Computing Safe Contention Bounds for Multicore Resources with Round-Robin and FIFO Arbitration

Numerous researchers have studied the contention that arises among tasks running in parallel on a multicore processor. Most of those studies seek to derive a tight and sound upper-bound for the worst-case delay with which a processor resource may serve an incoming request, when its access is arbitrated using time-predictable policies such as round-robin or FIFO. We call this value upper-bound delay ( ubd ). Deriving trustworthy ubd statically is possible when sufficient public information exists on the timing latency incurred on access to the resource of interest. Unfortunately however, that is rarely granted for commercial-of-the-shelf (COTS) processors. Therefore, the users resort to measurement observations on the target processor and thus compute a “measured” ubdm . However, using ubdm to compute worst-case execution time values for programs running on COTS multicore processors requires qualification on the soundness of the result. In this paper, we present a measurement-based methodology to derive a ubdm under round-robin (RoRo) and first-in-first-out (FIFO) arbitration, which accurately approximates ubd from above, without needing latency information from the hardware provider. Experimental results, obtained on multiple processor configurations, demonstrate the robustness of the proposed methodology.The research leading to this work has received funding from: the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644080(SAFURE); the European Space Agency under Contract 789.2013 and NPI Contract 40001102880; and COST Action IC1202, Timing Analysis On Code-Level (TACLe). This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. The authors would like to thanks Paul Caheny for his help with the proofreading of this document.Peer ReviewedPostprint (author's final draft

NoCo: ILP-based worst-case contention estimation for mesh real-time manycores

Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015- 65316-P and the HiPEAC Network of Excellence. It also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (agreement No. 772773). Carles Hernández is jointly supported by the MINECO and FEDER funds through grant TIN2014-60404-JIN. Jaume Abella has been partially supported by the Spanish Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Enrico Mezzetti has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva-Incorporaci´on postdoctoral fellowship number IJCI-2016-27396.Peer ReviewedPostprint (author's final draft

A Survey on Cache Management Mechanisms for Real-Time Embedded Systems

© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {48, 2, (November 2015)} http://doi.acm.org/10.1145/2830555Multicore processors are being extensively used by real-time systems, mainly because of their demand for increased computing power. However, multicore processors have shared resources that affect the predictability of real-time systems, which is the key to correctly estimate the worst-case execution time of tasks. One of the main factors for unpredictability in a multicore processor is the cache memory hierarchy. Recently, many research works have proposed different techniques to deal with caches in multicore processors in the context of real-time systems. Nevertheless, a review and categorization of these techniques is still an open topic and would be very useful for the real-time community. In this article, we present a survey of cache management techniques for real-time embedded systems, from the first studies of the field in 1990 up to the latest research published in 2014. We categorize the main research works and provide a detailed comparison in terms of similarities and differences. We also identify key challenges and discuss future research directions.King Saud University NSER

Architectural performance analysis of FPGA synthesized LEON processors

Current processors have gone through multiple internal opti- mization to speed-up the average execution time e.g. pipelines, branch prediction. Besides, internal communication mechanisms and shared resources like caches or buses have a sig- nificant impact on Worst-Case Execution Times (WCETs). Having an accurate estimate of a WCET is now a challenge. Probabilistic approaches provide a viable alternative to single WCET estimation. They consider WCET as a probabilistic distribution associated to uncertainty or risk. In this paper, we present synthetic benchmarks and associated analysis for several LEON3 configurations on FPGA targets. Benchmarking exposes key parameters to execution time variability allowing for accurate probabilistic modeling of system dynamics. We analyze the impact of architecture- level configurations on average and worst-case behaviors

Taming Data Caches for Predictable Execution on GPU-based SoCs

Heterogeneous SoCs (HeSoCs) typically share a single DRAM between the CPU and GPU, making workloads susceptible to memory interference, and predictable execution troublesome. State-of-the art predictable execution models (PREM) for HeSoCs prefetch data to the GPU scratchpad memory (SPM), for computations to be insensitive to CPU-generated DRAM traffic. However, the amount of work that the small SPM sizes allow is typically insufficient to absorb CPU/GPU synchronization costs. On-chip caches are larger, and would solve this issue, but have been argued too unpredictable due to self-evictions. We show how self-eviction can be minimized in GPU caches via clever managing of prefetches, thus lowering the performance cost, while retaining timing predictability

A survey of techniques for reducing interference in real-time applications on multicore platforms

This survey reviews the scientific literature on techniques for reducing interference in real-time multicore systems, focusing on the approaches proposed between 2015 and 2020. It also presents proposals that use interference reduction techniques without considering the predictability issue. The survey highlights interference sources and categorizes proposals from the perspective of the shared resource. It covers techniques for reducing contentions in main memory, cache memory, a memory bus, and the integration of interference effects into schedulability analysis. Every section contains an overview of each proposal and an assessment of its advantages and disadvantages.This work was supported in part by the Comunidad de Madrid Government "Nuevas Técnicas de Desarrollo de Software de Tiempo Real Embarcado Para Plataformas. MPSoC de Próxima Generación" under Grant IND2019/TIC-17261

07101 Abstracts Collection -- Quantitative Aspects of Embedded Systems

From March 5 to March 9, 2007, the Dagstuhl Seminar 07101 ``Quantitative Aspects of Embedded Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Deterministic Memory Abstraction and Supporting Multicore System Architecture

Poor time predictability of multicore processors has been a long-standing challenge in the real-time systems community. In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time computing on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware. We, therefore, propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform-the OS and hardware-guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We propose to utilize both abstractions to achieve high time predictability but without significantly sacrificing performance. We present deterministic memory-aware OS and architecture designs, including OS-level page allocator, hardware-level cache, and DRAM controller designs. We implement the proposed OS and architecture extensions on Linux and gem5 simulator. Our evaluation results, using a set of synthetic and real-world benchmarks, demonstrate the feasibility and effectiveness of our approach