skip to main content

Title: Simultaneous Multithreading Applied to Real Time
Existing models used in real-time scheduling are inadequate to take advantage of simultaneous multithreading (SMT), which has been shown to improve performance in many areas of computing, but has seen little application to real-time systems. The SMART task model, which allows for combining SMT and real time by accounting for the variable task execution costs caused by SMT, is introduced, along with methods and conditions for scheduling SMT tasks under global earliest-deadline-first scheduling. The benefits of using SMT are demonstrated through a large-scale schedulability study in which we show that task systems with utilizations 30% larger than what would be schedulable without SMT can be correctly scheduled. This artifact includes benchmark experiments used to compare execution times with and without SMT and code to duplicate the reported schedulability experiments.
; ;
Award ID(s):
1717589 1837337
Publication Date:
Journal Name:
Dagstuhl artifacts series
Sponsoring Org:
National Science Foundation
More Like this
  1. The applicability of Simultaneous Multithreading (SMT) to real-time systems has been hampered by the difficulty of obtaining reliable execution costs in an SMT-enabled system. This problem is addressed by introducing a scheduling framework, called CERT-MT, that combines scheduling-aware timing analysis with a cyclic-executive scheduler in a way that minimizes SMT-related timing variations. The proposed scheduling-aware timing analysis is based on maximum observed execution times and accounts for the uncertainty inherent in measurement-based timing analysis. The timing analysis is found to work for tasks with and without SMT, though some adjustments are required in the former case. A large-scale schedulability study is presented that shows CERT-MT can schedule systems with total utilizations approaching 1.4 times the core count, without sacrificing safety.
  2. Simultaneous Multithreading (SMT) has the ability to dramatically improve real-time scheduling, but existing methods are cumbersome, frequently need specialized hardware, or are limited to producing table-based schedules. Here, an easily portable method for quickly applying SMT to priority-driven hard real-time systems is given. Using a combination of integer linear programming and heuristic bin-packing, a partitioned Earliest-Deadline-First (EDF) scheduler that takes advantage of SMT is produced. The integer linear programming and partitioning are done offline, but generally require only a few seconds, even given over a hundred tasks. A large-scale schedulability study is conducted, showing that compared to partitioned scheduling without SMT, the schedulable utilization for the considered hardware platform is nearly doubled in the best cases.
  3. With the technology trend of hardware and workload consolidation for embedded systems and the rapid development of edge computing, there has been increasing interest in supporting parallel real-time tasks to better utilize the multi-core platforms while meeting the stringent real-time constraints. For parallel real-time tasks, the federated scheduling paradigm, which assigns each parallel task a set of dedicated cores, achieves good theoretical bounds by ensuring exclusive use of processing resources to reduce interferences. However, because cores share the last-level cache and memory bandwidth resources, in practice tasks may still interfere with each other despite executing on dedicated cores. Such resource interferences due to concurrent accesses can be even more severe for embedded platforms or edge servers, where the computing power and cache/memory space are limited. To tackle this issue, in this work, we present a holistic resource allocation framework for parallel real-time tasks under federated scheduling. Under our proposed framework, in addition to dedicated cores, each parallel task is also assigned with dedicated cache and memory bandwidth resources. Further, we propose a holistic resource allocation algorithm that well balances the allocation between different resources to achieve good schedulability. Additionally, we provide a full implementation of our framework by extending themore »federated scheduling system with Intel’s Cache Allocation Technology and MemGuard. Finally, we demonstrate the practicality of our proposed framework via extensive numerical evaluations and empirical experiments using real benchmark programs.« less
  4. Intermittently-powered devices have gained much interest in recent years. However, scheduling real-time tasks while supporting data consistency, timekeeping, and schedulability guarantees on these devices still remains a challenge. Many sensing tasks need long indivisible sensor reading operations, but most prior work has limited their focus to the forward progress of computation-only tasks. In this paper, we propose a scheduling framework to execute real-time periodic tasks with atomic sensing operations. Our proposed method keeps track of time progress and ensures the periodic execution of sensing tasks while efficiently utilizing intermittent power sources. We provide schedulability analysis to determine if a taskset is schedulable under a given charging condition. As a proof-of-concept, we design a custom programmable RFID tag device, called R’tag, and demonstrate the effectiveness of our framework in a realistic sensing application. Evaluation results show that the proposed method satisfies the real-time task execution requirements on IPDs in terms of task scheduling, timekeeping, and periodic sensing while significantly outperforming prior work.
  5. In this paper, we present RT-Gang: a novel realtime gang scheduling framework that enforces a one-gang-at-atime policy. We find that, in a multicore platform, co-scheduling multiple parallel real-time tasks would require highly pessimistic worst-case execution time (WCET) and schedulability analysis—even when there are enough cores—due to contention in shared hardware resources such as cache and DRAM controller. In RT-Gang, all threads of a parallel real-time task form a real-time gang and the scheduler globally enforces the one-gangat-a-time scheduling policy to guarantee tight and accurate task WCET. To minimize under-utilization, we integrate a state-of-the-art memory bandwidth throttling framework to allow safe execution of best-effort tasks. Specifically, any idle cores, if exist, are used to schedule best-effort tasks but their maximum memory bandwidth usages are strictly throttled to tightly bound interference to real-time gang tasks. We implement RT-Gang in the Linux kernel and evaluate it on two representative embedded multicore platforms using both synthetic and real-world DNN workloads. The results show that RT-Gang dramatically improves system predictability and the overhead is negligible.