Parallel Real-time Applications Research Articles

Multi-criteria Analysis and Optimization in the AMPERE Ecosystem

The AMPERE project is developing the next generation of high-performance and energy efficient Cyber-Physical Systems supporting multi-criteria optimization. This paper provides the general overview of the AMPERE approach to analyse and optimize parallel real-time applications in heterogeneous platforms. More specifically, it details how parallel OpenMP programs are generated from AMALTHEA models, and the multi-criteria optimization methodology, considering the fault-tolerance, time and energy properties of the targeted applications.

Real-Time Scheduling of Conditional DAG Tasks With Intra-Task Priority Assignment

The conditional DAG task model can represent the conditional execution flows that commonly exist in many real-time parallel applications. Previous work has shown that by properly assigning the priority among vertices inside a non-conditional DAG task, we can reduce the task response time and achieve better system schedulability. This paper studies how to apply intra-task priority assignment to conditional DAG tasks. We develop a response time bound that theoretically dominates the state-of-the-art and present a novel algorithm to compute the bound in polynomial time. We further extend the proposed approach to the general setting of multiple conditional DAG tasks. Experiments with one conditional DAG task and multiple conditional DAG tasks demonstrate that our method consistently outperforms the state-of-the-art by a considerable margin.

Near-optimal energy-efficient partial-duplication task mapping of real-time parallel applications

Minimizing energy consumption, as well as meeting real-time and reliability constraints, are major goals during system deployment. When complex platforms, such as multicore architectures with DVFS, and parallel applications are considered, these goals are significantly impacted by task mapping. To minimize energy consumption, while meeting real-time and reliability constraints, this work proposes a task mapping approach to jointly solve the problem of task allocation, task scheduling, frequency assignment, and task duplication. A novel heuristic algorithm is proposed to cope with this NP-hard problem, consisting of a pruning phase, which maintains only the task configurations that satisfy reliability constraints, and a mapping phase, which minimizes total energy consumption under real-time and precedence constraints. The obtained results show that the proposed heuristic obtains near-optimal results, with low computation time, compared to optimal solvers, while it achieves better energy consumption and finds slightly more solutions compared to other heuristic approaches.

Toward Minimum WCRT Bound for DAG Tasks Under Prioritized List Scheduling Algorithms

Many modern real-time parallel applications can be modeled as a directed acyclic graph (DAG) task. Recent studies show that the worst-case response time (WCRT) bound of a DAG task can be significantly reduced when the execution order of the vertices is determined by the priority assigned to each vertex of the DAG. How to obtain the optimal vertex priority assignment, and how far from the best-known WCRT bound of a DAG task to the minimum WCRT bound are still open problems. In this article, we aim to construct the optimal vertex priority assignment and derive the minimum WCRT bound for the DAG task. We encode the priority assignment problem into an integer linear programming (ILP) formulation. To solve the ILP model efficiently, we do not involve all variables or constraints. Instead, we solve the ILP model iteratively, i.e., we initially solve the ILP model with only a few primary variables and constraints, and then at each iteration, we increment the ILP model with the variables and constraints which are more likely to derive the optimal priority assignment. Experimental work shows that our method is capable of solving the ILP model optimally without involving too many variables or constraints, e.g., for instances with 50 vertices, we find the optimal priority assignment by involving 12.67% variables on average and within several minutes on average.

Response-Time Analysis of Limited-Preemptive Sporadic DAG Tasks

Guaranteeing timing constraints for parallel real-time applications deployed on multicore platforms is challenging, especially for applications containing non-preemptive execution blocks, that suffer from priority inversions. In this article, we propose to model such applications using a sporadic directed acyclic graph (DAG) model where preemption may take place only between the nodes of a DAG task. We present a new method for response-time analysis of such tasks scheduled with the global fixed-priority scheduling policy. We show that our method outperforms the state-of-the-art techniques significantly in terms of resource utilization in experimental evaluations using both benchmark and randomly generated task sets. We also present a method to deal with global EDF scheduling, which is a new technique proposed for response time analysis of sporadic DAG tasks with non-preemptive nodes.

