Designing SSI clusters with hierarchical checkpointing and single I/O space

Adopting a new hierarchical checkpointing architecture, the authors develop a single I/O address space for building highly available clusters of computers. They propose a systematic approach to achieving a single system image by integrating existing middleware support with the newly developed features.published_or_final_versio

Extending and Implementing the Self-adaptive Virtual Processor for Distributed Memory Architectures

Many-core architectures of the future are likely to have distributed memory organizations and need fine grained concurrency management to be used effectively. The Self-adaptive Virtual Processor (SVP) is an abstract concurrent programming model which can provide this, but the model and its current implementations assume a single address space shared memory. We investigate and extend SVP to handle distributed environments, and discuss a prototype SVP implementation which transparently supports execution on heterogeneous distributed memory clusters over TCP/IP connections, while retaining the original SVP programming model

Effective Monte Carlo simulation on System-V massively parallel associative string processing architecture

We show that the latest version of massively parallel processing associative string processing architecture (System-V) is applicable for fast Monte Carlo simulation if an effective on-processor random number generator is implemented. Our lagged Fibonacci generator can produce $10^8$ random numbers on a processor string of 12K PE-s. The time dependent Monte Carlo algorithm of the one-dimensional non-equilibrium kinetic Ising model performs 80 faster than the corresponding serial algorithm on a 300 MHz UltraSparc.Comment: 8 pages, 9 color ps figures embedde

Virtual Runtime Application Partitions for Resource Management in Massively Parallel Architectures

This thesis presents a novel design paradigm, called Virtual Runtime Application Partitions (VRAP), to judiciously utilize the on-chip resources. As the dark silicon era approaches, where the power considerations will allow only a fraction chip to be powered on, judicious resource management will become a key consideration in future designs. Most of the works on resource management treat only the physical components (i.e. computation, communication, and memory blocks) as resources and manipulate the component to application mapping to optimize various parameters (e.g. energy efficiency). To further enhance the optimization potential, in addition to the physical resources we propose to manipulate abstract resources (i.e. voltage/frequency operating point, the fault-tolerance strength, the degree of parallelism, and the configuration architecture). The proposed framework (i.e. VRAP) encapsulates methods, algorithms, and hardware blocks to provide each application with the abstract resources tailored to its needs. To test the efficacy of this concept, we have developed three distinct self adaptive environments: (i) Private Operating Environment (POE), (ii) Private Reliability Environment (PRE), and (iii) Private Configuration Environment (PCE) that collectively ensure that each application meets its deadlines using minimal platform resources. In this work several novel architectural enhancements, algorithms and policies are presented to realize the virtual runtime application partitions efficiently. Considering the future design trends, we have chosen Coarse Grained Reconfigurable Architectures (CGRAs) and Network on Chips (NoCs) to test the feasibility of our approach. Specifically, we have chosen Dynamically Reconfigurable Resource Array (DRRA) and McNoC as the representative CGRA and NoC platforms. The proposed techniques are compared and evaluated using a variety of quantitative experiments. Synthesis and simulation results demonstrate VRAP significantly enhances the energy and power efficiency compared to state of the art.Siirretty Doriast

Generic Distribution Support for Programming Systems

This dissertation provides constructive proof, through the implementation of a middleware, that distribution transparency is practical, generic, and extensible. Fault tolerant distributed services can be developed by using the failure detection abilities of the middleware. By generic we mean that the middleware can be used for many different programming languages and paradigms. Distribution for each kind of language entity is done in terms of consistency protocols, which guarantee that the semantics of the entities are preserved in a distributed setting. The middleware allows new consistency protocols to be added easily. The efficiency of the middleware and the ease of integration are shown by coupling the middleware to a programming system, which encompasses the object oriented, the functional, and the concurrent-declarative programming paradigms. Our measurements show that the distribution middleware is competitive with the most popular distributed programming systems (JavaRMI, .NET, IBM CORBA)

Methods and architectures based on modular redundancy for fault-tolerant combinational circuits

