Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for燞omogeneous MPSoCs

Distributed architectures with fixed-priority scheduling using Dynamic Power Management (DPM) for CPU optimization are one of the serious concerns in INTEL PXA270. Increasing the number of transistors and task mapping on chips causes greater energy dissipation and power consumption. This study addresses the issue of system-level higher CPU energy dissipation during the execution of parallel workloads with common deadlines by introducing a framework that includes task migration based on DPM and an Adaptive Deadline First scheduling (A-DF) scheme to properly schedule migratable tasks. The DPM policy and efficient task allocation and scheduling using A-DF enhance overall throughput and optimize energy consumption to avoid delays and performance degradation in multiprocessor systems. The proposed model assigns processors to the ready task set to meet deadline requirements. A full task migration policy is also integrated to ensure proper task mapping and interprocess linkage among tasks with the same deadlines. The execution of a task can pause on one CPU and reschedule execution on another to avoid delay and ensure that the deadline is met. The proposed method shifts the context of the task from running to sleep and from idle to sleep using an adaptive DPM approach. The proposed scheme showed a promising reduction in energy dissipation compared to other conventional energy-aware task migration techniques. Simulations were conducted using a super pipelined microarchitecture Intel XScale PXA270 using instruction and data cache per core of 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors (ui) of 18% and 20%. The proposed approach consumed 6.3% less energy and achieved 2.1% and 2.4% improvements in terms of accuracy and precision when almost half of the CPU is running, and on a lower workload consumed 1.04% less energy. The proposed design provided significant improvements in clock rates of 100, 104, and 116 MHz.

System-level dynamic power management (DPM) schemes in Multiprocessor System on Chips (MPSoCs) exploit the idleness of processors to reduce the energy consumption by putting idle processors to low-power states. In the presence of multiple low-power states, the challenge is to predict the duration of the idle period with high accuracy so that the most beneficial power state can be selected for the idle processor. In this work, we propose a novel dynamic power management scheme for adaptive pipelined MPSoCs, suitable for multimedia applications. We leverage application knowledge in the form of future workload prediction to forecast the duration of idle periods. The predicted duration is then used to select an appropriate power state for the idle processor. We proposed five heuristics as part of the DPM and compared their effectiveness using an MPSoC implementation of the H.264 video encoder supporting HD720p at 30 fps. The results show that one of the application prediction based heuristic (MAMAPBH) predicted the most beneficial power states for idle processors with less than 3% error when compared to an optimal solution. In terms of energy savings, MAMAPBH was always within 1% of the energy savings of the optimal solution. When compared with a naive approach (where only one of the possible power states is used for all the idle processors), MAMAPBH achieved up to 40% more energy savings with only 0.5% degradation in throughput. These results signify the importance of leveraging application knowledge at system-level for dynamic power management schemes.

Embedded System plays a vital role in consumer Industry. Complex applications need systems which contains multiple heterogeneous processors, running in parallel to speed up the system. Also due to area constraints, the processors are evolved in a single System on Chip called Multiprocessor System on Chip (MPSoC). The system should be reusable and debuggable, hence the designers designed and developed Reconfigurable MPSoCs rather than Application Specific Integrated Circuits (ASIC) in Field Programmable Gate Arrays (FPGA). Multiprocessor System on Chip (MPSoC) platform plays a vital role in parallel processor architecture design. However the growth of number of processing elements in one chip, task decomposition and scheduling become major bottlenecks of MPSoC architecture. To execute the applications, the application software is splitted as tasks and mapped to the different available processors and scheduled the tasks as when to execute in the available processors when the resources are ready. Selection of most suitable candidates for execution in a particular processor is very much important. Hardware related tasks are executed in different hardware accelerators and software tasks in processors. The area occupied by the schedulers in memory is more in internal memory. For scheduling these tasks, a programmable hardware is developed as hardware scheduler in the reconfigurable MPSoC using NIOS II processor. The algorithm for optimized scheduling in the target architecture is proposed. The literature survey is made with the hardware scheduler and new target MPSoC architecture. Quartus II version 12.1 and SOPC Builder are used to configure the NIOS II processer. Nios II EDS software tool has been used to build the application code.

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.

Multiprocessor systems on chips (MPSoCs) are envisioned as the future of embedded platforms such as game-engines, smart-phones and palmtop computers. One of the main challenge preventing the widespread diffusion of these systems is the efficient mapping of multitask multimedia applications on processing elements. Dynamic solutions based on task migration has been recently explored to perform run-time reallocation of task to maximize performance and optimize energy consumption. Even if task migration can provide high flexibility, its overhead must be carefully evaluated when applied to soft real-time applications. In fact, these applications impose deadlines that may be missed during the migration process. In this paper we first present a middleware infrastructure supporting dynamic task allocation for NUMA architectures. Then we perform an extensive characterization of its impact on multimedia soft real-time applications using a software FM Radio benchmark.

