Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Parallel Processing Design for LTE PUSCH Demodulation and Decoding Based on Multi-Core Processor

The Long Term Evolution (LTE) system imposes high requirements for dispatching delay.Moreover,very large air interface rate of LTE requires good processing capability for the devices processing the baseband signals.Consequently,the single-core processor cannot meet the requirements of LTE system.This paper analyzes how to use multi-core processors to achieve parallel processing of uplink demodulation and decoding in LTE systems and designs an approach to parallel processing.The test results prove that this approach works quite well.

A heuristic mixed real-time task allocation of virtual utilization in multi-core processor

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft,and various types of real-time tasks are dynamically added at runtime.In order to improve the utilization of multi-core processors and ensure the real-time performance of the system,it is necessary to adopt a reasonable real-time task allocation method,but the existing methods are only for single-core processors or the performance is too low to be applicable.Aiming at the task allocation problem when mixed real-time tasks are dynamically added,we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF(Virtual Utilization Worst Fit)in multi-core processor.First,a 4-tuple task model is established to describe the fixedpoint task and the sporadic task in a unified manner.Then,a VDS(Virtual Deferral Server)for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is derived.Finally,combined with the analysis of VDS's capacity,VU-WF is proposed,which selects cores in ascending order of virtual utilization for the schedulability test.Experiments show that the overall performance of VU-WF is better than available algorithms,not only has a good schedulable ratio and load balancing but also has the lowest runtime overhead.In a 4-core processor,compared with available algorithms of the same schedulability ratio,the load balancing is improved by 73.9%,and the runtime overhead is reduced by 38.3%.In addition,we also develop a visual multi-core mixed task scheduling simulator RT-MCSS(open source)to facilitate the design and verification of multi-core scheduling for users.As the high performance,VU-WF can be widely used in resource-constrained and safety-critical real-time systems,such as spacecraft,self-driving cars,industrial robots,etc.

System Architecture of Godson-3 Multi-Core Processors

Godson-3 is the latest generation of Godson microprocessor family. It takes a scalable multi-core architecture with hardware support for accelerating applications including X86 emulation and signal processing. This paper introduces the system architecture of Godson-3 from various aspects including system scalability, organization of memory hierarchy, network-on-chip, inter-chip connection and I/O subsystem.

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Modern shared-memory multi-core processors typically have shared Level 2(L2)or Level 3(L3)caches.Cache bottlenecks and replacement strategies are the main problems of such architectures,where multiple cores try to access the shared cache simultaneously.The main problem in improving memory performance is the shared cache architecture and cache replacement.This paper documents the implementation of a Dual-Port Content Addressable Memory(DPCAM)and a modified Near-Far Access Replacement Algorithm(NFRA),which was previously proposed as a shared L2 cache layer in a multi-core processor.Standard Performance Evaluation Corporation(SPEC)Central Processing Unit(CPU)2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer.Results show improved performance of the multicore processor’s DPCAM and NFRA algorithms,corresponding to a higher number of concurrent accesses to shared memory.The new architecture significantly increases system throughput and records performance improvements of up to 8.7%on various types of SPEC 2006 benchmarks.The miss rate is also improved by about 13%,with some exceptions in the sphinx3 and bzip2 benchmarks.These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.

Parallel computing of discrete element method on multi-core processors

This paper describes parallel simulation techniques for the discrete element method （DEM） on multi-core processors. Recently, multi-core CPU and GPU processors have attracted much attention in accelerating computer simulations in various fields. We propose a new algorithm for multi-thread parallel computation of DEM, which makes effective use of the available memory and accelerates the computation. This study shows that memory usage is drastically reduced by using this algorithm. To show the practical use of DEM in industry, a large-scale powder system is simulated with a complicated drive unit. We compared the performance of the simulation between the latest GPU and CPU processors with optimized programs for each processor. The results show that the difference in performance is not substantial when using either GPUs or CPUs with a multi-thread parallel algorithm. In addition, DEM algorithm is shown to have high scalabilitv in a multi-thread parallel computation on a CPU.

Schedule refinement for homogeneous multi-core processors in the presence of manufacturing-caused heterogeneity

Multi-core homogeneous processors have been widely used to deal with computation-intensive embedded applications. However, with the continuous down scaling of CMOS technology, within-die variations in the manufacturing process lead to a significant spread in the operating speeds of cores within homogeneous multi-core processors. Task scheduling approaches, which do not consider such heterogeneity caused by within-die variations,can lead to an overly pessimistic result in terms of performance. To realize an optimal performance according to the actual maximum clock frequencies at which cores can run, we present a heterogeneity-aware schedule refining(HASR) scheme by fully exploiting the heterogeneities of homogeneous multi-core processors in embedded domains.We analyze and show how the actual maximum frequencies of cores are used to guide the scheduling. In the scheme,representative chip operating points are selected and the corresponding optimal schedules are generated as candidate schedules. During the booting of each chip, according to the actual maximum clock frequencies of cores, one of the candidate schedules is bound to the chip to maximize the performance. A set of applications are designed to evaluate the proposed scheme. Experimental results show that the proposed scheme can improve the performance by an average value of 22.2%, compared with the baseline schedule based on the worst case timing analysis. Compared with the conventional task scheduling approach based on the actual maximum clock frequencies, the proposed scheme also improves the performance by up to 12%.

