, such as smart sensors, can leak sensitive data and compromise the privacy of individuals. Effective IoT management requires a comprehensive approach to prioritize access security and data privacy protection. Digital twins create virtual representations of IoT resources. Blockchain adds decentralization, transparency, and reliability to IoT systems. This research integrates digital twins and blockchain to manage access to IoT data streaming. Digital twins are used to encapsulate data access and view configurations. Access is enabled on digital twins, not on IoT resources directly. Trust structures programmed as smart contracts are the ones that manage access to digital twins. Consequently, IoT resources are not exposed to third parties, and access security breaches can be prevented. Blockchain has been used to validate digital twins and store their configuration. The research presented in this paper enables multitenant access and customization of data streaming views and abstracts the complexity of data access management. This approach provides access and configuration security and data privacy protection.Comment: Reference: Mayra, Samaniego and Ralph, Deters. 2023. Digital Twins and Blockchain for IoT Management. In The 5th ACM International Symposium on Blockchain and Secure Critical Infrastructure (BSCI '23), July 10-14, 2023, Melbourne, VIC, Australia. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3594556.359461

Software Transactional Memory Building Blocks

Exploiting thread-level parallelism has become a part of mainstream programming in recent years. Many approaches to parallelization require threads executing in parallel to also synchronize occassionally (i.e., coordinate concurrent accesses to shared state). Transactional Memory (TM) is a programming abstraction that provides the concept of database transactions in the context of programming languages such as C/C++. This allows programmers to only declare which pieces of a program synchronize without requiring them to actually implement synchronization and tune its performance, which in turn makes TM typically easier to use than other abstractions such as locks. I have investigated and implemented the building blocks that are required for a high-performance, practical, and realistic TM. They host several novel algorithms and optimizations for TM implementations, both for current hardware and future hardware extensions for TM, and are being used in or have influenced commercial TM implementations such as the TM support in GCC