As feature sizes decrease, power dissipation and heat generation density exponentially increase. Thus, temperature gradients in Multiprocessor Systems on Chip (MPSoCs) can seriously impact system performance and reliability. Thermal balancing policies based on task migration have been proposed to modulate power distribution between processing cores to achieve temperature flattening. However, in the context of MPSoC for multimedia streaming computing, where timeliness is critical, the impact of migration on quality of service must be carefully analyzed. In this paper we present the design and implementation of a lightweight thermal balancing policy that reduces on-chip temperature gradients via task migration. This policy exploits run-time temperature and load information to balance the chip temperature. Moreover, we assess the effectiveness of the proposed policy for streaming computing architectures by analyzing deadlines misses and architectural thermal effects of task migration using a cycle-accurate thermal-aware emulation infrastructure. Our results using a real-life software defined radio multitask benchmark show that our policy achieves thermal balancing while keeping migration costs bounded.

As feature sizes decrease, power dissipation and heat generation density exponentially increase. Thus, temperature gradients in multiprocessor systems on chip (MPSoCs) can seriously impact system performance and reliability. Thermal balancing policies based on task migration have been proposed to modulate power distribution between processing cores to achieve temperature flattening. However, in the context of MPSoC for multimedia streaming computing, where timeliness is critical, the impact of migration on quality of service must be carefully analyzed. In this paper we present the design and implementation of a lightweight thermal balancing policy that reduces on-chip temperature gradients via task migration. This policy exploits run-time temperature and load information to balance the chip temperature. Moreover, we assess the effectiveness of the proposed policy for streaming computing architectures using a cycle-accurate thermal-aware emulation infrastructure. Our results using a real-life software defined radio multitask benchmark show that our policy achieves thermal balancing while keeping migration costs bounded.

Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average ~30% total energy improvement. Additionally, it maintains high QoS and robustness for real-time applications.

Battery life-time extension is a primary design objective for portable systems. Traditionally, battery life-time has been prolonged mainly by reducing average power consumption of system components. A careful analysis of discharge characteristics and the adoption of accurate high-level battery models in system-level design open new opportunities for life-time extension. In this paper, we introduce dynamic power management (DPM) policies specifically tailored to battery-powered systems. Battery-driven DPM strives to enhance life-time by automatically adapting discharge rate and current profiles to battery state-of-charge. The distinctive feature of these policies is the control of system operation based on the observation of battery output voltage. The effectiveness of the proposed policies and, more in general, of the idea of accounting for battery behavior during system design, is proved by the experiments carried out on a realistic case study, namely, an MP3 audio player.

With technology scaling, Multiprocessor System on Chip (MPSoC) which consist of multiple processors connected via a Network on Chip (NoC) have become prevalent. Applications are mapped to MPSoC's by representing it in the form of a task graph. Task scheduling involves mapping task to processor cores so as to meet the deadline. For a given deadline, slack at each node is defined by the amount of time by which a task execution can be delayed without missing the deadline. With increase in the number of cores and high application parallelism, NoC is becoming a bottleneck due to the presence of large number of concurrent communications. Increasing network resources (links and routers) reduces the communication time but the area and power goes up. In this paper we present an application aware heuristic to synthesize a minimal network connecting a set of cores in an MPSoC in the presence of hard deadlines. Our approach is based on modeling communication between a pair of processors as tasks known as “Comtasks”. The network is generated by “scheduling” these comtasks onto a set of routers so as to obtain a network with minimum area which fills up the available slack. Moreover, we also identify the set of overlapping comtasks and generate a minimal network to allow the maximum set of overlapping comtasks to execute concurrently. We compared our approach with a greedy network generation heuristic and the results show 80% benefit in the router area.

The impact of the enhanced mobility of a strained-Si channel on the performance of sub-100 nm complementary metal oxide semiconductor (CMOS) circuits is investigated by the combination of device and circuit simulations, considering both velocity saturation and velocity overshoot effects. It is found that higher mobilities of strained Si become more advantageous with reducing the channel length. It is also pointed out that the increase in energy relaxation time in strained Si, in addition to higher mobility, is effective for the enhancement of the circuit performance. The evaluated performance of 50 nm strained-Si CMOS amounts to around 1.7 times that of bulk-Si CMOS.

Verification of softwarehardware hybrid systems is difficult because of the inter process communication, concurrency and synchronization and the configuration of processors. Multi-Processor Systems on Chip (MPSoC) are examples of such hybrid systems, targeted for embedded systems. We focus on the verification of MPSoC software (application running on MPSoC) and the system consisting of the application running on the MPSoC platform. We aim at generating test cases for verifying the software while overcoming the limitations of previous approaches. Metaheuristic approaches get blocked in local optima and constraint programming approaches are slow on complex programs, to satisfy given coverage criteria. We propose to combine metaheuristic and constraint programming solvers in a novel way: executing both the solvers sequentially and splitting and reducing the individual input variable domains. We also propose to generate test cases from the functional test objectives to check the proper functioning of the software running on the hardware platform. We report the results of the preliminary experiments.