Energy Efficiency of a Multi-Core Processor by Tag Reduction

We consider the energy saving problem for caches on a multi-core processor. In the previous research on low power processors, there are various methods to reduce power dissipation. Tag reduction is one of them. This paper extends the tag reduction technique on a single-core processor to a multi-core processor and investigates the potential of energy saving for multi-core processors. We formulate our approach as an equivalent problem which is to find an assignment of the whole instruction pages in the physical memory to a set of cores such that the tag-reduction conflicts for each core can be mostly avoided or reduced. We then propose three algorithms using different heuristics for this assignment problem. We provide convincing experimental results by collecting experimental data from a real operating system instead of the traditional way using a processor simulator that cannot simulate operating system functions and the full memory hierarchy. Experimental results show that our proposed algorithms can save total energy up to 83.93% on an 8-core processor and 76.16% on a 4-core processor in average compared to the one that the tag-reduction is not used for. They also significantly outperform the tag reduction based algorithm on a single-core processor.

Thread Private Variable Access Optimization Technique for Sunway High-Performance Multi-core Processors

The primary way to achieve thread-level parallelism on the Sunwayhigh-performance multicore processor is to use the OpenMP programming technique.To address the problem of low parallelism efficiency caused by slow accessto thread private variables in the compilation of Sunway OpenMP programs, thispaper proposes a thread private variable access technique based on privilegedinstructions. The privileged instruction-based thread-private variable access techniquecentralizes the implementation of thread-private variables at the compilerlevel, eliminating the model switching overhead of invoking OS core processingand improving the speed of accessing thread-private variables. On the Sunway1621 server platform, NPB3.3-OMP and SPEC OMP2012 achieved 6.2% and6.8% running efficiency gains, respectively. The results show that the techniquesproposed in this paper can provide technical support for giving full play to theadvantages of Sunway’s high-performance multi-core processors.

Parallel Region Reconstruction Technique for Sunway High-Performance Multi-core Processors

The leading way to achieve thread-level parallelism on the Sunwayhigh-performance multicore processors is to use OpenMP programming techniques.In order to address the problem of low parallel efficiency caused by highthread group control overhead in the compilation of Sunway OpenMP programs,this paper proposes the parallel region reconstruction technique. The parallelregion reconstruction technique expands the parallel scope of parallel regionsin OpenMP programs by parallel region merging and parallel region extending.Moreover, it reduces the number of parallel regions in OpenMP programs,decreases the overhead of frequent creation and convergence of thread groups,and converts standard fork-join model OpenMP programs to higher performanceSPMD modelOpenMP programs. On the Sunway 1621 server computer, NPB3.3-OMP and SPEC OMP2012 achieved 8.9% and 7.9% running efficiency improvementrespectively through parallel region reconstruction technique. As a result,the parallel region reconstruction technique is feasible and effective. It providestechnical support to fully exploit the multi-core parallelism advantage of Sunway’shigh-performance processors.

Adaptive data-driven parallelization of multi-view video coding on multi-core processor

Multi-view video coding （MVC） comprises rich 3D information and is widely used in new visual media, such as 3DTV and free viewpoint TV （FTV）. However, even with mainstream computer manufacturers migrating to multi-core processors, the huge computational requirement of MVC currently prohibits its wide use in consumer markets. In this paper, we demonstrate the design and implementation of the first parallel MVC system on Cell Broadband Engine^TM processor which is a state-of-the-art multi-core processor. We propose a task-dispatching algorithm which is adaptive data-driven on the frame level for MVC, and implement a parallel multi-view video decoder with modified H.264/AVC codec on real machine. This approach provides scalable speedup （up to 16 times on sixteen cores） through proper local store management, utilization of code locality and SIMD improvement. Decoding speed, speedup and utilization rate of cores are expressed in experimental results.

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

Multi-core optimization for conjugate gradient benchmark on heterogeneous processors

Developing parallel applications on heterogeneous processors is facing the challenges of 'memory wall',due to limited capacity of local storage,limited bandwidth and long latency for memory access. Aiming at this problem,a parallelization approach was proposed with six memory optimization schemes for CG,four schemes of them aiming at all kinds of sparse matrix-vector multiplication (SPMV) operation. Conducted on IBM QS20,the parallelization approach can reach up to 21 and 133 times speedups with size A and B,respectively,compared with single power processor element. Finally,the conclusion is drawn that the peak bandwidth of memory access on Cell BE can be obtained in SPMV,simple computation is more efficient on heterogeneous processors and loop-unrolling can hide local storage access latency while executing scalar operation on SIMD cores.

