SOLVERS FOR SYSTEMS OF LARGE SPARSE LINEAR AND NONLINEAR EQUATIONS BASED ON MULTI-GPUS

Numerical treatment of engineering application problems often eventually results in a solution of systems of linear or nonlinear equations.The solution process using digital computational devices usually takes tremendous time due to the extremely large size encountered in most real-world engineering applications.So,practical solvers for systems of linear and nonlinear equations based on multi graphic process units（GPUs）are proposed in order to accelerate the solving process.In the linear and nonlinear solvers,the preconditioned bi-conjugate gradient stable（PBi-CGstab）method and the Inexact Newton method are used to achieve the fast and stable convergence behavior.Multi-GPUs are utilized to obtain more data storage that large size problems need.

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.

GPP Based Open Cellular Network Towards 5G

Due to 5G's stringent and uncertainty traffic requirements,open ecosystem would be one inevitable way to develop 5G.On the other hand,GPP based mobile communication becomes appealing recently attributed to its striking advantage in flexibility and re-configurability.In this paper,both the advantages and challenges of GPP platform are detailed analyzed.Furthermore,both GPP based software and hardware architectures for open 5G are presented and the performances of real-time signal processing and power consumption are also evaluated.The evaluation results indicate that turbo and power consumption may be another challengeable problem should be further solved to meet the requirements of realistic deployments.

Feasibility study for the measurement of Bc meson mass and lifetime with the general purpose detector at the LHC

In this paper a feasibility study of the Bc meson to measure its mass and lifetime is described with the general purpose detector at the LHC. The study solely concentrated on the J/ψn^＋, J/ψ→μ^＋μ^- decay channel of the Bc and it was concluded that about 120 events can be selected in the first fb^-1 of data. With this data sample, the mass resolution was estimated to be 2.0（stat.） MeV/c^2 while the cτ resolution was found to be 13.1（stat.） p.m, i.e. the lifetime resolution to be 0.044（stat.） ps.

Exploiting Parallelism in the Simulation of General Purpose Graphics Processing Unit Program

The simulation is an important means of performance evaluation of the computer architecture. Nowadays, the serial simulation of general purpose graphics processing unit(GPGPU) architecture is the main bottleneck for the simulation speed. To address this issue, we propose the intra-kernel parallelization on a multicore processor and the inter-kernel parallelization on a multiple-machine platform. We apply these two methods to the GPGPU-sim simulator. The intra-kernel parallelization method firstly parallelizes the serial simulation of multiple compute units in one cycle. Then it parallelizes the timing and functional simulation to reduce the performance loss caused by the synchronization between different compute units. The inter-kernel parallelization method divides multiple kernels of a CUDA program into several groups and distributes these groups across multiple simulation hosts to perform the simulation. Experimental results show that the intra-kernel parallelization method achieves a speed-up of up to 12 with a maximum error rate of 0.009 4% on a 32-core machine, and the inter-kernel parallelization method can accelerate the simulation by a factor of up to 3.9 with a maximum error rate of 0.11% on four simulation hosts. The orthogonality between these two methods allows us to combine them together on multiple multi-core hosts to get further performance improvements.

SOME GENERAL ASPECTS OF ENGLISH FOR SPECIFIC PURPOSES

The teaching of English for Specific Purposes has been in existence for over 30 years,and has beenprominence for more than 20 years,and is still in the ascendant.ESP teaching accounts for a consider-able proportion of the teaching of English as a foreign language in China.It is therefore necessary forus Chinese teacher s to know more about ESP.

New atomic data for Kr XXXV useful in fusion plasma

Energy levels and emission line wavelengths of high-Z materials are useful for impurity diagnostics due to their potential application in the next generation fusion devices.For this purpose,we have calculated the fine structural energies of the 67 levels belonging to the 1s 2,1s2l,1s3l,1s4l,1s5l,and 1s6l configurations of Kr XXXV using GRASP（general purpose relativistic atomic structure package） code.Additionally,we have reported the transition probabilities,oscillator strengths,line strengths,and transition wavelengths for some electric dipole（E1） transitions among these levels.We predict new energy levels and radiative rates,which have not been reported experimentally or theoretically,forming the basis for future experimental work.

