Despite their proliferation across many embedded platforms, GPUs present still many challenges to embedded-system designers. In particular, GPU-optimized software actually slows down the execution of embedded applications on system simulators. This problem is worse for concurrent simulations of multiple instances of embedded devices equipped with GPUs. To address this challenge, we present ΣVP, a framework to accelerate concurrent simulations of multiple virtual platforms by leveraging the physical GPUs present on the host machine. ΣVP multiplexes the host GPUs to speed up the concurrent simulations without requiring any change to the original GPU-optimized application code. With ΣVP, GPU applications run more than 600 times faster than GPU-software emulation on virtual platforms. We also propose Kernel Interleaving and Kernel Coalescing, two techniques that further speed up the simulation by one order of magnitude. Finally, we show how ΣVP supports simulation-based functional validation and performance/power estimation.

Personal mobile devices offer a growing variety of personalized services that enrich considerably the user experience. This is made possible by increased access to personal information, which to a large extent is extracted from user email messages and archives. There are, however, two main issues. First, currently these services can be offered only by large web-service companies that can also deploy email services. Second, keeping a large amount of structured personal information on the cloud raises privacy concerns. To address these problems, we propose LN-Annote, a new method to extract personal information from the email that is locally available on mobile devices (without remote access to the cloud). LN-Annote enables third-party service providers to build a question-answering system on top of the local personal information without having to own the user data. In addition, LN-Annote mitigates the privacy concerns by keeping the structured personal information directly on the personal device. Our method is based on a named-entity recognizer trained in two separate steps: first using a common dataset on the cloud and then using a personal dataset in the mobile device at hand. Our contributions include also the optimization of the implementation of LN-Annote: in particular, we implemented an OpenCL version of the custom-training algorithm to leverage the Graphic Processing Unit (GPU) available on the mobile device. We present an extensive set of experiment results: beside proving the feasibility of our approach, they demonstrate its efficiency in terms of the named-entity extraction performance as well as the execution speed and the energy consumption spent in mobile devices.

From a single SoC to a network of embedded devices communicating with a backend cloud-computing server, emerging classes of embedded systems feature an increasing number of heterogeneous components that operate concurrently in a distributed environment. As the scale and complexity of these systems continues to grow, there is a critical need for scalable and efficient simulators. We propose a networked virtual platform as a scalable environment for modeling and simulation. The goal is to support the development and optimization of embedded computing applications by handling heterogeneity at the chip, node, and network level. To illustrate the properties of our approach, we present two very different case studies: the design of an Open MPI scheduler for a heterogeneous distributed embedded system and the development of an application for crowd estimation through the analysis of pictures uploaded from mobile phones.

An expanding wealth of ubiquitous, heterogeneous, and interconnected embedded devices is behind most of the exponential growth of the “Big Data” phenomenon. Meanwhile, the same embedded devices continue to improve in terms of computational capabilities, thus closing the gap with more traditional computers. Motivated by these trends, we developed a heterogeneous computing system for MapReduce applications that couples cloud computing with distributed embedded computing. Specifically, our system combines a central cluster of Linux servers with a broadband network of embedded set-top box (STB) devices. The MapReduce platform is based on the Hadoop software framework, which we modified and optimized for execution on the STBs. Experimental results confirm that this type of heterogeneous computing system can offer a scalable and energy-efficient platform for the processing of large-scale data-intensive applications.