Abstract

Abstract
Nowadays, the prevalence of computing systems in our lives is so ubiquitous that it would not be far-fetched to state that we live in a cyber-physical world dominated by computer systems. These systems demand for more and more computational performance to process large amounts of data from multiple data sources, some of them with guaranteed processing response times. In other words, systems are required to deliver their results within pre-defined (and sometimes extremely short) time bounds. Examples can be found for instance in intelligent transportation systems for fuel consumption reduction in cities or railway, or autonomous driving of vehicles. To cope with such performance requirements, chip designers produced chips with dozens or hundreds of cores, interconnected with complex networks on chip. Unfortunately, the parallelization of the computing activities brings many challenges, among which how to provide timing guarantees, as the timing behaviour of the system running within a many-core processor depends on interactions on shared resources that are most of the time not know by the system designer. P-SOCRATES (Parallel Software Framework for Time-Critical Many-core Systems) is an FP7 European project, which developed a novel methodology to facilitate the deployment of standardized parallel architectures for real-time applications. This methodology was implemented (based on existent models and components) to provide an integrated software development kit, the UpScale SDK, to fully exploit the huge performance opportunities brought by the most advanced many-core processors, whilst ensuring a predictable performance and maintaining (or even reducing) development costs of applications. The presentation will provide an overview of the UpScale SDK, its underlying methodology, and the results of its application on relevant industrial use-cases.

Abstract

Abstract

Abstract
This paper presents the timing analysis methodology developed in the European project P-SOCRATES (Parallel Software Framework for Time-Critical Many-core Systems). This timing analysis methodology is defined for parallel applications that must satisfy both performance and real-time requirements and are executed on modern many-core processor architectures. We discuss the motivation and objectives of the project, the timing analysis flow that we proposed, the tool that has been developed to automatize it, and finally we report on some of the preliminary results that we have obtained when applying this methodology to the three application use-cases of the project.

Abstract
There exist many dataflow applications with timing constraints that require real-time guarantees on safe execution without violating their deadlines. Extraction of timing parameters (offsets, deadlines, periods) from these applications enables the use of real-time scheduling and analysis techniques, and provides guarantees on satisfying timing constraints. However, existing extraction techniques require the transformation of the dataflow application from highly expressive dataflow computational models, for example, Synchronous Dataflow (SDF) and Cyclo-Static Dataflow (CSDF) to Homogeneous Synchronous Dataflow (HSDF). This transformation can lead to an exponential increase in the size of the application graph that significantly increases the runtime of the analysis. In this article, we address this problem by proposing an offline heuristic algorithm called slack-based merging. The algorithm is a novel graph reduction technique that helps in speeding up the process of timing parameter extraction and finding a feasible real-time schedule, thereby reducing the overall design time of the real-time system. It uses two main concepts: (a) the difference between the worst-case execution time of the SDF graph's firings and its timing constraints (slack) to merge firings together and generate a reducedsize HSDF graph, and (b) the novel concept of merging called safe merge, which is a merge operation that we formally prove cannot cause a live HSDF graph to deadlock. The results show that the reduced graph (1) respects the throughput and latency constraints of the original application graph and (2) typically speeds up the process of extracting timing parameters and finding a feasible real-time schedule for real-time dataflow applications. They also show that when the throughput constraint is relaxed with respect to the maximal throughput of the graph, the merging algorithm is able to achieve a larger reduction in graph size, which in turn results in a larger speedup of the real-time scheduling algorithms.