Building applications that are responsive and can exploit parallel hardware while remaining simple to write, understand, test, and maintain, poses an important challenge for developers. In particular, it is often desirable to enable various tasks to read or modify shared data concurrently without requiring complicated locking schemes that may throttle concurrency and introduce bugs.We introduce a mechanism that simplifies the parallel execution of different application tasks. Programmers declare what data they wish to share between tasks by using isolation types, and execute tasks concurrently by forking and joining revisions. These revisions are isolated: they read and modify their own private copy of the shared data only. A runtime creates and merges copies automatically, and resolves conflicts deterministically, in a manner declared by the chosen isolation type.To demonstrate the practical viability of our approach, we developed an efficient algorithm and an implementation in the form of a C# library, and used it to parallelize an interactive game application. Our results show that the parallelized game, while simple and very similar to the original sequential game, achieves satisfactory speedups on a multicore processor.

With the advent of the Internet of Things, billions of objects or devices are inserted into the global computer network, generating and processing data at a volume never imagined before. This paper proposes a way to collect and process local data through a data fusion technology called summarization. The main feature of the proposal is the local data fusion, through parameters provided by the application, ensuring the quality of data collected by the sensor node. In the evaluation, the sensor node was compared when performing the data summary with another that performed a continuous recording of the collected data. Two sets of nodes were created, one with a sensor node that analyzed the luminosity of the room, which in this case obtained a reduction of 97% in the volume of data generated, and another set that analyzed the temperature of the room, obtaining a reduction of 80% in the data volume. Through these tests, it has been proven that the local data fusion at the node can be used to reduce the volume of data generated, consequently decreasing the volume of messages generated by IoT environments.

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Electronic system level (ESL) modeling allows early hardware-dependent software (HDS) development. Due to broad CPU diversity and shrinking time-to-market, HDS development can neither rely on hand-retargeting binary tools, nor can it rely on pre-existent tools within standard packages. As a consequence, binary utilities which can be easily adapted to new CPU targets are of increasing interest. We present in this article a framework for automatic generation of binary utilities. It relies on two innovative ideas: platform-aware modeling and more inclusive relocation handling. Generated assemblers, linkers, disassemblers and debuggers were validated for MIPS, SPARC, PowerPC, i8051 and PIC16F84. An open-source prototype generator is available for download.