A real-time scratchpad-centric OS for multi-core embedded systems

Multicore processors have been increasing in development by the industry to meet the ever-growing processing requirements of various applications because these processors offer benefits such as reduced power consumption, more processing power and efficient parallel task execution for general purpose work- loads. However, in hard real time systems where predictability is a key aspect, the average performance of these multicore processors is even worse than the scenarios in which the same task set is executed on a single core processor. This performance degradation is due to the fact that the multicore systems have shared resources such as DRAM, BUS and caches which make the system highly unpredictable. One way to achieve predictability in such systems is to serialize the access of the cores to the shared resources such that there is no contention. Another widely emerging approach is the integration of the scratchpad memory. Using scratchpad, at run time, the code and data for the requested task is made available in the scratchpad and contention can be avoided. In this thesis, we approach the problem of shared resource arbitration at an OS-level and propose a novel scratchpad centric OS design for multi-core platforms. In the proposed OS, the predictable usage of shared resources across multiple cores represents a central design-time goal. Hence, we show (i) how contention-free execution of real-time tasks can be achieved on scratchpad-based architectures, and (ii) how a separation of application logic and I/O operations in the time domain can be enforced. To validate the proposed design, we implemented the proposed OS using a commercial-off-the-shelf (COTS) platform. Experiments show that this novel design delivers predictable temporal behavior to hard real-time tasks, and it improves performance up to 2.1x compared to traditional approaches

Three-Dimensional Processing-In-Memory-Architectures: A Holistic Tool For Modeling And Simulation

Die gemeinhin als Memory Wall bekannte, sich stetig weitende Leistungslücke zwischen Prozessor- und Speicherarchitekturen erfordert neue Konzepte, um weiterhin eine Skalierung der Rechenleistung zu ermöglichen. Da Speicher als die Beschränkung innerhalb einer Von-Neumann-Architektur identifiziert wurden, widmet sich die Arbeit dieser Problemstellung. Obgleich dreidimensionale Speicher zu einer Linderung der Memory Wall beitragen können, sind diese alleinig für die zukünftige Skalierung ungenügend. Aufgrund höherer Effizienzen stellt die Integration von Rechenkapazität in den Speicher (Processing-In-Memory, PIM) ein vielversprechender Ausweg dar, jedoch existiert ein Mangel an PIM-Simulationsmodellen. Daher wurde ein flexibles Simulationswerkzeug für dreidimensionale Speicherstapel geschaffen, welches zur Modellierung von dreidimensionalen PIM erweitert wurde. Dieses kann Speicherstapel wie etwa Hybrid Memory Cube standardkonform simulieren und bietet zugleich eine hohe Genauigkeit indem auf elementaren Datenpaketen in Kombination mit dem Hardware validierten Simulator BOBSim modelliert wird. Ein eigens entworfener Simulationstaktbaum ermöglicht zugleich eine schnelle Ausführung. Messungen weisen im funktionalen Modus eine 100-fache Beschleunigung auf, wohingegen eine Verdoppelung der Ausführungsgeschwindigkeit mit Taktgenauigkeit erzielt wird. Anhand eines eigens implementierten, binärkompatiblen GPU-Beschleunigers wird die Modellierung einer vollständig dreidimensionalen PIM-Architektur demonstriert. Dabei orientieren sich die maximalen Hardwareressourcen an einem PIM-Beschleuniger aus der Literatur. Evaluiert wird einerseits das GPU-Simulationsmodell eigenständig, andererseits als PIM-Verbund jeweils mit Hilfe einer repräsentativ gewählten, speicherbeschränkten geophysikalischen Bildverarbeitung. Bei alleiniger Betrachtung des GPU-Simulationsmodells weist dieses eine signifikant gesteigerte Simulationsgeschwindigkeit auf, bei gleichzeitiger Abweichung von 6% gegenüber dem Verilator-Modell. Nachfolgend werden innerhalb dieser Arbeit unterschiedliche Konfigurationen des integrierten PIM-Beschleunigers evaluiert. Je nach gewählter Konfiguration kann der genutzte Algorithmus entweder bis zu 140GFLOPS an tatsächlicher Rechenleistung abrufen oder eine maximale Recheneffizienz von synthetisch 30% bzw. real 24,5% erzielen. Letzteres stellt eine Verdopplung des Stands der Technik dar. Eine anknüpfende Diskussion erläutert eingehend die Resultate.The steadily widening performance gap between processor- and memory-architectures - commonly known as the Memory Wall - requires novel concepts to achieve further scaling in processing performance. As memories were identified as the limitation within a Von-Neumann-architecture, this work addresses this constraining issue. Although three-dimensional memories alleviate the effects of the Memory Wall, the sole utilization of such memories would be insufficient. Due to higher efficiencies, the integration of processing capacity into memories (so-called Processing-In-Memory, PIM) depicts a promising alternative. However, a lack of PIM simulation models still remains. As a consequence, a flexible simulation tool for three-dimensional stacked memories was established, which was extended for modeling three-dimensional PIM architectures. This tool can simulate stacked memories such as Hybrid Memory Cube standard-compliant and simultaneously offers high accuracy by modeling on elementary data packets (FLIT) in combination with the hardware validated BOBSim simulator. To this, a specifically designed simulation clock tree enables an rapid simulation execution. A 100x speed up in simulation execution can be measured while utilizing the functional mode, whereas a 2x speed up is achieved during clock-cycle accuracy mode. With the aid of a specifically implemented, binary compatible GPU accelerator and the established tool, the modeling of a holistic three-dimensional PIM architecture is demonstrated within this work. Hardware resources used were constrained by a PIM architecture from literature. A representative, memory-bound, geophysical imaging algorithm was leveraged to evaluate the GPU model as well as the compound PIM simulation model. The sole GPU simulation model depicts a significantly improved simulation performance with a deviation of 6% compared to a Verilator model. Subsequently, various PIM accelerator configurations with the integrated GPU model were evaluated. Depending on the chosen PIM configuration, the utilized algorithm achieves 140GFLOPS of processing performance or a maximum computing efficiency of synthetically 30% or realistically 24.5%. The latter depicts a 2x improvement compared to state-of-the-art. A following discussion showcases the results in depth

