Abstract
Current generation of computing platforms is embracing multi-core and many-core processors to improve the overall performance of the system, meeting at the same time the stringent energy budgets requested by the market. Parallel programming languages are nowadays paramount to extracting the tremendous potential offered by these platforms: parallel computing is no longer a niche in the high performance computing (HPC) field, but an essential ingredient in all domains of computer science. The advent of next-generation many-core embedded platforms has the chance of intercepting a converging need for predictable high-performance coming from both the High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. This converging demand raises the problem about how to guarantee timing requirements in presence of parallel execution. The paper presents how the time-criticality and parallelisation challenges are addressed by merging techniques coming from both HPC and EC domains, and provides an overview of the proposed framework to achieve these objectives.

Abstract
There is an increasing eagerness to deploy and execute parallel applications on many-core infrastructures, preserving the time-predictability of the execution as required by real-time practices to upper-bound the response time of the embedded application. In this context, the paper discusses the application of the currently-available WCET analysis techniques and tools on such platforms and with highly parallel activities. After discussing the pros and cons of all different methodologies for WCET analysis, we introduce a new approach that is developed within the P-SOCRATES project.

Abstract
This paper proposes a model for scheduling parallel real-time tasks. The proposed model uses a work-stealing approach to schedule real-time parallel task sets at runtime, where each job may present a nested fork-join structure, generate an arbitrary number of parallel jobs, and each parallel job inherits the timing properties of the job that spawns it.

Abstract
Recently, a semantic and runtime model for parallel programming was proposed for addition to Ada. The proposal uses program annotations (expressed as Ada 2012 aspects) to inform the compiler of opportunities for parallel computation, and also offers the ability to specify details of parallel execution. The proposal includes support for specialized behaviors via dedicated libraries and a runtime environment that builds on pools of worker tasks. This paper extends that work by adding notations for data types and parallel blocks, simplifying some of the parallel notations and eliminating obstructions to the implementation of efficient parallel algorithms.

Abstract

Abstract
The advent of next-generation many-core embedded platforms has the chance of intercepting a converging need for predictable high-performance coming from both the High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. This converging demand, however, raises the problem about how to guarantee timing requirements in presence of parallel execution. This paper presents the approach of project P-SOCRATES for the design of an integrated framework for the execution of workload-intensive applications with real-time requirements on top of nextgeneration commercial-off-the-shelf (COTS) platforms based on many-core accelerated architectures. The time-criticality and parallelisation challenges are addressed by merging techniques coming from both HPC and EC domains, identifying the main sources of indeterminism and proposing efficient mapping and scheduling algorithms, along with the associated timing and schedulability analysis, to guarantee the real-time and performance requirements of the applications.