Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c); definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations were correct only for special case of p_i=d_

Energy-efficient scheduling for homogeneous multiprocessor systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes

Simulation of Real-Time Scheduling with Various Execution Time Models

Presented during the Work-in-Progress session (WiP session)International audienceIn this paper, we present SimSo, a simulator that aims at facilitating the design of experimental evaluations for real-time scheduling algorithms. Currently, more than twenty-five algorithms were implemented. Special attention is paid to the execution time model of tasks. We show that the worst-case execution time for experimental simulation can introduce a bias in evaluation and we discuss as a work in progress how cache effects could be taken into consideration in the simulation

Studies on Core-Based Testing of System-on-Chips Using Functional Bus and Network-on-Chip Interconnects

The tests of a complex system such as a microprocessor-based system-onchip (SoC) or a network-on-chip (NoC) are difficult and expensive. In this thesis, we propose three core-based test methods that reuse the existing functional interconnects-a flat bus, hierarchical buses of multiprocessor SoC's (MPSoC), and a N oC-in order to avoid the silicon area cost of a dedicated test access mechanism (TAM). However, the use of functional interconnects as functional TAM's introduces several new problems. During tests, the interconnects-including the bus arbitrator, the bus bridges, and the NoC routers-operate in the functional mode to transport the test stimuli and responses, while the core under tests (CUT) operate in the test mode. Second, the test data is transported to the CUT through the functional bus, and not directly to the test port. Therefore, special core test wrappers that can provide the necessary control signals required by the different functional interconnect are proposed. We developed two types of wrappers, one buffer-based wrapper for the bus-based systems and another pair of complementary wrappers for the NoCbased systems. Using the core test wrappers, we propose test scheduling schemes for the three functionally different types of interconnects. The test scheduling scheme for a flat bus is developed based on an efficient packet scheduling scheme that minimizes both the buffer sizes and the test time under a power constraint. The schedulingscheme is then extended to take advantage of the hierarchical bus architecture of the MPSoC systems. The third test scheduling scheme based on the bandwidth sharing is developed specifically for the NoC-based systems. The test scheduling is performed under the objective of co-optimizing the wrapper area cost and the resulting test application time using the two complementary NoC wrappers. For each of the proposed methodology for the three types of SoC architec .. ture, we conducted a thorough experimental evaluation in order to verify their effectiveness compared to other methods

MULTI-SCALE SCHEDULING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS

A variety of hardware platforms for signal processing has emerged, from distributed systems such as Wireless Sensor Networks (WSNs) to parallel systems such as Multicore Programmable Digital Signal Processors (PDSPs), Multicore General Purpose Processors (GPPs), and Graphics Processing Units (GPUs) to heterogeneous combinations of parallel and distributed devices. When a signal processing application is implemented on one of those platforms, the performance critically depends on the scheduling techniques, which in general allocate computation and communication resources for competing processing tasks in the application to optimize performance metrics such as power consumption, throughput, latency, and accuracy. Signal processing systems implemented on such platforms typically involve multiple levels of processing and communication hierarchy, such as network-level, chip-level, and processor-level in a structural context, and application-level, subsystem-level, component-level, and operation- or instruction-level in a behavioral context. In this thesis, we target scheduling issues that carefully address and integrate scheduling considerations at different levels of these structural and behavioral hierarchies. The core contributions of the thesis include the following. Considering both the network-level and chip-level, we have proposed an adaptive scheduling algorithm for wireless sensor networks (WSNs) designed for event detection. Our algorithm exploits discrepancies among the detection accuracy of individual sensors, which are derived from a collaborative training process, to allow each sensor to operate in a more energy efficient manner while the network satisfies given constraints on overall detection accuracy. Considering the chip-level and processor-level, we incorporated both temperature and process variations to develop new scheduling methods for throughput maximization on multicore processors. In particular, we studied how to process a large number of threads with high speed and without violating a given maximum temperature constraint. We targeted our methods to multicore processors in which the cores may operate at different frequencies and different levels of leakage. We develop speed selection and thread assignment schedulers based on the notion of a core's steady state temperature. Considering the application-level, component-level and operation-level, we developed a new dataflow based design flow within the targeted dataflow interchange format (TDIF) design tool. Our new multiprocessor system-on-chip (MPSoC)-oriented design flow, called TDIF-PPG, is geared towards analysis and mapping of embedded DSP applications on MPSoCs. An important feature of TDIF-PPG is its capability to integrate graph level parallelism and actor level parallelism into the application mapping process. Here, graph level parallelism is exposed by the dataflow graph application representation in TDIF, and actor level parallelism is modeled by a novel model for multiprocessor dataflow graph implementation that we call the Parallel Processing Group (PPG) model. Building on the contribution above, we formulated a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for chip-level MPSoC mapping of DSP systems that are represented as synchronous dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized can be parallelized. For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows --- particle swarm optimization with a mixed integer programming formulation, particle swarm optimization with a simulated annealing engine, and particle swarm optimization with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support general PAS problem which considers the actors cannot be parallelized

Parallel Architectures and Parallel Algorithms for Integrated Vision Systems

Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g., object recognition). An IVS normally involves algorithms from low level, intermediate level, and high level vision. Designing parallel architectures for vision systems is of tremendous interest to researchers. Several issues are addressed in parallel architectures and parallel algorithms for integrated vision systems

Task assignment in parallel processor systems

A generic object-oriented simulation platform is developed in order to conduct experiments on the performance of assignment schemes. The simulation platform, called Genesis, is generic in the sense that it can model the key parameters that describe a parallel system: the architecture, the program, the assignment scheme and the message routing strategy. Genesis uses as its basis a sound architectural representation scheme developed in the thesis. The thesis reports results from a number of experiments assessing the performance of assignment schemes using Genesis. The comparison results indicate that the new assignment scheme proposed in this thesis is a promising alternative to the work-greedy assignment schemes. The proposed scheme has a time-complexity less than those of the work-greedy schemes and achieves an average performance better than, or comparable to, those of the work-greedy schemes. To generate an assignment, some parameters describing the program model will be required. In many cases, accurate estimation of these parameters is hard. It is thought that inaccuracies in the estimation would lead to poor assignments. The thesis investigates this speculation and presents experimental evidence that shows such inaccuracies do not greatly affect the quality of the assignments