Variation among nonclinical subjects on a line-bisection task

Absence of leftward bias in a line bisection task is often used as a clinical hemispheric indicator, but the effect is not uniform in a normal population. Sex, handedness, and strategy variables affect the strength and direction of any bias.

Dual task performance of working memory and postural control in major depressive disorder

Objective: Previous studies with patients diagnosed with Major Depressive Disorder (MDD) revealed deficits in working memory and executive functions. In the present study we investigated whether patients with MDD have the ability to allocate cognitive resources in dual task performance of a highly challenging cognitive task (working memory) and a task that is seemingly automatic in nature (postural control). Method: Fifteen young (18–35 years old) patients with MDD and 24 healthy age-matched controls performed a working memory task and two postural control tasks (standing on a stable or on a moving platform) both separately (single task) and concurrently (dual task). Results: Postural stability under single task conditions was similar in the two groups, and in line with earlier studies, MDD patients recalled fewer working memory items than controls. To equate working memory challenges for patients and controls, task difficulty (number of items presented) in dual task was individually adjusted such that accuracy of working memory performance was similar for the two groups under single task conditions. Patients showed greater postural instability in dual task performance on the stable platform, and more importantly when posture task difficulty increased (moving platform) they showed deficits in both working memory accuracy and postural stability compared with healthy controls. Conclusions: We interpret our results as evidence for executive control deficits in MDD patients that affect their task coordination. In multitasking, these deficits affect not only cognitive but also sensorimotor task performance.

Parallel Programming of General-Purpose Programs Using Task-Based Programming Models

The prevalence of multicore processors is bound to drive most kinds of software development towards parallel programming. To limit the difficulty and overhead of parallel software design and maintenance, it is crucial that parallel programming models allow an easy-to-understand, concise and dense representation of parallelism. Parallel programming models such as Cilk++ and Intel TBBs attempt to offer a better, higher-level abstraction for parallel programming than threads and locking synchronization. It is not straightforward, however, to express all patterns of parallelism in these models. Pipelines are an important parallel construct, although difficult to express in Cilk and TBBs in a straightfor- ward way, not without a verbose restructuring of the code. In this paper we demonstrate that pipeline parallelism can be easily and concisely expressed in a Cilk-like language, which we extend with input, output and input/output dependency types on procedure arguments, enforced at runtime by the scheduler. We evaluate our implementation on real applications and show that our Cilk-like scheduler, extended to track and enforce these dependencies has performance comparable to Cilk++.

Acoustic Interculturalism: Listening to Performance

Acoustic Interculturalism is a study of the soundscapes of intercultural performance through the examination of sound's performativity. Employing an interdisciplinary approach, the book examines an akoumenological reception of sound to postulate the need for an acoustic knowing – an awareness of how sound shapes the intercultural experience.

Cognitive and task-related EEG correlates of arithmetic performance in adolescents

Cognitive and neurophysiological correlates of arithmetic calculation, concepts, and applications were examined in 41 adolescents, ages 12-15 years. Psychological and task-related EEG measures which correctly distinguished children who scored low vs. high (using a median split) in each arithmetic subarea were interpreted as indicative of processes involved. Calculation was related to visual-motor sequencing, spatial visualization, theta activity measured during visual-perceptual and verbal tasks at right- and left-hemisphere locations, and right-hemisphere alpha activity measured during a verbal task. Performance on arithmetic word problems was related to spatial visualization and perception, vocabulary, and right-hemisphere alpha activity measured during a verbal task. Results suggest a complex interplay of spatial and sequential operations in arithmetic performance, consistent with processing model concepts of lateralized brain function.

Prefetching and Cache Management using Task Lifetimes

Task-based dataflow programming models and runtimes emerge as promising candidates for programming multicore and manycore architectures. These programming models analyze dynamically task dependencies at runtime and schedule independent tasks concurrently to the processing elements. In such models, cache locality, which is critical for performance, becomes more challenging in the presence of fine-grain tasks, and in architectures with many simple cores.

This paper presents a combined hardware-software approach to improve cache locality and offer better performance is terms of execution time and energy in the memory system. We propose the explicit bulk prefetcher (EBP) and epoch-based cache management (ECM) to help runtimes prefetch task data and guide the replacement decisions in caches. The runtimem software can use this hardware support to expose its internal knowledge about the tasks to the architecture and achieve more efficient task-based execution. Our combined scheme outperforms HW-only prefetchers and state-of-the-art replacement policies, improves performance by an average of 17%, generates on average 26% fewer L2 misses, and consumes on average 28% less energy in the components of the memory system.

A Unified Scheduler for Recursive and Task Dataflow Parallelism

Task dataflow languages simplify the specification of parallel programs by dynamically detecting and enforcing dependencies between tasks. These languages are, however, often restricted to a single level of parallelism. This language design is reflected in the runtime system, where a master thread explicitly generates a task graph and worker threads execute ready tasks and wake-up their dependents. Such an approach is incompatible with state-of-the-art schedulers such as the Cilk scheduler, that minimize the creation of idle tasks (work-first principle) and place all task creation and scheduling off the critical path. This paper proposes an extension to the Cilk scheduler in order to reconcile task dependencies with the work-first principle. We discuss the impact of task dependencies on the properties of the Cilk scheduler. Furthermore, we propose a low-overhead ticket-based technique for dependency tracking and enforcement at the object level. Our scheduler also supports renaming of objects in order to increase task-level parallelism. Renaming is implemented using versioned objects, a new type of hyper object. Experimental evaluation shows that the unified scheduler is as efficient as the Cilk scheduler when tasks have no dependencies. Moreover, the unified scheduler is more efficient than SMPSS, a particular implementation of a task dataflow language.