Heterogeneous Multiprocessor Research Articles

Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

Research and optimization of task scheduling algorithm based on heterogeneous multi-core processor

An efficient machine learning based CPU scheduler for heterogeneous multicore processors

An efficient machine learning based CPU scheduler for heterogeneous multicore processors

Distribution of resources beyond 5G networks with heterogeneous parallel processing and graph optimization algorithms

In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively.

Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication (gemm) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm.

Fuzzy-based efficient task scheduling scheme on heterogeneous multicore processor

Fuzzy-based efficient task scheduling scheme on heterogeneous multicore processor

A BSF‐EDZL scheduling algorithm for heterogeneous multiprocessors

AbstractTo improve the performance of real‐time systems, heterogeneous asymmetric multiprocessors are proposed. Heterogeneous asymmetric multiprocessor architecture allows the system to better match computational resources to meet the needs and dynamic workload of each application. The benefits of system performance and reduced power consumption cannot be fully exploited unless proper task scheduling and task allocation methods are implemented at the operating system level.The EDZL scheduling algorithm has been shown to be a better scheduling algorithm in heterogeneous multiprocessor platforms. In this paper, the BSF‐EDZL (best speed fit for earliest deadline first until zero‐laxity) performance asymmetric multiprocessor scheduling algorithm is proposed, and an effective schedulability analysis is performed. The algorithm selects an appropriate processor for non‐zero relaxation highest priority task rather than the fastest processor when assigning tasks. Experimental results show that the proposed BSF‐EDZL scheduling algorithm can schedule a larger set of tasks than the ordinary EDZL scheduling algorithm.

A scheduling algorithm based on critical factors for heterogeneous multicore processors

SummaryAs the development of chip manufacturing technology slows down, high‐performance processors often have high energy consumption and high heat generation. Therefore, heterogeneous multi‐core processors become more and more popular, and the heterogeneous multi‐core processors is adopted to execute programs. At present, the general program consists of multiple threads. To reach goals of accelerating program execution and reducing energy consumption and heat generation of system, a suitable thread scheduling algorithm for heterogeneous multi‐core processors is needed. In this article, a thread scheduling algorithm based on multiple critical scheduling factors is proposed. First, a prediction model of thread performance and energy consumption is used to predict the core sensitivity of threads. Then, critical threads are judged and accelerated by collecting the synchronization information between threads. Finally, the load balancing method based on the computing power of cores and the core sensitivity of threads is employed to perform system load balancing, which ensures the fairness of the scheduling. Several experiments are provided, and the results show that the proposed algorithm can obtain better performance of thread schedule.

MT-office: parallel password recovery program for office on domestic heterogeneous multi-core processor

MT-office: parallel password recovery program for office on domestic heterogeneous multi-core processor

Efficient Large-Scale Virtual Screening Based on Heterogeneous Many-Core Supercomputing System.

With the rapid growth of virtual drug data- bases, the need for efficient molecular docking tools for large-scale screening is also growing. We have developed Vina@QNLM 2.0, a novel molecular docking system that leverages the logical processing units and computational processing arrays of heterogeneous multicore architecture processors. Compared to Vina@QNLM, the new version optimizes the docking speed without sacrificing accuracy. This greatly improves the scoring capability for large molecules (molecular weight > 500). Simultaneously, the new system provides enhanced support for applications such as reverse target finding through an improved parallel strategy. Vina@QNLM 2.0 achieves a speedup 20 times higher than that, using logical processing units only during a single docking process. Additionally, we successfully scaled the reverse target finding a task to 122,401 kernel groups with a robust scalability of 80.01%. In practice, we completed a reverse target-seeking for nine glycan molecules with 10,094 proteins within 1 hour.

