PAPA: A parallel programming assistant powered by IBM Watson cognitive computing technology

Yield gap and resource utilization efficiency of three major food crops in the world – A review

Due to the culture of sequential programming, the lack of tools and the inherent difficulties of parallel programming, programmers find hard to design and evaluate parallel real-time programs. As a result, a major problem found in the development of parallel real-time systems is the difficulty to predict the performance of the application, and the need to reduce rapid prototypes of the systems behind schedule. This work is part of a bigger project financed by FINEP, with the title Arquitetura Paralela Usando DSPs (Parallel Architecture Using DSPs), whose aim is to use a parallel system for processing and visualising tomography images from the solar atmosphere in real time. In this way, it can be possible to have the 3D reconstruction of the solar corona from the angular tomography, as well as the spectral lines. This paper presents a Visual Environment which should be able to provide continuity in the development of projects using the most common development methods, traditional or object-oriented, by offering support during the phases of implementation, debugging and testing. The environment has a visual interface and the main aim is to facilitate the development process and debugging of source code of applications developed for parallel kernels, particularly for Virtuoso (Virtual Single Processor Programming System – Virtuoso). The environment is composed by four tools: a Parallel Programs Generator, a Worst Case Execution Time Analyser, a Scheduling Analyser, and a Parallel Debugger.

We tested the hypothesis that introduced populations may have higher resource capture ability and utilization efficiency than native ones of invasive plants. We compared ecophysiological traits including maximum photosynthetic rate (Pmax), apparent quantum yield (Q), specific leaf area (SLA), photosynthetic energy use efficiency (PEUE), photosynthetic nitrogen use efficiency (PNUE), water use efficiency (WUE), mass-based and area-based leaf construction cost (CCmass and CCarea), and mass-based and area-based leaf nitrogen concentration (Nmass and Narea) between native (Argentina) and introduced (USA) populations of two varieties (North Apa and South Apo) of Alternanthera philoxeroides under common garden conditions in China. For Apo and Apa, Pmax, Q, Nmass and WUE were not significantly different between native and introduced populations; introduced populations had significantly lower SLA and lower CCmass but significantly higher Narea and CCarea than native ones. For Apa, the introduced populations showed significantly lower PEUE and lower PNUE while for Apo, PEUE and PNUE were not significantly different between native and introduced populations. The results indicated that introduced populations of A. philoxeroides do not show higher resource capture ability and resource utilization efficiency than their native ones in the common garden experiment, suggesting that these traits may not necessarily contribute to successful invasion of invasive plants.

For this special issue we have selected five papers that address, from several points of view, the problem of efficient utilization of resources in parallel and distributed systems. These papers were among the best papers presented at the IASTED PDCS 2001 conference. The topics covered include: efficient cache strategies for simultaneous execution of threads as well as for the distribution of video-on-demand, efficient communication and failure recovery, and run-time support for the automatic parallelization of dynamic structures

This paper proposes Pyne, a parallel programming framework for developing parallel simulations. Pyne is designed such that parallel applications are developed with sequential programming. Pyne provides a programming environment in which programmers select data structures, define the sequence of operations for the data, and implement some sequential functions to describe the detailed calculation rules for the operations. Pyne automatically creates parallel data structures and performs parallel executions. This paper discusses the programming interface and runtime optimization algorithms that enable the parallel programming model. We evaluated Pyne by implementing a fluid dynamics simulation with both Pyne and a state-of-the-art parallel library. The results show that Pyne succeeds at reducing the code size by 70%, while maintaining performance and scalability.

