Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

Abstract. We apply three different modeling frameworks -timed automata (Uppaal), colored Petri nets and synchronous data flow -to model a challenging industrial case study that involves an existing stateof-the-art image processing pipeline. Each of the resulting models is used to derive schedules for multiple concurrent jobs in the presence of limited resources (processing units, memory, USB bandwidth,..). The three models and corresponding analysis results are compared.

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This paper introduces two tools for manual energy evaluation and runtime tuning developed at IT4Innovations in the READEX project. The MERIC library can be used for manual instrumentation and analysis of any application from the energy and time consumption point of view. Besides tracing, MERIC can also change environment and hardware parameters during the application runtime, which leads to energy savings. MERIC stores large amounts of data, which are difficult to read by a human. The RADAR generator analyses the MERIC output files to find the best settings of evaluated parameters for each instrumented region. It generates a L A T E X report and a MERIC configuration file for application production runs.

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In SDR, it is advantageous if the data is transmitted using the best among the various modulation techniques with respect to SNR. The receiver has the ability to automatically identify the modulation technique and demodulate it. This process involves a novel method where a pilot sequence helps in identifying the type of modulation at the receiver. Ten modulation techniques are used in this study and their performance is studied. Further, error detection and correction is done to the received data using hamming encoder. This study also discusses about the error handling capability and detection of the position in which the error occurs. This work can be implemented in remote locations and in emergency situations where a particular modulation technique is used based on the available QoS.