Approx-RM: Reducing Energy on Heterogeneous Multicore processors under Accuracy and Timing Constraints

Reducing energy consumption while providing performance and quality guarantees is crucial for computing systems ranging from battery-powered embedded systems to data centers. This paper considers approximate iterative applications executing on heterogeneous multi-core platforms under user-specified performance and quality targets. We note that allowing a slight yet bounded relaxation in solution quality can considerably reduce the required iteration count and thereby can save significant amounts of energy. To this end, this paper proposes Approx-RM , a resource management scheme that reduces energy expenditure while guaranteeing a specified performance as well as accuracy target. Approx-RM predicts the number of iterations required to meet the relaxed accuracy target at run-time. The time saved generates execution-time slack, which allows Approx-RM to allocate fewer resources on a heterogeneous multi-core platform in terms of DVFS, core type, and core count to save energy while meeting the performance target. Approx-RM contributes with lightweight methods for predicting the iteration count needed to meet the accuracy target and the resources needed to meet the performance target. Approx-RM uses the aforementioned predictions to allocate just enough resources to comply with quality of service constraints to save energy. Our evaluation shows energy savings of 31.6%, on average, compared to Race-to-idle when the accuracy is only relaxed by 1%. Approx-RM incurs timing and energy overheads of less than 0.1%.

Algorithms for tree-shaped task partition and allocation on heterogeneous multiprocessors

Algorithms for tree-shaped task partition and allocation on heterogeneous multiprocessors

On-Orbit Remote Sensing Image Processing Complex Task Scheduling Model Based on Heterogeneous Multiprocessor

On-Orbit Remote Sensing Image Processing Complex Task Scheduling Model Based on Heterogeneous Multiprocessor

Federated Scheduling Optimization Scheme for Typed Tasks With Power Constraints in Heterogeneous Multicore Processor Architectures

Federated Scheduling Optimization Scheme for Typed Tasks With Power Constraints in Heterogeneous Multicore Processor Architectures

Solving of the Biological Kinetics Problem on a Heterogeneous Multiprocessor Computer System

Solving of the Biological Kinetics Problem on a Heterogeneous Multiprocessor Computer System

A new direct acyclic graph task scheduling method for heterogeneous Multi-Core processors

At present, the traditional methods of calculating the worst response time upper bounds for direct acyclic graph (DAG) task scheduling on heterogeneous multi-core platforms suffer from problems such as nonself-sustainability, too many blocking nodes, and excessive estimation of the overhead time required for task scheduling, all of which cause response time upper bound estimates to be far too pessimistic. We propose to reconstruct the DAG graph by adding execution edges in order to eliminate blocking nodes. The specific method is to first use triples to represent each node of the DAG task graph, then to select blocking nodes according to the priority rules with which we add execution edges graph to reconstruct it, and finally to remove the blocking nodes again and repeat the process until all blocking nodes have been eliminated. Our experiments show that the worst response time upper bound from this method can achieve a 20% improvement in accuracy compared to the traditional methods for calculating the worst response time upper bound.

Schedulability Analysis for Coscheduling Real-Time Tasks on Multiprocessors

The real-time coscheduling problem, where tasks may have multiple phases executing on different types of processors, is known to be hard. The (already hard) self-suspending task scheduling simplifies the coscheduling problem by assuming that the latency a task may experience on the other type of processors is naturally bounded, which is unfortunately not true in practice. In this article, we present a novel analysis technique, namely, the vertical view analysis, for analyzing the schedulability of coscheduling sporadic tasks under global earliest-deadline-first (GEDF) on a heterogeneous multiprocessor consisting of two types of processors. We derive both hard (no deadline miss) and soft (bounded response times) real-time utilization-based tests. To the best of our knowledge, these results are the first-of-its-kind for the coscheduling problem and may allow real-time schedulability analysis to be carried out on more practical scenarios under heterogeneous computing.