Shared memory programming and distributed memory programming, are the most prominent ways of parallelize applications requiring high processing times and large amounts of storage in High Performance Computing (HPC) systems; parallel applications can be represented by Parallel Task Graphs (PTG) using Directed Acyclic Graphs (DAGs). The scheduling of PTGs in HPCS is considered a NP-Complete combinatorial problem that requires large amounts of storage and long processing times. Heuristic methods and sequential programming languages have been proposed to address this problem. In the open access paper: Scheduling in Heterogeneous Distributed Computing Systems Based on Internal Structure of Parallel Tasks Graphs with Meta-Heuristics, the Array Method is presented, this method optimizes the use of Processing Elements (PE) in a HPCS and improves response times in scheduling and mapping resource with the use of the Univariate Marginal Distribution Algorithm (UMDA); Array Method uses the internal characteristics of PTGs to make task scheduling; this method was programmed in the C language in sequential form, analyzed and tested with the use of algorithms for the generation of synthetic workloads and DAGs of real applications. Considering the great benefits of parallel software, this research work presents the Array Method using parallel programming with OpenMP. The results of the experiments show an acceleration in the response times of parallel programming compared to sequential programming when evaluating three metrics: waiting time, makespan and quality of assignments.

This study is focused on time optimization for the classification problem presenting a comparison of five Artificial Neural Network Multilayer Perceptron (ANN-MLP) architectures. We use the Artificial Neural Network (ANN) because it allows to recognize patterns in data in a lower time rate. Time and classification accuracy are taken into account together for the comparison. According to time comparison, two paradigms in the computational field for each ANN-MLP architecture are analysed with three schemes. Firstly, sequential programming is applied by using a single CPU core. Secondly, parallel programming is employed over a multi-core CPU architecture. Finally, a programming model running on GPU architecture is implemented. Furthermore, the classification accuracy is compared between the proposed five ANN-MLP architectures and a state-of.the-art Support Vector Machine (SVM) with three classification frames: 50%,60% and 70% of the data set's observations are randomly selected to train the classifiers. Also, a visual comparison of the classified results is presented. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) criteria are also calculated to characterise visual perception. The images employed were acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the Reflective Optics System Imaging Spectrometer (ROSIS) and the Hyperion sensor.

The multi-directional variation of results among environmental regulation, resource utilization, and Research and Development (R&D) investments continue to gain more popularity but few studies have examined the nexus among these variables in an open economy. To analyze the individual and combined effects of environmental regulation and resource utilization on industrial R&D investments, a panel data of 30 provinces in China from 2008 to 2018 was used. We also examined the mechanism underlying the magnitude of pollution inducement on R&D investment. The results show that environmental regulation significantly and positively promotes R&D investment while resource utilization has an inverse effect. Environmental regulation does not interfere with efficiency and neither does the quest for efficiency in resource utilization weaken the impact of environmental regulation on R&D investment. Only a few provinces are within the rising stages of the inverted n-shaped curve indicating environmental regulation level is still low. Even though there are tighter laws, government needs to continue to put in more stringent environmental policies towards achieving sustainable development.

Nowadays, a typical processor may have multiple processing cores on a single chip. Furthermore, a special purpose processing unit called Graphic Processing Unit (GPU), originally designed for 2D/3D games, is now available for general purpose use in computers and mobile devices. However, the traditional programming languages which were designed to work with machines having single core CPUs, cannot utilize the parallelism available on multi-core processors efficiently. Therefore, to exploit the extraordinary processing power of multi-core processors, researchers are working on new tools and techniques to facilitate parallel programming. To this end, languages like CUDA and OpenCL have been introduced, which can be used to write code with parallelism. The main shortcoming of these languages is that programmer needs to specify all the complex details manually in order to parallelize the code across multiple cores. Therefore, the code written in these languages is difficult to understand, debug and maintain. Furthermore, to parallelize legacy code can require rewriting a significant portion of code in CUDA or OpenCL, which can consume significant time and resources. Thus, the amount of parallelism achieved is proportional to the skills of the programmer and the time spent in code optimizations. This paper proposes a new open source compiler, Rubus, to achieve seamless parallelism. The Rubus compiler relieves the programmer from manually specifying the low-level details. It analyses and transforms a sequential program into a parallel program automatically, without any user intervention. This achieves massive speedup and better utilization of the underlying hardware without a programmer’s expertise in parallel programming. For five different benchmarks, on average a speedup of 34.54 times has been achieved by Rubus as compared to Java on a basic GPU having only 96 cores. Whereas, for a matrix multiplication benchmark the average execution speedup of 84 times has been achieved by Rubus on the same GPU. Moreover, Rubus achieves this performance without drastically increasing the memory footprint of a program.

Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level implementation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving such kind of problem can reduce under or over utilization of resources, increase user satisfaction by serving more users during peak hours, reduce implementation cost for providers and service cost for users. Existing works on cloud computing focuses to accurate estimation of the capacity needs, static or dynamic VM (Virtual Machine) creation and scheduling. But significant amount of time is required to create and destroy VMs which could be used to serve more user requests. In this paper, an adaptive QoS (Quality of Service) aware VM provisioning mechanism is developed that ensures efficient utilization of the system resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is ensured by serving all the tasks within the requirements described in SLA. Tasks are separated using multilevel queue and the most urgent task is given high priority. The simulation-based experimental results shows that a great number of tasks can be served compared to others which will help to satisfy customers during the peak hour.

Multidisciplinary design of complex products leads to an increasing demand for high performance simulation (HPS) platforms. One great challenge is how to achieve high efficient utilisation of large-scale simulation resources in distributed and heterogeneous environments. This article reports a virtualisation-based methodology to realise a HPS platform. This research is driven by the issues concerning large-scale simulation resources deployment and complex simulation environment construction, efficient and transparent utilisation of fine-grained simulation resources and high reliable simulation with fault tolerance. A framework of virtualisation-based simulation platform (VSIM) is first proposed. Then the article investigates and discusses key approaches in VSIM, including simulation resources modelling, a method to automatically deploying simulation resources for dynamic construction of system environment, and a live migration mechanism in case of faults in run-time simulation. Furthermore, the proposed methodology is applied to a multidisciplinary design system for aircraft virtual prototyping and some experiments are conducted. The experimental results show that the proposed methodology can (1) significantly improve the utilisation of fine-grained simulation resources, (2) result in a great reduction in deployment time and an increased flexibility for simulation environment construction and (3)achieve fault tolerant simulation.

Innovation in lignocellulosics dewatering and drying for energy sustainability and enhanced utilization of forestry, agriculture, and marine resources - A review

From patterns to frameworks to parallel programs

Due to the culture of sequential programming, the lack of tools and the inherent difficulties of parallel programming, most programmers find it hard to design and evaluate real time parallel programs. As a result, a major problem found in the development of real time parallel systems is the difficulty to produce rapid prototypes of the application and frequently the development of these types of systems, is behind schedule. The paper presents a visual environment which should be able to provide continuity in the development of projects using the most common development methods, traditional or object oriented, by offering support during the phases of implementation, debugging and testing. The environment has a visual interface with the user and the main aim is to facilitate the learning process in the generation and debugging of source code of applications developed for parallel kernels, in particular for Virtuoso (Virtual Single Processor Programming System-Virtuoso is a trademark of Eonic Systems-http://www.eonic.com). The environment is composed of four tools: a Parallel Program Generator, a Worst Case Execution Time Analyser, a Scheduling Analyser, and a Parallel Debugger. The first version of the tool is available for download from http://www.dc.ufscar.br/tev/tev.html and was released as Teaching Environment for Virtuoso (TEV).

Real-time applications, hard or soft, are raising
\nthe challenge of unpredictability. This is an extremely difficult
\nproblem in the context of modern, dynamic, multiprocessor
\nplatforms which, while providing potentially high performance,
\nmake the task of timing prediction extremely difficult. Also,
\nwith the growing software content in embedded systems and the
\ndiffusion of highly programmable and re-configurable platforms,
\nsoftware is given an unprecedented degree of control on resource
\nutilization. The 2PARMA project aims at overcoming the lack
\nof parallel programming models and run-time resource management techniques to exploit the features of many-core processor architectures.
\nThe main goals of the 2PARMA project are: the definition
\nof a parallel programming model combining component-based
\nand single-instruction multiple-thread approaches, instruction set
\nvirtualisation based on portable byte-code, run-time resource
\nmanagement policies and mechanisms as well as design space exploration methodologies for Many-core computing architectures.