Heterogeneous Multiprocessor Systems Research Articles

Numerical Representation of Directed Acyclic Graphs for Efficient Dataflow Embedded Resource Allocation

Stream processing applications running on Heterogeneous Multi-Processor Systems on Chips (HMPSoCs) require efficient resource allocation and management, both at compile-time and at runtime. To cope with modern adaptive applications whose behavior can not be exhaustively predicted at compile-time, runtime managers must be able to take resource allocation decisions on-the-fly, with a minimum overhead on application performance. Resource allocation algorithms often rely on an internal modeling of an application. Directed Acyclic Graph (DAGs) are the most commonly used models for capturing control and data dependencies between tasks. DAGs are notably often used as an intermediate representation for deploying applications modeled with a dataflow Model of Computation (MoC) on HMPSoCs. Building such intermediate representation at runtime for massively parallel applications is costly both in terms of computation and memory overhead. In this paper, an intermediate representation of DAGs for resource allocation is presented. This new representation shows improved performance for run-time analysis of dataflow graphs with less overhead in both computation time and memory footprint. The performances of the proposed representation are evaluated on a set of computer vision and machine learning applications.

SystemC-based electronic system-level design space exploration environment for dedicated heterogeneous multi-processor systems

This work faces the problem of the Electronic System-Level (ESL) HW/SW co-design of dedicated electronic digital systems based on heterogeneous multi-processor architectures. In particular, the work presents a prototype SystemC-based environment that exploits a Design Space Exploration (DSE) approach able to suggest an HW/SW partitioning of the system specification and a mapping onto an automatically defined architecture. The descriptions of the reference HW/SW co-design methodology and the main design issues related to the developed DSE SW tools, supported by two reference use cases that allows to understand the role of the DSE step in the whole design flow, represent the core of the paper.

Affinity-Driven Modeling and Scheduling for Makespan Optimization in Heterogeneous Multiprocessor Systems

With the advent of heterogeneous multiprocessor architectures, efficient scheduling for high performance has been of significant importance. However, joint considerations of reliability, temperature, and stochastic characteristics of precedence-constrained tasks for performance optimization make task scheduling particularly challenging. In this paper, we tackle this challenge by using an affinity (i.e., probability)-driven task allocation and scheduling approach that decouples schedule lengths and thermal profiles of processors. Specifically, we separately model the affinity of a task for processors with respect to schedule lengths and the affinity of a task for processors with regard to chip thermal profiles considering task reliability and stochastic characteristics of task execution time and intertask communication time. Subsequently, we combine the two types of affinities, and design a scheduling heuristic that assigns a task to the processor with the highest joint affinity. Extensive simulations based on randomly generated stochastic and real-world applications are performed to validate the effectiveness of the proposed approach. Experiment results show that the proposed scheme can reduce the system makespan by up to 30.1% without violating the temperature and reliability constraints compared to benchmarking methods.

Projection Methods in Krylov Subspaces

The paper considers preconditioned iterative methods in Krylov subspaces for solving large systems of linear algebraic equations with sparse coefficient matrices arising in solving multidimensional boundary-value problems by finite volume or finite element methods of different orders on unstructured grids. Block versions of the weighted Cimmino methods, based on various orthogonal and/or variational approaches and realizing preconditioning functions for two-level multi-preconditioned semi-conjugate residual algorithms with periodic restarts, are proposed. At the inner iterations between restarts, additional acceleration is achieved by applying deflation methods, providing low-rank approximations of the original matrix and playing the part of an additional preconditioner. At the outer level of the Krylov process, in order to compensate the convergence deceleration caused by restricting the number of the orthogonalized direction vectors, restarted approximations are corrected by using the least squares method. Scalable parallelization of the methods considered, based on domain decomposition, where the commonly used block Jacobi–Schwarz iterative processes is replaced by the block Cimmino–Schwarz algorithm, is discussed. Hybrid programming technologies for implementing different stages of the computational process on heterogeneous multi-processor systems with distributed and hierarchical shared memory are described.

Cloud data analysis using a genetic algorithm‐based job scheduling process

AbstractData analysis plays a major role in different research applications that require a large volume of data. Cloud computing can provide computer processing resources and device‐to‐device data sharing based on user requirements. The main goal of cloud computing is to allow users and enterprise of varying capabilities to store and process data in an efficient way and to access and distribute resources. However, a crucial problem in cloud computing is job scheduling for numerous users. Prior to the implementation of job scheduling, jobs must be categorized according to degree of criticalness, privacy and time required. Based on the experimental results, the combination of tasks was successfully determined by the processor. In heterogeneous multiprocessor systems, customized job scheduling is highly critical for obtaining optimal job performance. In this paper, an evolutionary genetic algorithm was used for obtaining better results in job scheduling, thereby improving performance in the cloud system in this regard. The genetic algorithm‐based job scheduling process introduced minimizes the investment in time through effective allocation of user requests in order to enhance the overall efficiency of the system.