Computing exact WCRT for typed DAG tasks on heterogeneous multi-core processors

Heterogeneous multi-core architectures achieve high performance and energy efficiency in real-time domain towards various applications. Most real-time parallel applications on heterogeneous multi-cores can be modeled as a typed directed acyclic graph (DAG) task, where the workload of each vertex is only allowed to execute on a particular type of core. Traditional worst-case response time (WCRT) analysis methods for these applications modeled as DAG are very pessimistic and impractical, i.e., there is a big gap between existing WCRT bounds and the exact WCRT. In this paper, we propose a satisfiability modulo theories-based method to exactly analyze the WCRT of the typed DAG task scheduled upon heterogeneous multi-cores. Experimental results show that our method can significantly improve the precision of the WCRT, and thus, dramatically increase the acceptance rate in the schedulability analysis.

Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption

Cloud computing has become an important paradigm in which scalable resources such as CPU, memory, disk and IO devices can be provided to users to remotely process their applications. In a cloud computing platform, energy consumption accounts for a significant cost portion. This article thus aims to present an energy-efficient scheduling algorithm for processing a user application with a real-time requirement. This problem is formulated as a non-linear mixed integer programming problem. We start with providing an optimal closed-form solution to its relaxation problem that aims to minimize the energy consumption without considering real-time requirements. To meet real-time requirements, we propose how to adjust task placement and resource allocation by making a good tradeoff between energy consumption and task execution time. Lastly, we find two equivalent optimal resource allocation strategies once task placement has been done. We then propose to adjust the start time of task execution such that an application’s completion time can be further shortened. Experimental results on two real-case enchmarks and extensive synthetic applications demonstrate that our proposed method finds a schedule that generally has 30 and 20 percent less energy consumption than enhancement heterogeneous earliest finish time (E-HEFT) and genetic algorithm, respectively. Besides, the proposed method has a higher rate to successfully find a feasible schedule than them, and its computation time is close to E-HEFT’s, but far less than the genetic algorithm's.

Security Enhancement for Real-Time Parallel In-Vehicle Applications by CAN FD Message Authentication

Controller Area Network with Flexible Data-rate (CAN FD) is beneficial for the in-vehicle communication of Internet of Connected Vehicles (IoCVs) because of its high bandwidth and data field length. However, CAN FD lacks a security authentication mechanism, making it extremely vulnerable to masquerade attacks. This study proposes the security enhancement for a real-time parallel in-vehicle application adopting a two-stage method. The first stage obtains the lower bound of an in-vehicle application by quickly abandoning most of sequences, while the second stage enhances security by adding Message Authentication Codes (MACs) to messages taking advantage of the laxity interval from the lower bound to the deadline. Experiments with an example and the adaptive cruise control in-vehicle application show the advantage of the proposed two-stage method in increasing the total byte size of MACs.

A DVFS-Weakly Dependent Energy-Efficient Scheduling Approach for Deadline-Constrained Parallel Applications on Heterogeneous Systems

Heterogeneous computing systems are being increasingly deployed on time-critical applications, where tasks need to meet execution deadlines and the energy consumption is to be minimized. Dynamic voltage and frequency scaling (DVFS) has been widely applied for energy saving on computing devices. Unfortunately, DVFS may introduce transient errors and shorten the processor lifetime. There is also time and energy overhead when computing and making the switching. In this article, we investigate scheduling approaches—that are independent of, or weakly dependent on DVFS—for parallel real-time applications with hard deadlines running on heterogeneous computing systems. The aim is to minimise the energy consumption while keeping all deadlines satisfied. First, in the domain without DVFS, we propose a DVFS-nondependent scheduling algorithm (DNDS), which prioritises tasks of high energy consumption during reassignment with slack time. Second, we propose a DVFS-weakly dependent scheduling (DWDS) algorithm, which finds an appropriate frequency for each processor in an iterative manner. DVFS is only allowed when switching applications. Third, based on DWDS, we further propose an algorithm Fast_DWDS, which quickly converges by deploying a binary search method. Our proposed scheduling approaches are evaluated with a large number of directed acyclic graph-based applications of high, low, and random parallelism. The results show that they significantly reduce the energy cost compared to their existing counterparts, i.e., without and with DVFS, respectively, while all deadlines remain satisfied.