Scratchpad Memory Management For Multicore Real-Time Embedded Systems

Multicore systems will continue to spread in the domain of real-time embedded systems due to the increasing need for high-performance applications. This research discusses some of the challenges associated with employing multicore systems for safety-critical real-time applications. Mainly, this work is concerned with providing: 1) efficient inter-core timing isolation for independent tasks, and 2) predictable task communication for communicating tasks. Principally, we introduce a new task execution model, based on the 3-phase execution model, that exploits the Direct Memory Access (DMA) controllers available in modern embedded platforms along with ScratchPad Memories (SPMs) to enforce strong timing isolation between tasks. The DMA and the SPMs are explicitly managed to pre-load tasks from main memory into the local (private) scratchpad memories. Tasks are then executed from the local SPMs without accessing main memory. This model allows CPU execution to be overlapped with DMA loading/unloading operations from and to main memory. We show that by co-scheduling task execution on CPUs and using DMA to access memory and I/O, we can efficiently hide access latency to physical resources. In turn, this leads to significant improvements in system schedulability, compared to both the case of unregulated contention for access to physical resources and to previous cache and SPM management techniques for real-time systems. The presented SPM-centric scheduling algorithms and analyses cover single-core, partitioned, and global real-time systems. The proposed scheme is also extended to support large tasks that do not fit entirely into the local SPM. Moreover, the schedulability analysis considers the case of recovering from transient soft errors (bit flips caused by a single event upset) in several levels of memories, that cannot be automatically corrected in hardware by the ECC unit. The proposed SPM-centric scheduling is integrated at the OS level; thus it is transparent to applications. The proposed scheme is implemented and evaluated on an FPGA platform and a Commercial-Off-The-Shelf (COTS) platform. In regards to real-time task communication, two types of communication are considered. 1) Asynchronous inter-task communication, between either sequential tasks (single-threaded) or parallel tasks (multi-threaded). 2) Intra-task communication, where parallel threads of the same application exchange data. A new task scheduling model for parallel tasks (Bundled Scheduling) is proposed to facilitate intra-task communication and reduce synchronization overheads. We show that the proposed bundled scheduling model can be applied to several parallel programming models, such as fork-join and DAG-based applications, leading to improved system schedulability. Finally, intra-task communication is governed by a predictable inter-core communication platform. Specifically, we propose HopliteRT, a lean and predictable Network-on-Chip that connects the private SPMs