Verification of parallelising transformations of KPN models

Parallelising transformations of Kahn process networks (KPNs) are important mechanisms for achieving speedup for deployment on heterogeneous multiprocessor systems particularly in the domain of signal processing applications. Correctness of such parallelising transformations is crucial for their reliable applications. In this study, verification frameworks for checking correctness of sequential to KPN behavioural transformation and KPN level transformations are presented. To the best of the authors’ knowledge, these are the first such approaches for verification problems. The sequential behaviour and the KPN behaviours are both modelled as array data dependence graphs (ADDGs) and the verification problem is posed as the problem of checking of equivalence between the two ADDGs. The key aspect of the proposed scheme is to model a KPN behaviour as an ADDG. Correctness of KPN to ADDG construction method is proved. Experimental results supporting usability of this scheme are also provided.

Hybrid dual-objective parallel genetic algorithm for heterogeneous multiprocessor scheduling

Scheduling is a process of mapping resources to tasks and it’s objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time.

A MULTIOBJECTIVE APPROACH FOR REAL TIME TASK ASSIGNMENT PROBLEM IN HETEROGENEOUS MULTIPROCESSORS

Effective assignment of real-time tasks in heterogeneous multi-processor systems to achieve high performance is said to be an NP-hard problem. This paper addresses the problem of real-time task assignment in heterogeneous multiprocessor systems with the goal of maximizing the number of task assigned and decreasing the energy consumption. A heuristic-based Multi-objective Hybrid Max-Min Ant Colony Optimization algorithm (MOHMMAS) on the heterogeneous multiprocessor system is proposed to analyze the tradeoffs between resource utilization of all assigned tasks and cumulative energy consumption. Also, we have constructed pareto fronts to illustrate different task allocations, which can cause a heterogeneous multiprocessor system to consume significantly different amounts of energy. The proposed algorithm has been implemented and evaluated using randomly generated problem instances.It was found that the proposed algorithm outperforms the Multi-objective ACO (MO-ACO) in terms of number of the tasks assigned and cumulative energy consumption of all assigned tasks.

Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Energy-Aware Task Allocation for Heterogeneous Multiprocessor Systems by Using Integer Linear Programming

Multi-objective algorithms for the application mapping problem in heterogeneous multiprocessor embedded system design

Design at the Electronic System-Level tackles the increasing complexity of embedded systems by raising the level of abstraction in system specification and modeling. Two important steps in this process are evaluation of a single design configuration and design space exploration. The exponential size of the design space, along with the complex task of simulating a single design point, makes it impossible to explore the design space efficiently in almost all MPSoC design situations. In order to overcome this problem, one or both of the main steps of the design process (i.e., simulation and exploration) must be accelerated. In this paper, for the first part of the design process, high-level analytical models for application mapping and evaluation are presented in order to accelerate the evaluation of a single design configuration. In the second part of the design process, two multi-objective optimization algorithms that are based on particle swarm optimization and simulated annealing have been proposed for performing design space exploration. Considering multimedia applications as case studies, each of these methods produces a set of near-optimal points. Simulation results show that the proposed methods can lead to near-optimal design configurations with acceptable accuracy in a reasonable time.

HYSTERY: a hybrid scheduling and mapping approach to optimize temperature, energy consumption and lifetime reliability of heterogeneous multiprocessor systems

In this paper, a hybrid scheduling and mapping approach to jointly optimize performance, lifetime reliability, energy consumption and temperature of heterogeneous multiprocessor systems on chip (MPSoCs), called “HYSTERY,” is proposed. Due to the growth of dynamic behavior in modern applications of embedded systems, along with necessity of performing complicated computations to jointly optimize the main design challenges of MPSoCs, we propose a hybrid scheduling approach in this paper. The proposed approach deals with the optimization of the mentioned design challenges at the design-time through solving an optimization problem and considering load balancing in task assignment. Moreover, at the runtime, the derived static solution is applied to the system and the design metrics monitored periodically and controlled, if required, to adapt the static scheduling decisions at the runtime. Several experiments with synthetic and real-life applications demonstrate that the proposed approach can effectively optimize the design challenges and manage dynamism of execution environment. In comparison with the uncontrolled runtime scheduling approach, HYSTERY shows 20% improvement in temperature averagely, which subsequently enhance lifetime reliability and power consumption. Furthermore, HYSTERY improves the main design parameters of MPSOCs about 21% in average compared to the existing scheduling approaches.

Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Energy saving and system reliability are two crucial issues for designing modern multiprocessor systems. There has been reliability-aware power management with dynamic voltage–frequency scaling (DVFS) schemes in recent studies. However, they are limited to optimization under the impact of DVFS on energy and reliability and have not considered reducing the non-negligible leakage energy consumption. In this paper, we focus on co-management of system reliability and total energy for applications with precedence constrained tasks on heterogeneous multiprocessor real-time systems. We first investigate the impact of energy management techniques on both reliability and energy of the systems using task recovery for fault tolerance and then propose an Energy-efficient Fault-tolerant Scheduling (EFS) scheme integrated with power mode management, which can mitigate the negative impact of DVFS on system reliability. To obtain the optimal energy-efficient reliability-guaranteed scheduling for pre-mapped applications on systems considering various realistic issues, we build mixed integer linear programing formulations with the proposed EFS scheme. To address mapping and scheduling for energy-efficiency and fault-tolerance, we finally develop a framework implemented by a List-based Binary Particle Swarm Optimization algorithm. The extensive comparative evaluations for synthetic and realistic benchmarks show that our approaches outperform several related studies in terms of energy consumption and system reliability.

Noc-HMP

Scalability and performance in multicore processors for embedded and real-time systems usually don't go well each with the other. Networks on Chip (NoCs) provide scalable execution platforms suitable for such kind of embedded systems. This article presents a NoC-based Heterogeneous Multi-Processor system, called NoC-HMP, which is a scalable platform for embedded systems developed in the GALS language SystemJ. NoC-HMP uses a time-predictable TDMA-MIN NoC to guarantee latencies and communication time between the two types of time-predictable cores and can be customized for a specific performance goal through the execution strategy and scheduling of SystemJ program deployed across multiple cores. Examples of different execution strategies are introduced, explored and analyzed via measurements. The number of used cores can be minimized to achieve the target performance of the application. TDMA-MIN allows easy extensions of NoC-HMP with other cores or IP blocks. Experiments show a significant improvement of performance over a single core system and demonstrate how the addition of cores affects the performance of the designed system.

Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

Quantum-Inspired Hyper-Heuristics for Energy-Aware Scheduling on Heterogeneous Computing Systems

Power and performance tradeoff optimization is one of the most significant issues on heterogeneous multiprocessor or multicomputer systems (HMCSs) with dynamically variable voltage. In this paper, the problem is defined as energy-constrained performance optimization and performance-constrained energy optimization. Task scheduling for precedence-constrained parallel applications represented by a directed acyclic graph (DAG) in HMCSs is an NP-HARD problem. Over the last three decades, several task scheduling techniques have been developed for energy-aware scheduling. However, it is impossible for a single task scheduling technique to outperform all other techniques for all types of applications and situations. Motivated by these observations, hyper-heuristic framework is introduced. Moreover, a quantum-inspired high-level learning strategy is proposed to improve the performance of this framework. Meanwhile, a fast solution evaluation technique is designed to reduce the computational burden for each iteration step. Experimental results show that the fast solution evaluation technique can improve average algorithm search speed by 38 percent and that the proposed algorithm generally exhibits outstanding convergence performance.

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

SummaryIn this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.

A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.

Data Partitioning on Multicore and Multi-GPU Platforms Using Functional Performance Models

Heterogeneous multiprocessor systems, which are composed of a mix of processing elements, such as commodity multicore processors, graphics processing units (GPUs), and others, have been widely used in scientific computing community. Software applications incorporate the code designed and optimized for different types of processing elements in order to exploit the computing power of such heterogeneous computing systems. In this paper, we consider the problem of optimal distribution of the workload of data-parallel scientific applications between processing elements of such heterogeneous computing systems. We present a solution that uses functional performance models (FPMs) of processing elements and FPM-based data partitioning algorithms. Efficiency of this approach is demonstrated by experiments with parallel matrix multiplication and numerical simulation of lid-driven cavity flow on hybrid servers and clusters.

A hybrid solution method for CFD applications on GPU-accelerated hybrid HPC platforms

Heterogeneous multiprocessor systems, where commodity multicore processors are coupled with graphics processing units (GPUs), have been widely used in high performance computing (HPC). In this work, we focus on the design and optimization of Computational Fluid Dynamics (CFD) applications on such HPC platforms. In order to fully utilize the computational power of such heterogeneous platforms, we propose to design the performance-critical part of CFD applications, namely the linear equation solvers, in a hybrid way. A hybrid linear solver includes both one CPU version and one GPU version of code for solving a linear equations system. When a hybrid linear equation solver is invoked during the CFD simulation, the CPU portion and the GPU portion will be run on corresponding processing devices respectively in parallel according to the execution configuration. Furthermore, we propose to build functional performance models (FPMs) of processing devices and use FPM-based heterogeneous decomposition method to distribute workload between heterogeneous processing devices, in order to ensure balanced workload and optimized communication overhead. Efficiency of this approach is demonstrated by experiments with numerical simulation of lid-driven cavity flow on both a hybrid server and a hybrid cluster.

Particle Swarm Optimization based Sequential and Parallel Tasks Scheduling Model for Heterogeneous Multiprocessor Systems

Particle Swarm Optimization based Sequential and Parallel Tasks Scheduling Model for Heterogeneous Multiprocessor Systems