Recent market forecasts predict that the portable computing trend will vastly spread, as by 2020 there will bemore than 3 billion LTE device users worldwide. Motivated by this fact, many companies and research institutes have already launched research projects that utilize portable devices, voluntarily provided by users, to perform the required computations. Many such projects employ Berkeley's BOINC middleware, since it can support a large variety of stationary and mobile devices. However, currently available BOINC high-level APIs, either do not support portable devices or lack advanced processing capabilities (such as inter-node task dependencies) and/or easiness of use. To resolve these issues, we propose the mCluster software framework for application execution powered by the BOINC middleware on portable devices. mCluster adopts a task-based programming model that requires simple, pragma-based annotations of the application software, in order to dynamically resolve task dependencies. To evaluate our framework, we have have mapped a scientific application from the neuroscience domain on an small-scaled network of portable devices. mCluster significantly reduces the required programming effort and complexity to efficiently map BOINC-powered applications with task dependencies on portable devices compared to previous approaches.@en

In this paper, we describe a generic approach for integrating a dynamically reconfigurable device into a general purpose system interconnected with a high-speed link. The system can dynamically install and execute hardware instances of functions to accelerate parts of a given software code. The hardware descriptions of the functions (bitstreams) are inserted into the executable binary running on the system. Our compiler further inserts system-calls to the software code to control the reconfigurable device. Thereby, the general purpose host-processor of the system manages the hardware reconfiguration and execution through a Linux device driver. The device has direct access to the main memory (DMA) operating in the virtual address space; it further supports memory mapped IO for data and control, and is able to raise and handle interrupts for synchronization. The above system is implemented on a general purpose machine providing a HyperTransport bus to connect a Xilinx Virtex4-100 FPGA, an AMD Opteron-244, and 1 GB of DDR main memory. We evaluate our proposal using a secure audio processing application. We accelerate in hardware the Audio processing kernel as well as the subsequent AES encryption function via dynamic partial self-reconfiguration. The proposed system achieves a 12 speedup over a software for the application at hand.

@en