Improving efficiency of persistent storage access in embedded Linux

Real-time embedded systems increasingly need to process and store large volumes of persistent data, requiring fast, timely and predictable storage. Traditional methods of accessing storage using general-purpose operating system-based file systems do not provide the performance and timing predictability needed. This paper firstly examines the speed and consistency of SSD operations in an embedded Linux system, identifying areas where inefficiencies in the storage stack cause issues for performance and predictability. Secondly, the CharIO storage device driver is proposed to bypass Linux file systems and the kernel block layer, in order to increase performance, and provide improved timing predictability

TimeWeaver: A Tool for Hybrid Worst-Case Execution Time Analysis

Many embedded control applications have real-time requirements. If the application is safety-relevant, worst-case execution time bounds have to be determined in order to demonstrate deadline adherence. For high-performance multi-core architectures with degraded timing predictability, WCET bounds can be computed by hybrid WCET analysis which combines static analysis with timing measurements. This article focuses on a novel tool for hybrid WCET analysis based on non-intrusive instruction-level real-time tracing

GPU Wavefront Splitting for Safety-Critical Systems

Graphics processing units (GPUs) are compute platforms that are ideal for highly parallel workloads due to their high degree of hardware parallelism. Parallelism offered by GPUs lends itself well to machine learning and computer vision applications, including in safety-critical systems. Safety-critical systems require a guarantee of timing predictability. Guaranteeing timing predictability means being able to statically analyze the worst-case execution time (WCET) of the GPU program. Unfortunately, existing GPUs are designed for average-case performance and are thus not designed for timing predictability. Consequently, there is potential for research effort to provide these guarantees. Prior research works have proposed several new techniques to improve performance. One such technique is wavefront splitting, which reduces the number of idle threads on the GPU and increase utilization. However, no prior work addresses the WCET of this technique. The purpose of this thesis is to develop a GPU implementation for safety-critical systems that leverages wavefront splitting and to enable analysis of the WCET in such an implementation

A memory-centric approach to enable timing-predictability within embedded many-core accelerators

There is an increasing interest among real-time systems architects for multi- and many-core accelerated platforms. The main obstacle towards the adoption of such devices within industrial settings is related to the difficulties in tightly estimating the multiple interferences that may arise among the parallel components of the system. This in particular concerns concurrent accesses to shared memory and communication resources. Existing worst-case execution time analyses are extremely pessimistic, especially when adopted for systems composed of hundreds-tothousands of cores. This significantly limits the potential for the adoption of these platforms in real-time systems. In this paper, we study how the predictable execution model (PREM), a memory-aware approach to enable timing-predictability in realtime systems, can be successfully adopted on multi- and manycore heterogeneous platforms. Using a state-of-the-art multi-core platform as a testbed, we validate that it is possible to obtain an order-of-magnitude improvement in the WCET bounds of parallel applications, if data movements are adequately orchestrated in accordance with PREM. We identify which system parameters mostly affect the tremendous performance opportunities offered by this approach, both on average and in the worst case, moving the first step towards predictable many-core systems

A focus group study on psychology students' experience of assessments in higher education

Assessments at Higher Education (HE) have several functions. Its role in motivating student learning is undoubtedly its most important role. Despite this very little research has been carried out to assess the student experience of assessments (Hernandez, 2012). The design of this study was a qualitative focus group study. It is a preliminary study as part of a larger study involving a total of three focus groups. The data was analyzed using experiential Thematic Analysis (TA), as outlined by Braun and Clarke (2013). There were six focus group undergraduate student participants, five female and one male. Students’ experience of assessments and the resultant learning were influenced by both student and teaching factors. Student factors include the themes Academic Maturity and Emotion. Teaching factors include the themes Timing, Predictability and Support. All of these themes effected student learning and were substantial to the student experience of assessments. Academic staff need to be aware that the timing of assessments, level of predictability and balance of support all affect student learning. Strategies to promote academic maturity and reduce stress and fear in students could foster a more constructive approach to learning

Mutual Fund Trades: Timing Sentiment and Managing Tracking Error Variance

We use portfolio holdings to show that mutual funds preferentially trade stocks according to the stocks‟ sentiment betas. Stocks with high sentiment betas are more responsive to investor sentiment and increase (decrease) in value as sentiment increases (decreases). Sentiment-based trades may be motivated by the opportunity to increase fund returns through timing predictability in sentiment, or by management of portfolio risk. Sentiment is mean-reverting, but its level and recent change only partially explain these trades. In contrast, 30 percent of sentiment-based trades are explained by the initial sentiment beta of funds that trade to reduce their tracking error variance