RF-TSV DESIGN, MODELING AND APPLICATION FOR 3D MULTI-CORE COMPUTER SYSTEMS

The state-of-the-art multi-core computer systems are based on Very Large Scale three Dimensional (3D) Integrated circuits (VLSI). In order to provide high-speed vertical data transmission in such 3D systems, efficient Through-Silicon Via (TSV) technology is critically important. In this paper, various Radio Frequency (RF) TSV designs and models are proposed. Specifically, the Cu-plug TSV with surrounding ground TSVs is used as the baseline structure. For further improvement, the dielectric coaxial and novel air-gap coaxial TSVs are introduced. Using the empirical parameters of these coaxial TSVs, the simulation results are obtained demonstrating that these coaxial RF-TSVs can provide two-order higher of cut-off frequencies than the Cu-plug TSVs. Based on these new RF-TSV technologies, we propose a novel 3D multi-core computer system as well as new architectures for manipulating the interfaces between RF and baseband circuit. Taking into consideration the scaling down of IC manufacture technologies, predictions for the performance of future generations of circuits are made. With simulation results indicating energy per bit and area per bit being reduced by 7% and 11% respectively, we can conclude that the proposed method is a worthwhile guideline for the design of future multi-core computer ICs.

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.

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.

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.

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.

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.

Theoretical Analysis on Inter-Core Crosstalk Suppression Model for Multi-Core Fiber

Decreasing mode coupling coefficient(κ) is an effective approach to suppress the inter-core crosstalk. Therefore, we deploy a low index rod and rectangle trench in the middle of two neighboring cores to reduce κ so that the overlap of electric field distribution can be suppressed. We also propose approximate analytical solution(AAS) for κ of two crosstalk suppression models, which are two cores with one low index rod deployed in the middle and two cores with one low index rectangle trench deployed in the middle. We then do some modification for the results obtained by AAS and the modified results are proved to agree well with that obtained by finite element method(FEM). Therefore, we can use the modified AAS to get inter-core crosstalk for abovementioned two models quickly.

Modeling of Few-Mode Multi-Core Optical Fiber Channel Based on Non-Uniform Mode Field Distribution

In this paper, the influencing factors that affect few-mode and multi core optical fiber channel are analyzed in a comprehensive way. The theoretical modeling and computer simulation of the information channel are carried out and then the modeling scheme of few-mode multicore optical fiber channel based on non-uniform mode field distribution is put forward. The proposed modeling scheme can not only exponentially increases the system capacity through fewmode multi-core optical fiber channel, but has better transmission performance compared to the channel of the same type to the uniform channel revealing from the simulation results.

Cache performance optimization of irregular sparse matrix multiplication on modern multi-core CPU and GPU

This paper focuses on how to optimize the cache performance of sparse matrix-matrix multiplication(SpGEMM).It classifies the cache misses into two categories;one is caused by the irregular distribution pattern of the multiplier-matrix,and the other is caused by the multiplicand.For each of them,the paper puts forward an optimization method respectively.The first hash based method removes cache misses of the 1 st category effectively,and improves the performance by a factor of 6 on an Intel 8-core CPU for the best cases.For cache misses of the 2nd category,it proposes a new cache replacement algorithm,which achieves a cache hit rate much higher than other historical knowledge based algorithms,and the algorithm is applicable on CELL and GPU.To further verify the effectiveness of our methods,we implement our algorithm on GPU,and the performance perfectly scales with the size of on-chip storage.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

The Channel Maps and the Position-Velocity Diagrams of Multi-core Structure of Cepheus C

The first important problem in the star forming process is the formation of proto star core in star forming regions of molecular cloud. The multi core structure in star forming regions is related to the forming of proto star core. The molecular radiation of C 18 O( J = 1-0) in Cepheus C has been observed. The C 18 O( J = 1-0) observations form the basis for an interesting study on the cloud cores and star formation activity in the cores of the Cepheus C. In order to study the multi core structure of C 18 O( J = 1-0) in the Cepheus C the channel maps and the position velocity diagrams of C 18 O( J = 1-0) will be shown. From the maps it is found that the contour level and distribution size of the three cores in Cepheus C are related to the channel velocity very much. The channel velocity of C 18 O( J = 1-0) molecules in core b, which distributed in all the channels velocity, is different with one in core a and core c very much. The C 18 O( J = 1-0) molecules in core a and core c of the Cepheus C mostly distributed in the blue shifted channel velocity relating to peak velocity, and only in -10.0 ～ -9.5 km/s, which is the red shifted channel velocity relating to peak velocity. And the contour level of C 18 O( J = 1-0) in -10.0 ～ -9.5 km/s is small and the distrbution size in the channel map is small. According to the position velocity diagrams the asymmetry of the distribution both blue shifted and red shifted components should reflect the asymmetry of the profile. From the diagrams it also is found that the contour level and the distribution size of the three cores are different from each other. Both results from the maps and diagrams are coincident with each other.

