A profile-based tool for finding pipeline parallelism in sequential programs

Traditional static analysis fails to auto-parallelize programs with a complex control and data flow. Furthermore, thread-level parallelism in such programs is often restricted to pipeline parallelism, which can be hard to discover by a programmer. In this paper we propose a tool that, based on profiling information, helps the programmer to discover parallelism. The programmer hand-picks the code transformations from among the proposed candidates which are then applied by automatic code transformation techniques.

This paper contributes to the literature by presenting a profiling tool for discovering thread-level parallelism. We track dependencies at the whole-data structure level rather than at the element level or byte level in order to limit the profiling overhead. We perform a thorough analysis of the needs and costs of this technique. Furthermore, we present and validate the belief that programs with complex control and data flow contain significant amounts of exploitable coarse-grain pipeline parallelism in the program’s outer loops. This observation validates our approach to whole-data structure dependencies. As state-of-the-art compilers focus on loops iterating over data structure members, this observation also explains why our approach finds coarse-grain pipeline parallelism in cases that have remained out of reach for state-of-the-art compilers. In cases where traditional compilation techniques do find parallelism, our approach allows to discover higher degrees of parallelism, allowing a 40% speedup over traditional compilation techniques. Moreover, we demonstrate real speedups on multiple hardware platforms.

Clustered indexing for branch predictors

As a result of resource limitations, state in branch predictors is frequently shared between uncorrelated branches. This interference can significantly limit prediction accuracy. In current predictor designs, the branches sharing prediction information are determined by their branch addresses and thus branch groups are arbitrarily chosen during compilation. This feasibility study explores a more analytic and systematic approach to classify branches into clusters with similar behavioral characteristics. We present several ways to incorporate this cluster information as an additional information source in branch predictors.

Standoff detection of weapons and contraband in the 100 GHz to 1 THz region

The techniques and technologies currently being investigated to detect weapons and contraband concealed on persons under clothing are reviewed. The basic phenomenology of the atmosphere and materials that must be understood in order to realize such a system are discussed. The component issues and architectural designs needed to realize systems are outlined. Some conclusions with respect to further technology developments are presented.

Free-space optoelectronic switching cores with MPLS for SANs over WDM ring networks

With increasing demands on storage devices in the modern communication environment, the storage area network (SAN) has evolved to provide a direct connection allowing these storage devices to be accessed efficiently. To optimize the performance of a SAN, a three-stage hybrid electronic/optical switching node architecture based on the concept of a MPLS label switching mechanism, aimed at serving as a multi-protocol label switching (MPLS) ingress label edge router (LER) for a SAN-enabled application, has been designed. New shutter-based free-space multi-channel optical switching cores are employed as the core switch fabric to solve the packet contention and switching path conflict problems. The system-level node architecture design constraints are evaluated through self-similar traffic sourced from real gigabit Ethernet network traces and storage systems. The extension performance of a SAN over a proposed WDM ring network, aimed at serving as an MPLS-enabled transport network, is also presented and demonstrated.

Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks

To optimize the performance of wireless networks, one needs to consider the impact of key factors such as interference from hidden nodes, the capture effect, the network density and network conditions (saturated versus non-saturated). In this research, our goal is to quantify the impact of these factors and to propose effective mechanisms and algorithms for throughput guarantees in multi-hop wireless networks. For this purpose, we have developed a model that takes into account all these key factors, based on which an admission control algorithm and an end-to-end available bandwidth estimation algorithm are proposed. Given the necessary network information and traffic demands as inputs, these algorithms are able to provide predictive control via an iterative approach. Evaluations using analytical comparison with simulations as well as existing research show that the proposed model and algorithms are accurate and effective.

Psychosocial factors in GP work:the effects of taking a GP position or leaving GP work

BACKGROUND: We examined the effects of leaving public sector general practitioner (GP) work and of taking a GP position on changes in work-related psychosocial factors, such as time pressure, patient-related stress, distress and work interference with family. In addition, we examined whether changes in time pressure and patient-related stress mediated the association of employment change with changes of distress and work interference with family. METHODS: Participants were 1705 Finnish physicians (60% women) who responded to surveys in 2006 and 2010. Analyses of covariance were conducted to examine the effect of employment change to outcome changes adjusted for gender, age and response format. Mediational effects were tested following the procedures outlined by Baron and Kenny. RESULTS: Employment change was significantly associated with all the outcomes. Leaving public sector GP work was associated with substantially decreased time pressure, patient-related stress, distress and work interference with family. In contrast, taking a position as a public sector GP was associated with an increase in these factors. Mediation tests suggested that the associations of employment change with distress change and work interference with family change were partially explained by the changes in time pressure and patient-related stress. CONCLUSIONS: Our results showed that leaving public sector GP work is associated with favourable outcomes, whereas taking a GP position in the public sector is associated with adverse effects. Primary health-care organizations should pay more attention to the working conditions of their GPs, in particular, to time pressure and patient-related stress.

Obfuscation: The Hidden Malware

A cyberwar exists between malware writers and antimalware researchers. At this war's heart rages a weapons race that originated in the 80s with the first computer virus. Obfuscation is one of the latest strategies to camouflage the telltale signs of malware, undermine antimalware software, and thwart malware analysis. Malware writers use packers, polymorphic techniques, and metamorphic techniques to evade intrusion detection systems. The need exists for new antimalware approaches that focus on what malware is doing rather than how it's doing it.

Behavioural skeletons in GCM: autonomic management of grid components

Autonomic management can be used to improve the QoS provided by parallel/distributed applications. We discuss behavioural skeletons introduced in earlier work: rather than relying on programmer ability to design “from scratch” efficient autonomic policies, we encapsulate general autonomic controller features into algorithmic skeletons. Then we leave to the programmer the duty of specifying the parameters needed to specialise the skeletons to the needs of the particular application at hand. This results in the programmer having the ability to fast prototype and tune distributed/parallel applications with non-trivial autonomic management capabilities. We discuss how behavioural skeletons have been implemented in the framework of GCM(the Grid ComponentModel developed within the CoreGRID NoE and currently being implemented within the GridCOMP STREP project). We present results evaluating the overhead introduced by autonomic management activities as well as the overall behaviour of the skeletons. We also present results achieved with a long running application subject to autonomic management and dynamically adapting to changing features of the target architecture.
Overall the results demonstrate both the feasibility of implementing autonomic control via behavioural skeletons and the effectiveness of our sample behavioural skeletons in managing the “functional replication” pattern(s).

Regularized Online Mixture of Gaussians for Background Subtraction

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .

Introducing Automated Management Through Iteratively Increased Automation and Indicators

Introducing automation into a managed environment includes significant initial overhead and abstraction, creating a disconnect between the administrator and the system. In order to facilitate the transition to automated management, this paper proposes an approach whereby automation increases gradually, gathering data from the task deployment process. This stored data is analysed to determine the task outcome status and can then be used for comparison against future deployments of the same task and alerting the administrator to deviations from the expected outcome. Using a machinelearning
approach, the automation tool can learn from the administrator's reaction to task failures and eventually react to faults autonomously.