Threads Scheduling and Load Balancing with Loop Iteration in Multicore Processors: a Case Study with OpenMP

To satisfy the increasing demands in computing throughput, new processor designs are frequently expanding their support for parallel operations, including multiple memory controllers, caches, and various I/O devices, but especially multiple processor cores. For developers of real-time embedded systems with security and/or safety-critical computing requirements, the advent of processors that include multiple cores has created a fundamental problem: how to satisfy certification considerations so that safety and/or security related applications can execute on real-time multi-core based partitioning enforcing systems. This paper examines some architectural considerations that may be taken into account as part of safety and/or security certification of a partitioning operating system that supports the scheduling of multiple applications on a multi-core processor. In particular, it covers a set of considerations and challenges associated with using multiple cores as part of an architecture that supports simultaneous execution of applications on different cores and as part of an architecture that supports simultaneous use of multiple cores cooperatively within an application. This includes high-level considerations of safety and security topics such as, shared resource management, caching, covert channels, and fault management. In addition, it examines the use of synchronous time-scheduling controls as a means to resolve some of the safety and security related issues. This paper, by examining and detailing some of the high-level safety and security considerations associated with multi-core processor architectures, is intended to demonstrate the benefits of utilizing synchronous time-scheduling controls across the entire multi-core processor as a means to resolve some of the issues. Developers of real-time embedded systems can maximize the benefit of multi-core processors through understanding the types of architectural features that may be necessary to resolve specific safety and/or security issues.

Code examples are important resources for expressing correct application programming interface (API) usages. However, many framework and library APIs fail in offering sufficient code examples in corresponding API documentations. This is because constructing proper code examples for documentation purpose takes significant developers' efforts. To reduce such effort, this work proposes a methodology, PropER-Doc, that recommends proper code examples for documentation purpose. PropER-Doc accepts queries from API developers and utilizes code search engines (CSEs) to collect corresponding code example candidates. The structural and conceptual links between API elements are captured from the API implementation and available API documents to guide candidate recommendation. During recommendation, PropER-Doc groups collected candidates based on involved API types for distinguishing different API usages. To assist API developers in selecting proper candidates, a diagrammatic presentation and three code example appropriateness metrics are also developed in {PropER-Doc}. Two case studies on Eclipse JDT framework are conducted to confirm the effectiveness of PropER-Doc.

Application Programming Interfaces (APIs) are everyday tools for every software professional. When creating an API, a designer typically abstracts the tasks the API intends to perform. The API user, on the other hand, should use the API to perform specific-propose tasks. During the development of an API, the designer needs to write dialogues with which the user will interact with the API, performing a conversation between them. In this way, an API capable of offering effective dialogues to its users is called a conversational API. A conversational API is the one that is capable of communicating to its users its form of use and its internal logic of operation, making it clear the design decisions when abstracting concepts and tasks. In this paper, we present Colloquy, an evidence-based method to support the design of conversational APIs. Colloquy was conceived based on the lessons learned from an action research we conducted to identify effective strategies for designing conversational APIs. For six months, we followed the redesign of an existing API developed at an R&D laboratory of a large IT company. In this paper, we also present the first empirical evaluation of Colloquy. We conducted a case study in which the method was employed to design an API for refactoring source code in the Java programming language. Among other benefits, we found that Colloquy was effective in developing empathy with users and modeling conversations with them. Besides, Colloquy contributed to identifying new requirements and created models as API documentation.

APIs (application programming interfaces) play a key role in software development. Virtually, all programmers are potential users of third-party APIs. From the perspective of the theory of Semiotic Engineering, we may characterize an API as an artifact mediating the communication between two types of developers: the API designers and its users. During the construction of an API, the designers should establish proper dialogues with the users. These dialogues will enable the conversation of these actors at the interaction time. In this way, we define a conversational API as an API capable of offering effective dialogues to its users. In this paper, we introduce Colloquy, a method for supporting the design of conversational APIs. Colloquy results from the lessons learnt during an action research conducted for redesigning a real and complex API. The set of Colloquy resources allow API designers to go beyond conventional concerns with usability. We also report in this paper a case study in which Colloquy was used for redesigning a refactoring API. The study findings indicate the method helped the designer creating empathy with the API users, as well as better reflecting and depicting the requirements and the conversations that the API should attend to the different user profiles.

