Design of a WCET-Aware C Compiler

This paper presents techniques to tightly integrate worst-case execution time information into a compiler framework. Currently, a tight integration of WCET information into the compilation process is strongly desired, but only some ad-hoc approaches have been reported currently. Previous publications mainly used self-written WCET estimators with very limited functionality and preciseness during compilation. A very tight integration of a high quality industry-relevant WCET analyzer into a compiler was not yet achieved up to now. This work is the first to present techniques capable of achieving such a tight coupling between a compiler and the WCET analyzer aiT. This is done by automatically translating the assembly-like contents of the compiler\u27s low-level intermediate representation (LLIR) to aiT\u27s exchange format CRL2. Additionally, the results produced by the WCET analyzer are automatically collected and re-imported into the compiler infrastructure. The work described in this paper is smoothly integrated into a C compiler environment for the Infineon TriCore processor. It opens up new possibilities for the design of WCET-aware optimizations in the future. The concepts for extending the compiler infrastructure are kept very general so that they are not limited to WCET information. Rather, it is possible to use our structures also for multi-objective optimization of e.g. best-case execution time (BCET) or energy dissipation

Fine-Grain Iterative Compilation for WCET Estimation

Compiler optimizations, although reducing the execution times of programs, raise issues in static WCET estimation techniques and tools. Flow facts, such as loop bounds, may not be automatically found by static WCET analysis tools after aggressive code optimizations. In this paper, we explore the use of iterative compilation (WCET-directed program optimization to explore the optimization space), with the objective to (i) allow flow facts to be automatically found and (ii) select optimizations that result in the lowest WCET estimates. We also explore to which extent code outlining helps, by allowing the selection of different optimization options for different code snippets of the application

Towards a Time-predictable Dual-Issue Microprocessor: The Patmos Approach

Current processors are optimized for average case performance, often leading to a high worst-case execution time (WCET). Many architectural features that increase the average case performance are hard to be modeled for the WCET analysis. In this paper we present Patmos, a processor optimized for low WCET bounds rather than high average case performance. Patmos is a dual-issue, statically scheduled RISC processor. The instruction cache is organized as a method cache and the data cache is organized as a split cache in order to simplify the cache WCET analysis. To fill the dual-issue pipeline with enough useful instructions, Patmos relies on a customized compiler. The compiler also plays a central role in optimizing the application for the WCET instead of average case performance

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

Static analysis of energy consumption for LLVM IR programs

Energy models can be constructed by characterizing the energy consumed by executing each instruction in a processor's instruction set. This can be used to determine how much energy is required to execute a sequence of assembly instructions, without the need to instrument or measure hardware. However, statically analyzing low-level program structures is hard, and the gap between the high-level program structure and the low-level energy models needs to be bridged. We have developed techniques for performing a static analysis on the intermediate compiler representations of a program. Specifically, we target LLVM IR, a representation used by modern compilers, including Clang. Using these techniques we can automatically infer an estimate of the energy consumed when running a function under different platforms, using different compilers. One of the challenges in doing so is that of determining an energy cost of executing LLVM IR program segments, for which we have developed two different approaches. When this information is used in conjunction with our analysis, we are able to infer energy formulae that characterize the energy consumption for a particular program. This approach can be applied to any languages targeting the LLVM toolchain, including C and XC or architectures such as ARM Cortex-M or XMOS xCORE, with a focus towards embedded platforms. Our techniques are validated on these platforms by comparing the static analysis results to the physical measurements taken from the hardware. Static energy consumption estimation enables energy-aware software development, without requiring hardware knowledge

TASKers: A Whole-System Generator for Benchmarking Real-Time-System Analyses

Implementation-based benchmarking of timing and schedulability analyses requires system code that can be executed on real hardware and has defined properties, for example, known worst-case execution times (WCETs) of tasks. Traditional approaches for creating benchmarks with such characteristics often result in implementations that do not resemble real-world systems, either due to work only being simulated by means of busy waiting, or because tasks have no control-flow dependencies between each other. In this paper, we address this problem with TASKers, a generator that constructs realistic benchmark systems with predefined properties. To achieve this, TASKers composes patterns of real-world programs to generate tasks that produce known outputs and exhibit preconfigured WCETs when being executed with certain inputs. Using this knowledge during the generation process, TASKers is able to specifically introduce inter-task control-flow dependencies by mapping the output of one task to the input of another

Is Time Predictability Quantifiable?

Abstract—Computer architects and researchers in the realtime domain start to investigate processors and architectures optimized for real-time systems. Optimized for real-time systems means time predictable, i.e., architectures where it is possible to statically derive a tight bound of the worst-case execution time. To compare different approaches we would like to quantify time predictability. That means we need to measure time predictability. In this paper we discuss the different approaches for these measurements and conclude that time predictability is practically not quantifiable. We can only compare the worst-case execution time bounds of different architectures. I

When the worst-case execution time estimation gains from the application semantics

International audienceCritical embedded systems are generally composed of repetitive tasks that must meet drastic timing constraints, such as termination deadlines. Providing an upper bound of the worst-case execution time (WCET) of such tasks at design time is thus necessary to prove the correctness of the system. Static timing analysis methods compute safe WCET upper bounds, but at the cost of a potentially large over-approximation. Over-approximation may come from the fact that WCET analysis may consider as potential worst-cases some executions that are actually infeasible, because of the semantics of the program and/or because they correspond to unrealistic inputs. In this paper, we introduce a complete semantic-aware WCET estimation workflow. We introduce some program analysis to find infeasible paths: they can be performed at design, C or binary level, and may take into account information provided by the user. We design an annotation-aware compilation process that enables to trace the infeasible path properties through the program transformations performed by the compilers. Finally, we adapt the WCET estimation tool to take into account the kind of annotations produced by the workflow

WCET-aware Software Based Cache Partitioning for Multi-Task Real-Time Systems

Caches are a source of unpredictability since it is very difficult to predict if a memory access results in a cache hit or miss. In systems running multiple tasks steered by a preempting scheduler, it is even impossible to determine the cache behavior since interrupt-driven schedulers lead to unknown points of time for context switches. Partitioned caches are already used in multi-task environments to increase the cache hit ratio by avoiding mutual eviction of tasks from the cache. For real-time systems, the upper bound of the execution time is one of the most important metrics, called the Worst-Case Execution Time (WCET). In this paper, we use partitioning of instruction caches as a technique to achieve tighter WCET estimations since tasks can not be evicted from their partition by other tasks. We propose a novel WCET-aware cache partitioning algorithm, which determines the optimal partition size for each task with focus on decreasing the system\u27s WCET for a given set of possible partition sizes. Employing this algorithm, we are able to decrease the WCET depending on the number of tasks in a set by up to 34%. On average, reductions between 12% and 19% can be achieved