Mobile devices are increasingly expected to sup-port high-performance cyber-physical applications in small form factors, e.g., drones and rovers. However, the gap between hardware limitations of these devices and application requirements is still prohibitive – conflicting goals such as robust, accurate, and efficient execution must be managed carefully to achieve acceptable operation. In this paper, we explore the tradeoff between performance and efficiency in such cyber-physical systems, specifically with respect to localization (a core task for any mobile autonomous device). We perform a design space exploration (DSE) given a number of configurable parameters for both localization algorithm and platform layers. Given the configuration space, we formulate a cross-layer multi-objective optimization problem to explore the tradeoff between localization accuracy and power consumption. We then propose a predictive model for robust execution that can be used to determine desirable configurations at runtime in the face of environmental changes.

Average programmers struggle to solve performance problems in OpenMP programs with tasks and parallel for-loops. Existing performance analysis tools visualize OpenMP task performance from the runtime system's perspective where task execution is interleaved with other tasks in an unpredictable order. Problems with OpenMP parallel for-loops are similarly difficult to resolve since tools only visualize aggregate thread-level statistics such as load imbalance without zooming into a per-chunk granularity. The runtime system/threads oriented visualization provides poor support for understanding problems with task and chunk execution time, parallelism, and memory hierarchy utilization, forcing average programmers to rely on experts or use tedious trial-and-error tuning methods for performance. We present grain graphs, a new OpenMP performance analysis method that visualizes grains -- computation performed by a task or a parallel for-loop chunk instance -- and highlights problems such as low parallelism, work inflation and poor parallelization benefit at the grain level. We demonstrate that grain graphs can quickly reveal performance problems that are difficult to detect and characterize in fine detail using existing visualizations in standard OpenMP programs, simplifying OpenMP performance analysis. This enables average programmers to make portable optimizations for poor performing OpenMP programs, reducing pressure on experts and removing the need for tedious trial-and-error tuning.

Task scheduling is crucial for the performance of parallel applications. Given dependence constraints between tasks, their arbitrary sizes, and bounded resources available for execution, optimal task scheduling is considered as an NP-hard problem. Therefore, proposed scheduling algorithms are based on heuristics. This paper1 presents a novel heuristic algorithm, called the Noodle heuristic, which differs from the existing list scheduling techniques in the way it assigns task priorities. We conduct an extensive experimental to validate Noodle for task graphs taken from Standard Task Graph (STG). Results show that Noodle produces schedules that are within a maximum of 12% (in worst-case) of the optimal schedule for 2, 4, and 8 core systems. We also compare Noodle with existing scheduling heuristics and perform comparative analysis of its performance.