A strategy for reducing turnaround time in design optimization using a distributed computer system

There is a need to explore methods for reducing lengthly computer turnaround or clock time associated with engineering design problems. Different strategies can be employed to reduce this turnaround time. One strategy is to run validated analysis software on a network of existing smaller computers so that portions of the computation can be done in parallel. This paper focuses on the implementation of this method using two types of problems. The first type is a traditional structural design optimization problem, which is characterized by a simple data flow and a complicated analysis. The second type of problem uses an existing computer program designed to study multilevel optimization techniques. This problem is characterized by complicated data flow and a simple analysis. The paper shows that distributed computing can be a viable means for reducing computational turnaround time for engineering design problems that lend themselves to decomposition. Parallel computing can be accomplished with a minimal cost in terms of hardware and software

Defect Prediction on the Hardware Repository - A Case Study on the OpenRISC1000 Project

Software defect prediction is one of the most active research topics in the area of mining software engineering data. The software engineering data sources like the code repositories and the bug databases contain rich information about software development history. Mining these data can guide software developers for future development activities and help managers to improve the development process. Nowadays, the computer-engineering field has rapidly evolved from 1972 until present times to the modern chip design, which looks superficially and very much like software design. Hence, the main objective of this thesis is to check whether it would be possible to apply software defect prediction techniques on hardware repositories. In this thesis, we have applied various data mining methods (e.g., linear regression, logistic regression, random forests, and entropy) to predict the post-release bugs of OpenRISC 1000 projects. We have conducted two types of studies: classification (predicting buggy and non-buggy files) and ranking (predicting the buggiest files). In particular, the classification studies show promising results with an average precision and recall of up to 74% and 70% for projects written in Verilog and close to 100% for projects written in C

Climate Models: A Software Engineering Approach

Climate Simulation and Weather Forecasting are amongst the most representative examples of scientific software, which has evolved through- out the past sixty years. In this paper, a set of Global Climate Models (GCM) have been analysed from a Software Engineering perspective, analysing the composition of their internal structure and programming constructs which have been used in the building process. We have implemented a set of software metrics such as Cyclomatic Complexity, Lines of Code, Number of Fortran Obsolete Language Features, among others.We have followed a compiler like approach, collecting information based on traversing the Abstract Syntax Tree (AST). The obtained data can be used for different purposes at different stages of the software life cycle such as: maintenance tasks, parallelization, and optimization. The results suggest that some programming techniques used for building scientic software have fallen into disuse because they are now considered obsolete and error-prone. In addition, GCM's internal structure seems to evolve at a slower pace than programming techniques. The analysis methodology can be used to update and enhance the scientific software in order to make simpler other tasks such as optimization and parallelization for specic new hardware such as multi/many-core processors and co-processors, distributed memory parallel hardware, etc

CASES: A Smart, Compact GPS Software Receiver for Space Weather Monitoring

A real-time software-defined GPS receiver for the L1 C/A and L2C codes has been developed as a low-cost space weather instrument for monitoring ionospheric scintillation and total electron content. The so-called CASES receiver implements several novel processing techniques not previously published that make it well suited for space weather monitoring: (A) a differencing technique for eliminating local clock effects, (B) an advanced triggering mechanism for determining the onset of scintillation, (C) data buffering to permit observation of the prelude to scintillation, and (D) data-bit prediction and wipe-off for robust tracking. The receiver has been tested in a variety of benign and adverse signal conditions (e.g., severe ionospheric scintillation, both real and simulated); the results are presented here. The custom hardware platform on which the software runs is compact while remaining flexible and extensible. The CASES platform consists of a digital signal processor, an ARM microcontroller, and a custom-built narrow-band dualfrequency front end. Because the receiver is softwaredefined, it can be remotely reprogrammed via the internet or another communications link.Aerospace Engineering and Engineering Mechanic

GREEN COMPUTING FOR IOT – SOFTWARE APPROACH

More efficient usage of limited energy resources on embedded platforms, found in various IoT applications, is identified as a universal challenge in designing such devices and systems. Although many power management techniques for control and optimization of device power consumption have been introduced at the hardware and software level, only few of them are addressing device operation at the application level. In this paper, a software engineering approach for managing the operation of IoT edge devices is presented. This approach involves a set of the application-level software parameters that affect consumption of the IoT device and its real-time behavior. To investigate and illustrate the impact of the introduced parameters on the device performance and its energy footprint, we utilize a custom-built simulation environment. The simulation results obtained from analyzing simplified data producer-consumer configuration of IoT edge tier, under push-based communication model, confirm that careful tuning of the identified set of parameters can lead to more energy efficient IoT end-device operation