Safety Enhancement for Real-Time Parallel Applications in Distributed Automotive Embedded Systems: A Stable Stopping Approach

In distributed automotive embedded systems, safety issues run through the entire life cycle, and safety mechanisms for error handling are desirable for risk control. This article focuses on safety enhancement (i.e., safety mechanisms for error handling) for a safety-critical automotive application within its deadline. A stable stopping approach used for safety enhancement for an automotive application is proposed based on the static recovery mechanism provided in ISO 26262. The Stable Stopping-based Safety Enhancement (SSSE) approach is proposed by combining known backward recovery, proposed forward recovery, and proposed forward-and-backward recovery through primary-backup repetition. The stable stopping (i.e., SSSE) approach is a convergence algorithm, which means that when the reliability value reaches a steady state and the algorithm can stop. Experimental results reveal that the exposure level defined in ISO 26262 drops from E3 to E1 after using SSSE, and such improvement enables a safety guarantee of higher level.

Energy-Efficient Scheduling Algorithms for Real-Time Parallel Applications on Heterogeneous Distributed Embedded Systems

Energy consumption minimization is one of the primary design requirements for heterogeneous distributed systems. State-of-the-art algorithms are used to study the problem of minimizing the energy consumption of a real-time parallel application with precedence constrained tasks on a heterogeneous distributed system by introducing the concept of latest finish time (LFT) to reclaim the slack time based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design optimization technique. However, the use of DVFS technique alone is insufficient, and the energy consumption reduction is limited because scaling down the frequency is restricted in practice. Furthermore, these studies merely minimize energy consumption through a local energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on the fixed processor, rather than a global energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on different processors. This study solves the problem of minimizing the energy consumption of a real-time parallel application on heterogeneous distributed systems by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. Results of the experiments show that the combined NDES&GDES algorithm can save up to 36.25-55.65 percent of energy compared with state-of-the-art counterparts under different scales, parallelism, and heterogeneity degrees of parallel applications.

Tightening Contention Delays While Scheduling Parallel Applications on Multi-core Architectures

Multi-core systems are increasingly interesting candidates for executing parallel real-time applications, in avionic, space or automotive industries, as they provide both computing capabilities and power efficiency. However, ensuring that timing constraints are met on such platforms is challenging, because some hardware resources are shared between cores. Assuming worst-case contentions when analyzing the schedulability of applications may result in systems mistakenly declared unschedulable, although the worst-case level of contentions can never occur in practice. In this paper, we present two contention-aware scheduling strategies that produce a time-triggered schedule of the application’s tasks. Based on knowledge of the application’s structure, our scheduling strategies precisely estimate the effective contentions, in order to minimize the overall makespan of the schedule. An Integer Linear Programming (ILP) solution of the scheduling problem is presented, as well as a heuristic solution that generates schedules very close to ones of the ILP (5% longer on average), with a much lower time complexity. Our heuristic improves by 19% the overall makespan of the resulting schedules compared to a worst-case contention baseline.

Minimizing Energy Consumption of Real-Time Parallel Applications Using Downward and Upward Approaches on Heterogeneous Systems

The problem of minimizing the energy consumption of a real-time parallel application on a heterogeneous system has been studied recently, and slack time reclamation based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design technique has been proposed as a solution. However, the state-of-the-art algorithms merely minimize energy consumption through an “upward” approach (i.e., from exit to entry tasks) and do not apply the “downward” approach (i.e., from entry to exit tasks) to energy consumption minimization. This study solves the same problem by employing “downward” and “upward” approaches. The concepts of deadline-slack and task level are introduced to transfer the deadline of the parallel application to each task, that is, “downward” energy consumption minimization is implemented. “Upward” energy consumption minimization by reclaiming the slack time is then included to implement “downward” and “upward” energy consumption minimization with low time complexity. Results of the experiments using real parallel applications show that the proposed algorithm can generate the minimum energy consumption compared with the state-of-the-art algorithms under different real-time and scale conditions.

Supporting soft real-time parallel applications on multiprocessors

The prevalence of multicore processors has resulted in the wider applicability of parallel programming models such as OpenMP and MapReduce. A common goal of running parallel applications implemented under such models is to guarantee bounded response times while maximizing system utilization. Unfortunately, little previous work has been done that can provide such performance guarantees. In this paper, this problem is addressed by applying soft real-time scheduling analysis techniques. Analysis and conditions are presented for guaranteeing bounded response times for parallel applications under global EDF multiprocessor scheduling.

On the parallel real-time applications

We consider a system of parallel programming to implement real-time applications in the railway transport management. The choice of optimal programming system is substantiated.

