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

Neurocognitive profiles in autism spectrum disorder

textThe current research project examines the performance of a group of high functioning young adult males with autism spectrum disorders on standardized measures of neurocognitive functioning to determine whether distinct cognitive profiles of strengths and weaknesses emerge. Neuropsychological test data across various domains: general cognitive ability, visuospatial processing, verbal learning and memory, visual learning and memory, working memory, reasoning, cognitive flexibility, attention, receptive language, expressive language, social and emotional processing, and fine motor skills was examined. Data were analyzed using cluster analysis to assess for the presence and nature of unique clusters/subgroups based on neuropsychological test performance. Three unique clusters were derived from the analyses. This study highlights the well-documented heterogeneity across the spectrum of autism and suggests a method for parsing a heterogeneous sample of ASD subjects into smaller and more meaningful homogeneous groups using standardized neuropsychological assessments.Educational Psycholog

Model-driven Scheduling for Distributed Stream Processing Systems

Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application. In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior. Specifically, we use application performance models to offer reliable estimates of the resource allocation required. Further, this intuition also drives resource mapping, and helps narrow the estimated and actual dataflow performance and resource utilization. Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.Comment: 54 page

Dynamic and Leakage Power-Composition Profile Driven Co-Synthesis for Energy and Cost Reduction

Recent research has shown that combining dynamic voltage scaling (DVS) and adaptive body bias (ABB) techniques achieve the highest reduction in embedded systems energy dissipation [1]. In this paper we show that it is possible to produce comparable energy saving to that obtained using combined DVS and ABB techniques but with reduced hardware cost achieved by employing processing elements (PEs) with separate DVS or ABB capability. A co-synthesis methodology which is aware of tasks’ power-composition profile (the ratio of the dynamic power to the leakage power) is presented. The methodology selects voltage scaling capabilities (DVS, ABB, or combined DVS and ABB) for the PEs, maps, schedules, and voltage scales applications given as task graphs with timing constraints, aiming to dynamic and leakage energy reduction at low hardware cost. We conduct detailed experiments, including a real-life example, to demonstrate the effectiveness of our methodology. We demonstrate that it is possible to produce designs that contain PEs with only DVS or ABB technique but have energy dissipation that are only 4.4% higher when compared with the same designs that employ PEs with combined DVS and ABB capabilities

The conditional process model of mindfulness and emotion regulation: An empirical test

BACKGROUND: The conditional process model (CPM) of mindfulness and emotion regulation posits that specific mediators and moderators link these constructs to mental health outcomes. The current study empirically examined the central tenets of the CPM, which posit that nonreactivity moderates the indirect effect of observation on symptoms of emotional disorders through cognitive emotion regulation strategies. METHODS: A clinical sample (n=1667) of individuals from Japan completed a battery of self-report instruments. Several path analyses were conducted to determine whether cognitive emotion regulation strategies mediate the relationship between observation and symptoms of individual emotional disorders, and to determine whether nonreactivity moderated these indirect effects. RESULTS: Results provided support the CPM. Specifically, nonreactivity moderated the indirect effect of observation on symptoms through reappraisal, but it did not moderate the indirect effect of observation on symptoms through suppression. LIMITATIONS: Causal interpretations are limited, and cultural considerations must be acknowledged given the Japanese sample CONCLUSIONS: These results underscore the potential importance of nonreactivity and emotion regulation as targets for interventions.R01 AT007257 - NCCIH NIH HHS; R34 MH099311 - NIMH NIH HH