Dans cette thèse, nous nous intéressons à la recherche d architectures fiables pour les circuits logiques. Par fiable , nous entendons des architectures permettant le masquage des fautes et les rendant de ce fait tolérantes" à ces fautes. Les solutions pour la tolérance aux fautes sont basées sur la redondance, d où le surcoût qui y est associé. La redondance peut être mise en oeuvre de différentes manières : statique ou dynamique, spatiale ou temporelle. Nous menons cette recherche en essayant de minimiser tant que possible le surcoût matériel engendré par le mécanisme de tolérance aux fautes. Le travail porte principalement sur les solutions de redondance modulaire, mais certaines études développées sont beaucoup plus générales.In this thesis, we mainly take into account the representative technique Triple Module Redundancy (TMR) as the reliability improvement technique. A voter is an necessary element in this kind of fault-tolerant architectures. The importance of reliability in majority voter is due to its application in both conventional fault-tolerant design and novel nanoelectronic systems. The property of a voter is therefore a bottleneck since it directly determines the whole performance of a redundant fault-tolerant digital IP (such as a TMR configuration). Obviously, the efficacy of TMR is to increase the reliability of digital IP. However, TMR sometimes could result in worse reliability than a simplex function module could. A better understanding of functional and signal reliability characteristics of a 3-input majority voter (majority voting in TMR) is studied. We analyze them by utilizing signal probability and boolean difference. It is well known that the acquisition of output signal probabilities is much easier compared with the obtention of output reliability. The results derived in this thesis proclaim the signal probability requirements for inputs of majority voter, and thereby reveal the conditions that TMR technique requires. This study shows the critical importance of error characteristics of majority voter, as used in fault-tolerant designs. As the flawlessness of majority voter in TMR is not true, we also proposed a fault-tolerant and simple 2-level majority voter structure for TMR. This alternative architecture for majority voter is useful in TMR schemes. The proposed solution is robust to single fault and exceeds those previous ones in terms of reliability.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Distributed Shared Memory: A Survey of Issues and Algorithms

26 pagesA distributed shared memory (DSM) is an implementation of the shared memory abstraction on a multicomputer architecture which has no physically shared memory. Shared memory is important (as a programming model) not only because of the vast number of existing applications which use it, but also because it is a more appropriate paradigm for certain algorithms. The DSM concept was demonstrated to be viable by Li, in IVY. Recently, there has been a surge of new projects which implement DSM in a variety of software and hardware environments. This paper gives an integrated overview of distributed shared memory. We discuss theoretical lower bounds on the performance of DSM systems, design choices such as structure and granularity, access, coherence semantics, scalability, and heterogeneity, and open problems in DSM. In addition, we describe algorithms used to implement and improve efficiency: reducing thrashing, eliminating false sharing, matching the coherence protocol to the type of sharing, and relaxing the semantics of the memory coherence provided. A spectrum of current DSM systems are used as illustrative examples

Stateful data-parallel processing

Democratisation of data means that more people than ever are involved in the data analysis process. This is beneficial—it brings domain-specific knowledge from broad fields—but data scientists do not have adequate tools to write algorithms and execute them at scale. Processing models of current data-parallel processing systems, designed for scalability and fault tolerance, are stateless. Stateless processing facilitates capturing parallelisation opportunities and hides fault tolerance. However, data scientists want to write stateful programs—with explicit state that they can update, such as matrices in machine learning algorithms—and are used to imperative-style languages. These programs struggle to execute with high-performance in stateless data-parallel systems. Representing state explicitly makes data-parallel processing at scale challenging. To achieve scalability, state must be distributed and coordinated across machines. In the event of failures, state must be recovered to provide correct results. We introduce stateful data-parallel processing that addresses the previous challenges by: (i) representing state as a first-class citizen so that a system can manipulate it; (ii) introducing two distributed mutable state abstractions for scalability; and (iii) an integrated approach to scale out and fault tolerance that recovers large state—spanning the memory of multiple machines. To support imperative-style programs a static analysis tool analyses Java programs that manipulate state and translates them to a representation that can execute on SEEP, an implementation of a stateful data-parallel processing model. SEEP is evaluated with stateful Big Data applications and shows comparable or better performance than state-of-the-art stateless systems.Open Acces