Security-Aware Resource Allocation for Real-Time Parallel Jobs on Homogeneous and Heterogeneous Clusters

Security is increasingly becoming an important issue in the design of real-time parallel applications, which are widely used in the industry and academic organizations. However, existing resource allocation schemes for real-time parallel jobs on clusters generally do not factor in security requirements when making allocation and scheduling decisions. In this paper, we develop two resource allocation schemes, called task allocation for parallel applications with deadline and security constraints (TAPADS) and security-aware and heterogeneity-aware resource allocation for parallel jobs (SHARP), by taking into account applications' timing and security requirements in addition to precedence constraints. We consider two types of computing platforms: homogeneous clusters and heterogeneous clusters. To facilitate the presentation of the new schemes, we build mathematical models to describe a system framework, security overhead, and parallel applications with deadline and security constraints. The proposed schemes are applied to heuristically find resource allocations that maximize the quality of security and the probability of meeting deadlines for parallel applications running on clusters. Extensive experiments using real-world applications and traces, as well as synthetic benchmarks, demonstrate the effectiveness and practicality of the proposed schemes.

Visual environment for high performance real-time 3D reconstruction

We are carrying out a development of a whole system for processing solar images in a high performance parallel system. The main objective is to create the initial conditions for studying and forecasting the solar explosions in real time. Due to the high computational costs involved in the processing, visualization and analysis of a great amount of solar images, a high performance computer system becomes necessary to carry out the forecast of solar explosions. As a joint effort between the Department of Computer Science at Federal University of São Carlos (UFSCar), the Astrophysics Division (DAS) and Associated Laboratory for Computing and Applied Mathematics (LAC) at National Institute for Space Research - INPE, a high performance parallel system was developed with capacity to support realistic applications, involving a reasonable amount of parallel processing. The forecast of solar explosions is important as they may cause serious perturbations in terrestrial communication systems. A significant limitation for the development of parallel real-time systems is the lack of adequate programming tools, mainly for supporting the final stages of the development life cycle. This work presents a development environment, called Visual Environment for the Development of Parallel Real-Time Programs, that supports the design and implementation of parallel real-time applications executed with the support of a parallel kernel. This paper shows how this Environment was used to carry out the 3D Reconstruction of Solar Images.

A Real-Time Parallel Application:: The Detection of Gravitational Waves by a Network of Heterogeneous Workstations

We deal with the detection of gravitational chirp signals among noisy data, where the reception and the detection are piped and run in parallel. We consider the classical theory of signal detection, which yields a detector with a “bank-of-filters” structure. We investigate distributed network computing in order to implement such a detector by heterogeneous high performance workstations interconnected via an Ethernet network. The goal is to design a distributed detector running on a number of available workstations. The computation is decomposed across the workstations in such a way to minimize communications and to match the acquisition rate. Our approach is general and can be used for networks of workstations different from those used in our experimentation. We point out that the classical performance analysis seems inappropriate if applied to real-time detection by heterogeneous distributed systems, because the execution time requirements are disregarded. To take into account such constraints we characterize the algorithm, evaluate performances on different workstations, and propose a task decomposition strategy assigning the appropriateGrainto each workstation.

High-performance computing nodes for real-time parallel applications

Abstract A high-performance computing node combining the computational performance of the Digital Alpha 21066A microprocessor and the communications capability of the SGS Thomson T9000 Transputer has been developed and evaluated. The event reconstruction program used in the CPLEAR experiment at CERN has been successfully run on a small network containing these nodes and the hardware and software demonstrated to be reliable.

Toward Dynamic. Multiprocessor Real-Time Threads

Publisher Summary This chapter describes some of the research issues and solutions surrounding the development of a real-time threads package for multiprocessor systems. The package involved is based on Mach threads and has been implemented on standard UNIX platforms and on 32-node BBN Butterfly multiprocessor. It has also been used as a portable basis for the construction of parallel real-time applications and multiprocessor operating system kernels. The essential idea underlying real-time threads is that the schedulability of a thread should be guaranteed before it is actually run, not only at the time of program compilation but also at the time of thread creation. Specifically, the real-time threads package guarantees the schedulability of threads being created prior to their execution, assuming that thread deadlines, start times, and maximum execution times are known. The second main idea underlying real-time threads is that minimum functionality should be offered by the threads package itself. Specifically, when scheduling, guarantees cannot be made by the package's underlying algorithms.