Proposal for sequential Stern-Gerlach experiment with programmable quantum processors

The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory.To date,the practical implementation of the sequential SG experiment has not been fully achieved.In this study,we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment.The specific parametric shallow quantum circuits,which are suitable for the limitations of current noisy quantum hardware,are given to replicate the functionality of SG devices with the ability to perform measurements in different directions.Surprisingly,it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit.With the utilization of the identical circuits,it is also feasible to implement Wheeler’s delayed-choice experiment.We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments,and the simulation results demonstrate a strong alignment with theoretical predictions.With the rapid advancement of cloud-based quantum computing,such as BAQIS Quafu,it is our belief that the proposed solution is well-suited for deployment on the cloud,allowing for public accessibility.Our findings not only expand the potential applications of quantum computers,but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.

Efficient cache replacement framework based on access hotness for spacecraft processors

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access behaviors.However,modern cache management rarely considers this fine-grained feature,which impacts the effective cache capacity of contemporary high-performance spacecraft processors.To harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was proposed.This framework reconstructs the cache replacement policy,facilitating data migration between the main cache and the auxiliary cache.Unlike traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache utilization.The evaluation shows impressive performance improvement,especially on workloads with irregular access patterns.Benefiting from fine granularity,the proposal achieves superior storage efficiency compared with commonly used cache management schemes,providing a potential optimization opportunity for modern resource-constrained processors,such as spacecraft processors.Furthermore,the framework complements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

Resources Snapshot Model for Concurrent Transactions in Multi-Core Processors

Transaction parallelism in database systems is an attractive way of improving transaction performance. There exists two levels of transaction parallelism, inter-transaction level and intra-transaction level. With the advent of multi- core processors, new hopes of improving transaction parallelism appear on the scene. The greatest execution efficiency of concurrent transactions comes from the lowest dependencies of them. However, the dependencies of concurrent transactions stand in the way of exploiting parallelism. In this paper, we present Resource Snapshot Model （RSM） for resource modeling in both levels. We propose a non-restarting scheduling algorithm in the inter-transaction level and a processor assignment algorithm in the intra-transaction level in terms of multi-core processors. Through these algorithms, execution performance of transaction streams will be improved in a parallel system with multiple heterogeneous processors that have different number of cores.

An efficient geosciences workflow on multi-core processors and GPUs:a case study for aerosol optical depth retrieval from MODIS satellite data

Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth.However,the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures.In this article,taking the aerosol optical depth(AOD)retrieval as a study case,we exploit parallel computing methods for high efficient geophysical parameter retrieval.We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer(MODIS)satellite data.According to their individual potential for parallelization,several procedures were adapted and implemented for a successful parallel execution on multicore processors and Graphics Processing Units(GPUs).The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU.To specifically address the time-consuming model retrieval part,hybrid parallel patterns which combine the multicore processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations.It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.

EIA的基于NIT和Multi-Core的面向应用调节架构

资源受限的嵌入式系统中运行的应用程序与运行于桌面系统的应用程序有显著的不同,需要考虑应用本身的需求,也要更多地关注体系结构对于计算所提供的指令级和微指令级的支持。阐述了与嵌入式Intel架构(EIA)研究有关的重要问题,提出了一个基于Intel多处理器核技术的嵌入式应用处理器解决方案,其核心思想是提供可定制的计算单元用于对特定计算的适应。这个方案与普通多核技术及可配置处理器架构的差别在于通过粗粒度并行机制获得计算资源的定制以便适应嵌入式系统计算的特殊性。

Enrichment of Fetal Nucleated Red Blood Cells by Multi-core Magnetic Composite Particles for Non-invasive Prenatal Diagnosis

A novel kind of multi-core magnetic composite particles, the surfaces of which were respectively mo- dified with goat-anti-mouse IgG and antitransferrin receptor（anti-CD71）, was prepared. The fetal nucleated red blood cells（FNRBCs） in the peripheral blood of a gravida were rapidly and effectively enriched and separated by the mo- dified multi-core magnetic composite particles in an external magnetic field. The obtained FNRBCs were used for the identification of the fetal sex by means of fluorescence in situ hybridization（FISH） technique. The results demonstrate that the multi-core magnetic composite particles meet the requirements for the enrichment and speration of FNRBCs with a low concentration and the accuracy of detetion for the diagnosis of fetal sex reached to 95%. Moreover, the obtained FNRBCs were applied to the non-invasive diagnosis of Down syndrome and chromosome 3p21 was de- tected. The above facts indicate that the novel multi-core magnetic composite particles-based method is simple, relia- ble and cost-effective and has opened up vast vistas for the potential application in clinic non-invasive prenatal diag- nosis.

Variation-Aware Task Mapping on Homogeneous Fault-Tolerant Multi-Core Network-on-Chips

A variation-aware task mapping approach is proposed for a multi-core network-on-chips with redundant cores, which includes both the design-time mapping and run-time scheduling algorithms. Firstly, a design-time genetic task mapping algorithm is proposed during the design stage to generate multiple task mapping solutions which cover a maximum range of chips. Then, during the run, one optimal task mapping solution is selected. Additionally, logical cores are mapped to physically available cores. Both core asymmetry and topological changes are considered in the proposed approach. Experimental results show that the performance yield of the proposed approach is 96% on average, and the communication cost, power consumption and peak temperature are all optimized without loss of performance yield.