Heterogeneous Computing System Research Articles

An Innovative Task Scheduling Method Using the Knapsack Algorithm in Heterogeneous Computing Systems

An Innovative Task Scheduling Method Using the Knapsack Algorithm in Heterogeneous Computing Systems

Engineering Challenges for Developing Reliable and Secure Heterogeneous Computing Systems

Engineering Challenges for Developing Reliable and Secure Heterogeneous Computing Systems

Energy-efficiency optimization for heterogeneous computing-assisted NOMA-MEC edge AI tasks

Edge artificial intelligence (AI) is an emerging paradigm that leverages edge computing to pave the last-mile delivery of AI. To satisfy the increasing demand for high-performance computing and low latency of edge service, heterogeneous computing accelerators, especially Neural Processor Units (NPUs), are widely deployed on edge nodes. However, the edge AI heterogeneous computing system encounters the challenge of energy constraints. In this paper, we investigate the energy efficiency (EE) optimization problem in heterogeneous computing-assisted non-orthogonal multiple access mobile edge computing (NOMA-MEC) network model. Edge devices are configured with CPU-NPU heterogeneous computing processors to improve the AI task processing, and NOMA is applied to edge task offloading to enable massive connectivity. The system-centric energy efficiency maximization problem is studied. We formulate a system-centric energy efficiency maximization problem. To solve this problem, we decouple the original problem into two subproblems, i.e., the optimization problems of heterogeneous computing workload splitting and task offloading. A heuristic algorithm and binary search algorithm are proposed to optimize NOMA user pairing and task offloading delay, respectively. Furthermore, we propose a multi-objective alternative iterative algorithm to optimize system energy efficiency. Numerical results show that the proposed scheme can significantly improve the energy efficiency of heterogeneous computing-assisted NOMA-MEC networks compared to the existing baselines.

A modified method of self-recovery of distributed software in heterogeneous computer systems

The object of research is the distributed computing process in heterogeneous computer systems. The subject of the research is methods of self-healing for distributed software on heterogeneous computer systems. The goal is to increase the efficiency of distributed data processing systems with support for the functional stability of the computing process by developing a modified method of self-healing of distributed software. Tasks: to investigate the existing methods of restoring the distributed computing process, to draw conclusions about their advantages and disadvantages; on the basis of mathematical models of tasks, computing resources and existing methods of resource allocation, develop a modification of the method of self-recovery of distributed software taking into account management strategies, finding the best solution for the selected criteria, reducing energy consumption during the execution of tasks; conduct a number of experiments comparing the developed method with existing ones. Research methods are based on the use of set theory, general systems theory, and simulation modeling theory. The results of the experiments obtained during the simulation of the allocation of software tasks to computing resources in a simulated simulation environment and the simulation of the computing process during self-recovery in case of resource failures confirm the effectiveness of the proposed method. Conclusion: the application of the method in distributed computing control systems does not increase the time the system spends on performing the task in the absence of failures, at the same time, in the presence of failures, it allows to restore the functionality of the software task faster and reduces the execution time by 8–17%, and energy consumption by 7–12%. There is also an increase in efficiency with an increase in the size of the tasks and the probability of failures. The development of technologies for automated or automatic use of methods of resource allocation and self-recovery can be indicated as areas for future research.

Parallelization with load balancing of the weather scheme WSM7 for heterogeneous CPU-GPU platforms

This article provides an enhanced parallelization of the WSM7 microphysics scheme for the Weather Research and Forecasting Model (WRF). The parallelization is designed to maximize the utilization of a heterogeneous computing system consisting of CPUs, GPUs or both. Therefore the reference implementation of the WSM7 scheme is re-implemented for the heterogeneous execution model. For each time step, a dynamic load distribution is introduced which balances the computational load between the two components aiming for an overall minimum execution time. The evaluation of the parallelized implementation is done for a specific weather situation. Specifically, the precipitation of the low-pressure zone “Bernd” from July 2021 is simulated using an Intel Core i7-7700 CPU and a NVIDIA GTX 1070 GPU. The results show a speedup of up to 28.51 for the GPU version in comparison with the reference implementation. The heterogeneous dynamic load balancing increases the speedup achieved even further by introducing a distribution factor that is updated for each time step.