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

CORDITE: The Curated CORona Drug InTERactions Database for SARS-CoV-2

The problem of mining frequent sequential patterns (FSPs) has attracted a great deal of research attention. Although there are many efficient algorithms for mining FSPs, the mining time is still high, especially for large or dense datasets. Parallel processing has been widely applied to improve processing speed for various problems. Some parallel algorithms have been proposed, but most of them have problems related to synchronization and load balancing. Based on a multi-core processor architecture, this paper proposes a load-balancing parallel approach called Parallel Dynamic Bit Vector Sequential Pattern Mining (pDBV-SPM) for mining FSPs from huge datasets using the dynamic bit vector data structure for fast determining support values. In the pDBV-SPM approach, the support count is sorted in ascending order before the set of frequent 1-sequences is partitioned into parts, each of which is assigned to a task on a processor so that most of the nodes in the leftmost branches will be infrequent and thus pruned during the search; this strategy helps to better balance the search tree. Experiments are conducted to verify the effectiveness of pDBV-SPM. The experimental results show that the proposed algorithm outperforms PIB-PRISM for mining FSPs in terms of mining time and memory usage.

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Cloud computing provides us various services over the Internet and services have various problems includes security issues, energy efficiency, big data problem, load balancing etc. Proper Load balancing is used to accomplish proper resource utilization, optimal throughput achievement, reduce response time. Various research papers focused on various technique used to optimize response time, effectively lessens the system load, low computation and other overhead. Resources scheduling in virtual machine in cloud computing environment have also scheduling strategy constructed on genetic algorithm to reduce or avoids dynamic migration. The review paper specially focused on comparison of two algorithms PSO and SJF-MMBF on parameters like total flow time, completion time variance, RPD makespan, RPD total flow time etc.

We describe the development of a prototype to support the consideration of multiple objectives in vessel routing. Our aim is to offer shipping companies alternative routes that take into account various criteria, including costs, route safety and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions, so that they can make informed decisions about their itinerary. This work, conducted in collaboration with the start-up MaritimeAPI, is highly topical: Maritime transport represents about 3 percent of CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in the world. Today, a large number of organisations and countries wish to reduce CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in order to slow down climate change. Our proposed decision tool wraps around the Application Programming Interface (API) of the company Seametrix. This API generates shortest routes for a user-defined set of parameters, governing access to areas affected by piracy, emission restrictions or the price of passage. We use a combination of machine learning and mathematical calculations, operating on the API outputs, to obtain seven distinct criteria, deemed relevant by domain experts. Using multiobjective optimization over the API parameters, we then illustrate how the existing API can be employed to explore the trade-offs between these criteria. Finally, we discuss the use of preference elicitation to support practitioners in the selection of a final solution, and contrast the results from this a posteriori approach to those obtained using a priori preference elicitation.

Multimedia mapping provides a unique approach to integrating geospatial information in digital map format and multimedia information (e.g., text, photographs, sound, and video). Multimedia mapping on the Internet is the direct result of advancement of Web mapping techniques, Internet technology, and Web standards (e.g., HTML, XML, Ajax). However, like traditional Internet GIS applications, multimedia mapping on the Internet is suffered from slow response time, limitation of data sizes, and the slow client/server communications. In addition, the development of multimedia mapping on the Internet requires a huge investment of computer hardware and software, and a steep learning curve for the application developer to become knowledgeable about all of the components involved. It is imperative that a better solution is needed to implement multimedia mapping projects on the Internet. This chapter discusses a new approach of multimedia mapping that utilizes commercial Application Programming Interface (APIs) such as Google Maps API, Yahoo! Flickr API, and YouTube API that can provide faster response time, greater user interaction, as well as higher quality multimedia presentation. A case study of an online visitor guide for the campus of Southern Illinois University Edwardsville is demonstrated.

Multicore processors have been adopted for consumer electronics like portable electronics, mobile phones, car navigation systems, digital TVs and games to obtain high performance with low power consumption. The OSCAR automatic parallelizing compiler has been developed to utilize these multicores easily. Also, a new consumer electronics multicore application program interface (API) to use the OSCAR compiler with native sequential compilers for various kinds of multicores from different vendors has been developed in NEDO (New Energy and Industrial Technology Development Organization) "Multicore Technology for Realtime Consumer Electronics" project with Japanese 6 IT companies. This paper evaluates the parallel processing performance of multimedia applications using this API by the OSCAR compiler on the FR1000 4 VLIW cores multicore processor developed by Fujitsu Ltd, and the RP1 4 SH-4A cores multicore processor jointly-developed by Renesas Technology Corp., Hitachi Ltd. and Waseda University. As the results, the parallel codes generated by the OSCAR compiler using the API give us 3.27 times speedup on average using 4 cores against 1 core on the FR1000 multicore, and 3.31 times speedup on average using 4 cores against 1 core on the RP1 multicore.

The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of “free of charge” GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP) in bulk water, show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 38x between a single GPU vs. a single CPU core and of up to 2.6x when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5x when the API is used instead of FBI.

Load balancing optimization of telecommunication networks with two differentiated services