Expandable On-Board Real-Time Edge Computing Architecture for Luojia3 Intelligent Remote Sensing Satellite

Since the data generation rate of high-resolution satellites is increasing rapidly, to relieve the stress of data downloading and processing systems while enhancing the time efficiency of information acquisition, it is important to deploy on-board edge computing on satellites. However, the volume, weight, and computability of on-board systems are strictly limited by the harsh space environment. Therefore, it is very difficult to match the computability and the requirements of diversified intelligent applications. Currently, this problem has become the first challenge of the practical deployment of on-board edge computing. To match the actual requirements of the Luojia3 satellite of Wuhan University, this manuscript proposes a three-level edge computing architecture based on a System-on-Chip (SoC) for low power consumption and expandable on-board processing. First, a transfer level is designed to focus on hardware communications and Input/Output (I/O) works while maintaining a buffer to store image data for upper levels temporarily. Second, a processing framework that contains a series of libraries and Application Programming Interfaces (APIs) is designed for the algorithms to easily build parallel processing applications. Finally, an expandable level contains multiple intelligent remote sensing applications that perform data processing efficiently using base functions, such as instant geographic locating and data picking, stream computing balance model, and heterogeneous parallel processing strategy that are provided by the architecture. It is validated by the performance improvement experiment that following this architecture, using these base functions can help the Region of Interest (ROI) system geometric correction fusion algorithm to be 257.6 times faster than the traditional method that processes scene by scene. In the stream computing balance experiment, relying on this architecture, the time-consuming algorithm ROI stabilization production can maintain stream computing balance under the condition of insufficient computability. We predict that based on this architecture, with the continuous development of device computability, the future requirements of on-board computing could be better matched.

Examining partitioned caches performance in heterogeneous multi-core processors

Examining partitioned caches performance in heterogeneous multi-core processors

Energy-Saving Task Scheduling Based on Hard Reliability Requirements: A Novel Approach with Low Energy Consumption and High Reliability

With the increasing complexity of application situations in multi-core processing systems, how to assure task execution reliability has become a focus of scheduling algorithm research in recent years. Most fault-tolerant algorithms achieve hard reliability requirements through task redundancy, which increases energy consumption and contradicts the concept of sustainable development. In this paper, we propose a new algorithm called HDFE (Heterogeneous-Dag-task-fault-tolerance-energy-efficiency algorithm) that combines DVFS technology and task replication technology to solve the scheduling problem of DAG applications concerning energy-saving and hard reliability requirements in heterogeneous multi-core processor systems. Our algorithm is divided into three phases: the priority calculation phase, the task replication phase, and the task assignment phase. The HDFE algorithm achieved energy savings while meeting hard reliability requirements for applications, which was based on the interrelationship between reliability and energy consumption in filtering task replicas. In the experimental part of this paper, we designed four comparison experiments between the EFSRG algorithm, the HRRM algorithm, and the HDFE algorithm. The experimental results showed that the energy consumption of task scheduling using the HDFE algorithm is lower than other algorithms under different scales, thus achieving energy savings and complying with the concept of sustainable development.

Hybrid fuzzy-based Deep Remora Reinforcement Learning Based Task Scheduling in Heterogeneous Multicore-processor

In recent times, heterogeneous multicore processors have played a critical role in many applications because they provide better performance in adapting power constraints with higher flexibility. Hence, various techniques and optimal frameworks are introduced into the task scheduling of heterogeneous multicore processors, which is unique from application to application, but identifying the optimal task scheduler in a heterogeneous multicore processor is essential. A hybrid fuzzy-based deep remora reinforcement learning (HFDRRL) approach is introduced to achieve efficient multi-processor task scheduling. It will optimally perform the task scheduling process in a heterogeneous multicore processor to improve the overall system performance. Further, the solutions that fall within the local optimum are identified. The optimal weight solution for deep learning architecture is obtained using the novel bio-inspired remora optimization algorithm (ROA). The experimental analysis will be performed to analyze the performance of the improved approach and compared with existing approaches in terms of analyzing tasks execution time, energy consumption, utilization of task and task processing time. The proposed approach provides better performance in task scheduling of heterogeneous multicore processors.