Power reduction in Multiprocessor System-on-Chip (MPSoC) is a key design challenge today. In this paper, we present an approach to power reduction for homogeneous SIMD MPSoC platforms. The technique involves the use of dynamic voltage scaling and power management of voltage islands. The basic idea is to have different supply voltage for different instructions: slow instructions run at the higher voltage levels while fast instructions run at lower voltages to save on power. We use a reconfigurable MPSoC architecture, namely MorphoSys, as a vehicle to demonstrate the approach, although the methods are suitable for other SIMD MPSoC architectures. A number of image processing benchmarks are manually mapped to the hardware to demonstrate the power reduction. The results show energy savings from 12% up to 24% for the specified benchmarks using the proposed approach.

본 논문은 MPI 집합 통신 함수가 처리 레벨 (transaction level) 에서 변환된다는 가정 하에 MPI 집합 통신 중 방송 (Broadcast), 확산 (Scatter), 취합 (Gather) 함수를 최적화한 알고리즘을 제안하였다. 또한 제안하는 알고리즘이 구동되는 MPI 전용 하드웨어 엔진을 설계하였으며, 이를 OCC-MPE (Optimized Collective Communication - Message Passing Engine) 라 명명하였다. OCC-MPE는 표준 송신 모드 (standard send mode)로 점대점 통신 (point-to-point communication) 을 하며, 집합 통신 중 가장 빈번하게 사용되는 방송, 취합, 확산을 제안하는 알고리즘에 의해 전송 순서를 결정한 후 통신하여 전체 통신 완료 시간을 단축시켰다. 제안한 알고리즘들의 성능을 측정하기 위하여 OCC-MPE를 SystemC 기반의 BFM(Bus Functional Model)을 제작하였다. SystemC 기반의 시뮬레이터를 통한 성능 평가 후에 VerilogHDL을 사용하여 제안하는 OCC-MPE를 포함한 MPSoC (Multi-Processor System on a Chip)를 설계하였다. TSMC 0.18 공정으로 합성한 결과 프로세싱 노드가 4개일 때 각 OCC-MPE가 차지하는 면적은 약 1978.95 이었다. 이는 전체 시스템에서 약 4.15%를 차지하므로 비교적 작은 면적을 차지함을 확인하였다. 본 논문에서 제안하는 OCC-MPE를 MPSoC에 내장하면, 비교적 작은 하드웨어 자원의 추가로 높은 성능향상을 얻을 수 있다. In this paper, on the assumption that MPI collective communication function is converted into a group of point-to-point communication functions in the transaction level, an algorithm that optimizes broadcast, scatter and gather function among MPI collective communication is proposed. The MPI hardware engine that operates the proposed algorithm was designed, and it was named the OCC-MPE (Optimized Collective Communication Message Passing Engine). The OCC-MPE operates point-to-point communication by using the standard send mode. The transmission order is arranged according to the algorithm that proposes the most frequently used broadcast, scatter and gather functions among the collective communications, so the whole communication time is reduced. To measure the performance of the proposed algorithm, the OCC-MPE with the Bus Functional Model (BFM) based on SystemC was designed. After evaluating the performance through the BFM based on SystemC, the proposed OCC-MPE is designed by using VerilogHDL. As a result of synthesizing with the TSMC <TEX>$0.18{\mu}m$</TEX>, the gate count of each OCC-MPE is approximately 1978.95 with four processing nodes. That occupies approximately 4.15% in the whole system, which means it takes up a relatively small amount. Improved performance is expected with relatively small amounts of area increase if the OCC-MPE operated by the proposed algorithm is added to the MPSoC (Multi-Processor System on a Chip).

Pipeline of processors allow the execution of a sequential streaming program on multiple processors. However, partitioning sequential code for Multiprocessor Systems-on-Chips (MPSoCs), and then creating the MPSoC platform for the sequential code to execute, is a challenging problem. Parallelizing/pipelining statements within a control loop will improve the throughput of each iteration and the overall performance. Existing techniques, such as OpenMP, for parallelizing control loops is agnostic of the underlying MPSoC architecture, thus limiting the possibilities for further parallelisation. Previous techniques related to distribution of statements to MPSoCs considered homogeneous processors and not automated. In this paper, we propose a novel automated parallelization/ pipelining approach to synthesize a heterogeneous distributed pipelined MPSoC to improve the throughput of a loop (critical for streaming applications). An Integer Linear Programming (ILP)-based formulation to map statements to processor configurations is presented, in order to find the most suitable heterogeneous processor configurations for maximal throughput. Our approach complements state-of-the-art parallelization techniques, such as OpenMP, to further improve the performance of an application. A complete MPSoC platform, for the Tensilica framework, is automatically generated within minutes using our approach for the tested applications.