Single-particle 3D reconstruction on specialized stream architecture and comparison with GPGPUs

The wide acceptance and data deluge in medical imaging processing require faster and more efficient systems to be built.Due to the advances in heterogeneous architectures recently,there has been a resurgence in the first research aimed at FPGA-based as well as GPGPU-based accelerator design.This paper quantitatively analyzes the workload,computational intensity and memory performance of a single-particle 3D reconstruction application,called EMAN,and parallelizes it on CUDA GPGPU architectures and decouples the memory operations from the computing flow and orchestrates the thread-data mapping to reduce the overhead of off-chip memory operations.Then it exploits the trend towards FPGA-based accelerator design,which is achieved by offloading computingintensive kernels to dedicated hardware modules.Furthermore,a customized memory subsystem is also designed to facilitate the decoupling and optimization of computing dominated data access patterns.This paper evaluates the proposed accelerator design strategies by comparing it with a parallelized program on a 4-cores CPU.The CUDA version on a GTX480 shows a speedup of about 6 times.The performance of the stream architecture implemented on a Xilinx Virtex LX330 FPGA is justified by the reported speedup of 2.54 times.Meanwhile,measured in terms of power efficiency,the FPGA-based accelerator outperforms a 4-cores CPU and a GTX480 by 7.3 times and 3.4 times,respectively.

Business English Teaching Reform in Vocational Colleges in China

Since the development of China's economy and the strengthening of globalization, China's international trade is increas-ingly thriving and the total volume of trade is growing as well. The country's demand for bilingual trade talents is increasing at the same time. As the result, it is necessary to make the reform and corresponding adjustments according to the training objectives of Business English curriculum in order to cultivate job-oriented and interdisciplinary trade talents that can meet the needs of mod-ern commercial society.

Accelerating geospatial analysis on GPUs using CUDA

Inverse distance weighting (IDW) interpolation and viewshed are two popular algorithms for geospatial analysis.IDW interpolation assigns geographical values to unknown spatial points using values from a usually scattered set of known points,and viewshed identifies the cells in a spatial raster that can be seen by observers.Although the implementations of both algorithms are available for different scales of input data,the computation for a large-scale domain requires a mass amount of cycles,which limits their usage.Due to the growing popularity of the graphics processing unit (GPU) for general purpose applications,we aim to accelerate geospatial analysis via a GPU based parallel computing approach.In this paper,we propose a generic methodological framework for geospatial analysis based on GPU and its programming model Compute Unified Device Architecture (CUDA),and explore how to map the inherent parallelism degrees of IDW interpolation and viewshed to the framework,which gives rise to a high computational throughput.The CUDA-based implementations of IDW interpolation and viewshed indicate that the architecture of GPU is suitable for parallelizing the algorithms of geospatial analysis.Experimental results show that the CUDA-based implementations running on GPU can lead to dataset dependent speedups in the range of 13-33-fold for IDW interpolation and 28-925-fold for viewshed analysis.Their computation time can be reduced by an order of magnitude compared to classical sequential versions,without losing the accuracy of interpolation and visibility judgment.

A multi-scale architecture for multi-scale simulation and its application to gas-solid flows

A multi-scale hardware and software architecture implementing the EMMS （energy-minimization multi-scale） paradigm is proven to be effective in the simulation of a two-dimensional gas-solid suspension. General purpose CPUs are employed for macro-scale control and optimization, and many integrated cores （MlCs） operating in multiple-instruction multiple-data mode are used for a molecular dynamics simulation of the solid particles at the meso-scale. Many cores operating in single-instruction multiple- data mode, such as general purpose graphics processing units （GPGPUs）, are employed for direct numerical simulation of the fluid flow at the micro-scale using the lattice Boltzmann method. This architecture is also expected to be efficient for the multi-scale simulation of other comolex systems.