Bring memristive in-memory computing into general-purpose machine learning: A perspective

In-memory computing (IMC) using emerging nonvolatile devices has received considerable attention due to its great potential for accelerating artificial neural networks and machine learning tasks. As the basic concept and operation modes of IMC are now well established, there is growing interest in employing its wide and general application. In this perspective, the path that leads memristive IMC to general-purpose machine learning is discussed in detail. First, we reviewed the development timeline of machine learning algorithms that employ memristive devices, such as resistive random-access memory and phase-change memory. Then we summarized two typical aspects of realizing IMC-based general-purpose machine learning. One involves a heterogeneous computing system for algorithmic completeness. The other is to obtain the configurable precision techniques for the compromise of the precision-efficiency dilemma. Finally, the major directions and challenges of memristive IMC-based general-purpose machine learning are proposed from a cross-level design perspective.

Cross Layer Design Using HW/SW Co-Design and HLS to Accelerate Chaining in Genomic Analysis

DNA sequence analysis is a computationally intensive task. Minimap2 is a state-of-the-art software tool for third-generation sequence analysis workflow. Nearly 50% of Minimap2’s computation time is spent on what is known as the chaining step. In this paper, the chaining step is accelerated using a novel heterogeneous computing system combining an Intel FPGA-based hardware accelerator and a CPU (using HLS for the FPGA and multi-threaded software for the CPU). The system in this paper is capable of handling large realistic workloads, achieves up to ~1.35× performance improvement over the software solution running on an Intel CPU with SIMD intrinsics (Intel’s latest AVX-512), while consuming ~27% less energy. When compared to the software solution running on the CPU without SIMD intrinsics, the system performs ~1.9× faster while consuming ~38% less energy. Importantly, this work also ensures that the accuracy of the output generated is not compromised for the speed-up gained (this work only has an error rate of less than 10-6% compared to 26% in previous FPGA-based chaining step accelerator).

QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles

The Internet of Vehicles over vehicular ad hoc network is an emerging technology enabling the development of smart applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in the Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications' requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application's Quality of Service (QoS) in terms of execution time while respecting a single SLA constraint. They do not consider the impact of overlapped multi-request processing nor the vehicle's varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to baseline genetic-based, meta-heuristic Particle Swarm Optimization (PSO), random offloading, All Edge Computing (AEC), and All Cloud Computing (ACC) approaches. Results show QoS-SLA-AGA executes the requests 1.04, 1.23, 1.05, and 9.41 times faster on average compared to the PSO, random offloading, ACC, and AEC approaches respectively. Moreover, the proposed algorithm violates 49.58%, 60.36%, 16.26%, and 80.42% fewer SLAs compared to PSO, random, ACC, and AEC respectively. In contrast, the baseline genetic-based approach increases the requests' performance by 1.14 times, with 24.03% more SLA violations.

A machine learning-based resource-efficient task scheduler for heterogeneous computer systems

A machine learning-based resource-efficient task scheduler for heterogeneous computer systems

Art Installation Design and Algorithm Research Oriented to Heterogeneous Computing Architecture and Particle Swarm Algorithm

