The design of a real-time architecture is governed by a trade-off between analyzability necessary for real-time formalism and performance demanded by high-end embedded systems. We reconcile this trade-off with a novel Real-time Virtual Multiprocessor (RVMP). RVMP virtualizes a single in-order superscalar processor into multiple interference-free different-sized virtual processors. This provides a flexible spatial dimension. In the time dimension, the number and size of virtual processors can be rapidly reconfigured. A simple real-time scheduling approach concentrates scheduling within a small time interval, producing a simple repeating space/time schedule that orchestrates virtualization. RVMP successfully combines the analyzability (hence real-time formalism) of multiple processors with the flexibility (hence high performance) of simultaneous multithreading (SMT). Worst-case schedulability experiments show that more task-sets are provably schedulable on RVMP than on conventional rigid multiprocessors with equal aggregate resources, and the advantage only intensifies with more demanding task-sets. Run-time experiments show RVMP’s statically-controlled coarser-grain space/time configurability is as effective as unsafe SMT. Moreover, RVMP provides a real-time formalism that SMT does not currently provide.

Worst-case execution times (WCET) of tasks are essential for safe scheduling in hard real-time systems. However, contemporary processors exceed the capabilities of static worst-case timing analysis tools. The Virtual Simple Architecture (VISA) framework shifts the burden of bounding the WCET of tasks, in part, to hardware. A VISA is the pipeline timing specification of a hypothetical simple processor. WCET is derived for a task assuming the VISA. Nonetheless, at run-time, the task is executed speculatively on an unsafe complex processor, and its progress is continuously gauged. If continued safe progress appears to be in jeopardy, the complex processor is reconfigured to a simple mode of operation that directly implements the VISA, thereby explicitly bounding the task's overall execution time by the WCET.

Abstract—In this paper we present example applications using a deadline instruction. The deadline instruction brings cycle accurate timing information into the application code. We have implemented the mechanism in a time-predictable Java chipmultiprocessor. As a proof of the accuracy that can be gained, a digital to analog conversion of audio signals is implemented completely in software. Furthermore, we show how the deadline instruction can be used to verify bytecode execution times on chip-multiprocessors and how to synchronize tasks to a timedivision based memory arbiter. I.

Abstract—This paper argues that repeatable timing is more important and more achievable than predictable timing. It describes microarchitecture approaches to pipelining and memory hierarchy that deliver repeatable timing and promise comparable or better performance compared to established techniques. Specifically, threads are interleaved in a pipeline to eliminate pipeline hazards, and a hierarchical memory architecture is outlined that hides memory latencies. I.

Scheduling in hard real-time systems requires a priori knowledge of worst-case execution times (WCET). Obtaining the WCET of a task is a difficult problem. Static timing analysis techniques approach this problem via path analysis, pipeline simulation and cache simulation to derive safe WCET bounds. But such analysis has traditionally been constrained to only small programs due to the complexity of simulation, most notably the complexity of static cache simulation, which requires inter-procedural analysis. This

High-performance microprocessors, e.g., multithreaded and multicore processors, are being implemented in embedded real-time systems because of the increasing computational requirements. These complex microprocessors have two major drawbacks when they are used for real-time purposes. First, their complexity difficults the calculation of the WCET (Worst Case Execution Time). Second, power consumption requirements are much larger, which is a major concern in these systems. In this paper we propose a novel soft power-aware real-time scheduler for a state-of-the-art multicore multithreaded processor, which implements dynamic voltage scaling techniques. The proposed scheduler reduces the energy consumption while satisfying the constraints of soft real-time applications. Different scheduling alternatives have been evaluated, and experimental results show that using a fair scheduling policy, the proposed algorithm provides, on average, energy savings ranging from 34 % to 74%. 1

In Simultaneous Multithreaded (SMT) architectures most hardware resources are shared between threads. This provides a good cost/performance trade-off which renders these architectures suitable for use in embedded systems. However, since threads share many resources, they also interfere with each other. As a result, execution times of applications become highly unpredictable and dependent on the context in which an application is executed. Obviously, this poses problems if an SMT is to be used in a real-time system. In this paper, we propose two novel hardware mechanisms that can be used to reduce this performance variability. In contrast to previous approaches, our proposed mechanisms do not need any information beyond the information already known by traditional job schedulers. Nor do they require extensive profiling of workloads to determine optimal schedules. Our mechanisms are based on dynamic resource partitioning. The OS level job scheduler needs to be slightly adapted in order to provide the hardware resource allocator some information on how this resource partitioning needs to be done. We show that our mechanisms provide high stability for SMT architectures to be used in real-time systems: the real time benchmarks we used meet their deadlines in more than 98 % of the cases considered while the other thread in the workload still achieves high throughput.

Embedded systems with real-time constraints depend on a-priori knowledge of worst-case execution times (WCETs) to determine if tasks meet deadlines. Static timing analysis derives bounds on WCETs but requires statically known loop bounds. This work removes the constraint on known loop bounds through parametric analysis expressing WCETs as functions. Tighter WCETs are dynamically discovered to exploit slack by dynamic voltage scaling (DVS) saving 60%-82 % energy over DVS-oblivious techniques and showing savings close to more costly dynamic-priority DVS algorithms. Overall, parametric analysis expands the class of real-time applications to programs with loop-invariant dynamic loop bounds while retaining tight WCET bounds.

Static timing analysis safely bounds worst-case execution times to determine if tasks can meet their deadlines in hard real-time systems. However, conventional timing analysis requires that the upper bound of loops be known statically, which limits its applicability. Parametric timing analysis methods remove this constraint by providing the WCET as a formula parameterized on loop bounds. This paper contributes a novel technique to allow parametric timing analysis to interact with dynamic real-time schedulers. By dynamically detecting actual loop bounds, a lower WCET bound can be calculated, on-the-fly, for the remaining execution of a task. We analyze the benefits from parametric analysis in terms of dynamically discovered slack in a schedule. We then assess the potential for dynamic power conservation by exploiting parametric loop bounds for ParaScale, our intra-task dynamic voltage scaling (DVS) approach. Our results demonstrate that the parametric approach to timing analysis provides 66%-80% additional savings in power consumption. We further show that using this approach combined with online intra-task DVS to exploit parametric execution times results in much lower power consumption. Hence, even in the absence of dynamic scheduling, significant savings in power can be obtained, e.g., in the case of cyclic executives. 1.

Performance bounds represent the best achievable performance that can be delivered by target microarchitectures on specified workloads. Accurate performance bounds establish an efficient way to evaluate the performance potential of either code optimizations or architectural innovations. We advocate using performance bounds to guide code compilation. In this dissertation, we introduce a novel bound-guided approach to systematically regulate code-size related instruction level parallelism (ILP) optimizations, including tail duplication, loop unrolling, and if-conversion. Our approach is based on the notion of code size efficiency, which is defined as the ratio of ILP improvement over static code size increase. With such a notion, we (1) develop a general approach to selectively perform optimizations to maximize the ILP improvement while minimizing the cost in code size, (2) define the optimal tradeoff between ILP improvement and code size overhead, and (3) develop a heuristic to achieve this optimal tradeoff. We extend our performance bounds as well as code size efficiency to perform code-size-aware compilation for real-time applications. The profile independent