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.

Performance Behaviour Analysis of the Present 3-Level Cache System for Multi-Core Processors

In this paper, a study related to the expected performance behaviour of present 3-level cache system for multi-core systems is presented. For this a queuing model for present 3-level cache system for multi-core processors is developed and its possible performance has been analyzed with the increase in number of cores. Various important performance parameters like access time and utilization of individual cache at different level and overall average access time of the cache system is determined. Results for up to 1024 cores have been reported in this paper.

Performance Analysis for EDMA Based on TIC6678Multi-core DSP

Frequent data exchange among all kinds of memories has become an inevitable phenomenon in the process of modern embeddedsoftware design. In order to improve the ability of the embedded system data＇s throughput and computation, most embeddeddevices introduce Enhanced Direct Memory Access （EDMA） data transfer technology. TMS320C6678 is a multi-core DSPproduced by Texas Instruments （TI）. There are ten EDMA transmission controllers in the chip for configuration and datatransmissions are allowed to be performed between any two pieces of storage at the same time. This paper expounds the workingmechanism of EDMA based on multi-core DSP TMS320C6678. At the same time, multiple data sets are provided and thebottleneck of limiting data throughout is analyzed and solved.

An MPI parallel DEM-IMB-LBM framework for simulating fluid-solid interaction problems

The high-resolution DEM-IMB-LBM model can accurately describe pore-scale fluid-solid interactions,but its potential for use in geotechnical engineering analysis has not been fully unleashed due to its prohibitive computational costs.To overcome this limitation,a message passing interface(MPI)parallel DEM-IMB-LBM framework is proposed aimed at enhancing computation efficiency.This framework utilises a static domain decomposition scheme,with the entire computation domain being decomposed into multiple subdomains according to predefined processors.A detailed parallel strategy is employed for both contact detection and hydrodynamic force calculation.In particular,a particle ID re-numbering scheme is proposed to handle particle transitions across sub-domain interfaces.Two benchmarks are conducted to validate the accuracy and overall performance of the proposed framework.Subsequently,the framework is applied to simulate scenarios involving multi-particle sedimentation and submarine landslides.The numerical examples effectively demonstrate the robustness and applicability of the MPI parallel DEM-IMB-LBM framework.

Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

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.

A Scalable Interconnection Scheme in Many-Core Systems

Recent architectures of multi-core systems may have a relatively large number of cores that typically ranges from tens to hundreds;therefore called many-core systems.Such systems require an efficient interconnection network that tries to address two major problems.First,the overhead of power and area cost and its effect on scalability.Second,high access latency is caused by multiple cores’simultaneous accesses of the same shared module.This paper presents an interconnection scheme called N-conjugate Shuffle Clusters(NCSC)based on multi-core multicluster architecture to reduce the overhead of the just mentioned problems.NCSC eliminated the need for router devices and their complexity and hence reduced the power and area costs.It also resigned and distributed the shared caches across the interconnection network to increase the ability for simultaneous access and hence reduce the access latency.For intra-cluster communication,Multi-port Content Addressable Memory(MPCAM)is used.The experimental results using four clusters and four cores each indicated that the average access latency for a write process is 1.14785±0.04532 ns which is nearly equal to the latency of a write operation in MPCAM.Moreover,it was demonstrated that the average read latency within a cluster is 1.26226±0.090591 ns and around 1.92738±0.139588 ns for read access between cores from different clusters.

Comparative Evaluation of Data Mining Algorithms in Breast Cancer

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer.The only method to avoid breast cancer-related deaths is through early detection and treatment.The proper classification of malignancies is one of the most significant challenges in the medical industry.Due to their high precision and accuracy,machine learning techniques are extensively employed for identifying and classifying various forms of cancer.Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on.This article introduces several techniques,including support vector machine(SVM),K star(K∗)classifier,Additive Regression(AR),Back Propagation Neural Network(BP),and Bagging.These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients.Comparing the results,the author found that Support Vector Machine and Bagging had the highest precision and accuracy,respectively.Also,assess the number of studies that provide machine learning techniques for breast cancer detection.