Traditional high-performance computers generally use commercial general-purpose processors. When constructing large-scale parallel computing systems, they will face many challenges in system efficiency, power consumption, system maintenance, and cost. Heterogeneous architectures have begun to become the key to constructing supercomputer systems. In order to improve the optimization efficiency of the particle swarm algorithm, based on the simplified particle swarm algorithm, an improved strategy for fusion of population information is proposed. Only using the optimal position of the individual particle and the optimal position of the population to update the particle position makes the algorithm description simpler, construct a random term that depends on the population information to increase the diversity of the population, and design an adaptive control function equation to balance the algorithm's global detection and local detection. This article aims to study the design and algorithm of art installations for heterogeneous computing architecture and particle swarm algorithm. Aiming at the two heterogeneous system components of GPU and FPGA in heterogeneous computing systems to solve the dual heterogeneous problem between various computing components and applications, a task flow model for heterogeneous computing systems is proposed, which adopts heterogeneous systems. The structure focuses on the deterministic and nondeterministic calculation methods of particle swarm algorithm and carries out parallel research algorithms of particle transport data to analyze the design of art installations. The experimental results show that by making full use of the GPU hardware storage structure to improve memory access speed and reduce cache failure, the combination of particle swarm algorithm and heterogeneous computing system enables the calculation of the carrier components to obtain 3 times the acceleration effect; through the energy analysis model, Heterogeneous computing system components can obtain at least 1.5 times performance improvement.

Comments on “IPPTS: An Efficient Algorithm for Scientific Workflow Scheduling in Heterogeneous Computing Systems”

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IPPTS</i> (Improved Predict Priority Task Scheduling) is a list scheduling algorithm that schedules task graphs on fully connected heterogeneous distributed systems, with an objective of minimizing the overall makespan (i.e., schedule length). With respect to the literature on list scheduling techniques for task graphs, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IPPTS</i> improves the task prioritization by considering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“out-degree”</i> of a task. However, we have observed that the IPPTS algorithm contains an ambiguity which introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the possibility of assigning higher priority to a task compared to its predecessors in a task graph</i> . This priority inversion <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">may lead to the generation of an incorrect schedule</i> due to the violation of precedence-constraints among tasks. In this note, we first highlight this issue using a counter example. Then, we discuss two possible ways to fix the ambiguity in the algorithm.

SMT: Software-Defined Memory Tiering for Heterogeneous Computing Systems With CXL Memory Expander

The rapid development of data-intensive technologies has driven an increasing demand for new architectural solutions with scalable, composable, and coherent computing environments. Compute Express Link (CXL), an open-standard interconnect protocol, overcomes architectural limitations by efficiently expanding memory capacity and bandwidth. In this work, we propose a power-efficient and cost-effective solution consisting of CXL-attached memory hardware and a software suite. The memory module hardware integrated with double-data-rate (DDR) dynamic random access memory and a CXL controller expands bandwidth by dozens of gigabytes per second and increases memory capacity by a few terabytes. Our software suite, scalable memory development kit, inherits and expands the traditional memory management architecture of existing Linux systems. We proved the functionality of the proposed solution by integrating renowned data centers and computing-intensive applications. The proposed CXL solution improved throughput for in-memory database and artificial intelligence applications by 1.5-fold and 1.99-fold, respectively, compared with the conventional DDR-only memory system.

MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment

With the development of current computing technology, workflow applications have become more important in a variety of fields, including research, education, health care, and scientific experimentation. A group of tasks with complicated dependency relationships constitute the workflow applications. It can be difficult to create an acceptable execution sequence while maintaining precedence constraints. Workflow scheduling algorithms (WSA) are gaining more attention from researchers as a real-time concern. Even though a variety of research perspectives have been demonstrated for WSAs, it remains challenging to develop a single coherent algorithm that simultaneously meets a variety of criteria. There is very less research available on WSA in the heterogeneous computing system. Classical scheduling techniques, evolutionary optimisation algorithms, and other methodologies are the available solution to this problem. The workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service, interdependence between tasks, and user deadlines. In this paper, an efficient meta-heuristic approach named Multi-objective Artificial Algae (MAA) algorithm is presented for scheduling scientific workflows in a hierarchical fog-cloud environment. In the first phase, the algorithm pre-processes scientific workflow and prepares two task lists. In order to speed up execution, bottleneck tasks are executed with high priority. The MAA algorithm is used to schedule tasks in the following stage to reduce execution times, energy consumption and overall costs. In order to effectively use fog resources, the algorithm also utilises the weighted sum-based multi-objective function. The proposed approach is evaluated using five benchmark scientific workflow datasets. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialised WSAs. In comparison to previous methodologies, the average results demonstrate significant improvements of about 43% in execution time, 28% in energy consumption and 10% in total cost without any trade-offs.

In‐Memory Search for Highly Efficient Image Retrieval

Finding similar images in real time plays a key role in information retrieval and serves as an indispensable function of the search engine. However, image retrieval involves massive distance computation. With the increase in image data volume and dimension, distance computation is suffering from huge power consumption and high computational complexity. Despite the remarkable advantages in energy efficiency shown by nonvolatile content addressable memory (nvCAM)‐based in‐memory search, achieving software‐comparable search accuracy remains a critical challenge under the impact of device variations and other nonideal factors. Here, a heterogeneous image retrieval system combining highly parallel in‐memory search with a high‐precision digital system is reported. Hamming distance (HD) can be calculated in situ with a few memory read operations on the memristor‐based CAM, and several similar images are fetched for further high‐precision rerank in the digital system. This heterogeneous computing system shows high energy efficiency (50×) compared to the CPU and higher search accuracy than the fully in‐memory computing method, thus alleviating the efficiency bottleneck of CPU‐based image retrieval.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system. As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan. This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks. The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.

Task Scheduling Optimization in Cloud Computing by Coronavirus Herd Immunity Optimizer Algorithm

Cloud computing is now dominant in high-performance distributed computing, offering resource polling and ondemand services over the web. So, the task scheduling problem in a cloud computing environment becomes a significant analysis space due to the dynamic demand for user services. The primary goal of scheduling tasks is to allocate tasks to processors to achieve the shortest possible makespan while respecting priority restrictions. In heterogeneous multiprocessor systems, task and schedule assignments significantly impact the system's operation. Therefore, the different processes within the heuristic-based scheduling task algorithm will lead to a different makespan on a heterogeneous computing system. Thus, a suitable algorithm for scheduling should set precedence efficiently for every subtask based on the resources required to reduce its makespan. This paper proposes a novel efficient scheduling task algorithm based on the coronavirus herd immunity optimizer algorithm to solve task scheduling problems in a cloud computing environment. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We evaluate the performance of our algorithm by applying it to three cases. The collected findings suggest that the proposed strategy successfully achieved the best solution in terms of makespan, speedup, efficiency, and throughput compared to others. Furthermore, the results demonstrate that the suggested technique beats existing methods new genetic algorithm (NGA), proposed particle swarm optimization (PPSO), whale optimization algorithm (WOA), enhanced genetic algorithm for task scheduling (EGA-TS), gravitational search algorithm (GSA), genetic algorithm (GA), and hybrid heuristic and genetic (HHG) by 22.8%, 12.3%, 8.8%, 7.3%, 7.3%, 3.4%, and 3.4% respectively according to makespan.

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

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

Retracted: International Import and Export Trade Forecasting Algorithm Based on Heterogeneous Dynamic Edge Computing System

Retracted: International Import and Export Trade Forecasting Algorithm Based on Heterogeneous Dynamic Edge Computing System

Communication-Aware Energy Consumption Model in Heterogeneous Computing Systems

Abstract Large heterogeneous computing systems are composed of conventional central processing units and graphics processing units (GPUs) where communication plays a crucial role for system performance. This paper presents an energy consumption analytical model in terms of communication perception for the communication–computing pipeline characterization of discrete GPUs systems. We propose a dynamically adaptive energy-efficient task assignment approach, which harnesses particle swarm optimization. Static energy optimization is addressed by optimal task partition granularity. The experimental results demonstrate that the communication-based energy optimization algorithms can be more energy-saving than those without communication consideration. For some application benchmarks, the energy consumption can be saved by up to 31%. This implies the potential that the energy-saving optimization methods can be incorporated in system engineering processes.

Improving GPU performance in multimedia applications through FPGA based adaptive DMA controller

Purpose Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation. Design/methodology/approach The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing. Findings This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization. Originality/value The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.