Reinventing Memory System Design for Many-Accelerator Architecture

The many-accelerator architecture, mostly composed of general-purpose cores and accelerator-like function units （FUs）, becomes a great alternative to homogeneous chip multiprocessors （CMPs） for its superior power-efficiency. However, the emerging many-accelerator processor shows a much more complicated memory accessing pattern than general purpose processors （GPPs） because the abundant on-chip FUs tend to generate highly-concurrent memory streams with distinct locality and bandwidth demand. The disordered memory streams issued by diverse accelerators exhibit a mutual- interference behavior and cannot be efficiently handled by the orthodox main memory interface that provides an inflexible data fetching mode. Unlike the traditional DRAM memory, our proposed Aggregation Memory System （AMS） can function adaptively to the characterized memory streams from different FUs, because it provides the FUs with different data fetching sizes and protects their locality in memory access by intelligently interleaving their data to memory devices through sub-rank binding. Moreover, AMS can batch the requests without sub-rank conflict into a read burst with our optimized memory scheduling policy. Experimental results from trace-based simulation show both conspicuous performance boost and energy saving brought by AMS.

Optimizing non-coalesced memory access for irregular applications with GPU computing

General purpose graphics processing units(GPGPUs)can be used to improve computing performance considerably for regular applications.However,irregular memory access exists in many applications,and the benefits of graphics processing units(GPUs)are less substantial for irregular applications.In recent years,several studies have presented some solutions to remove static irregular memory access.However,eliminating dynamic irregular memory access with software remains a serious challenge.A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access,especially for indirect memory access.Data reordering and index redirection are suggested to reduce the number of memory transactions,thereby improving the performance of GPU kernels.To improve the efficiency of data reordering,an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data.Through concurrently executing the compute unified device architecture(CUDA)streams of data reordering and the data processing kernel,the overhead of data reordering can be reduced.After these optimizations,the volume of memory transactions can be reduced by 16.7%-50%compared with CUSPARSE-based benchmarks,and the performance of irregular kernels can be improved by 9.64%-34.9%using an NVIDIA Tesla P4 GPU.

GPGPU Accelerated Fast Convolution Back-Projection for Radar Image Reconstruction

This paper describes a parallel fast convolution back-projection algorithm design for radar image reconstruction. State-of-the-art general purpose graphic processing units （GPGPU） were utilized to accelerate the processing. The implementation achieves much better performance than conventional processing systems, with a speedup of more than 890 times on NVIDIA Tesla C1060 supercomputing cards compared to an Intel P4 2.4 GHz CPU. 256×256 pixel images could be reconstructed within 6.3 s, which makes real-time imaging possible. Six platforms were tested and compared. The results show that the GPGPU super-computing system has great potential for radar image processing.

Optimization of Coordinated Water Resource Management in Beijing-Tianjin-Hebei Region from the Perspective of Water Accounting

Beijing-Tianjin-Hebei region is connected by geography and water veins with strong complementary advantages.The coordinated development of Beijing,Tianjin and Hebei is a major national strategy put forward by the Chinese government in the new historical period.However,Beijing,Tianjin and Hebei are faced with common problems such as water shortage,serious water pollution,over-exploitation of groundwater and fragile water ecology.The synergy governance of water resources is the basis and key issue behind the coordinated development of the tree areas.This paper analyzes the water problem in Beijing-Tianjin-Hebei region and the status of water resources accounting and discusses the basic ideas of inspecting water resources management and innovating water accounting system.In this paper,a fi ne,standardized,water accounting system is established,which is permitted for inter-sustainable use of water resources.On this basis,this paper proposes to keep in line with Australian water accounting standards and to establish General Purpose Water Accounting for disclosure of water accounting reports,based on accrual basis,so as to build a new mechanism of coordinated governance of Beijing-Tianjin-Hebei water resources.Finally,in order to demonstrate the optimization mechanism of Beijing-Tianjin-Hebei water resources coordinated management,this paper applies General Purpose Water Accounting to Beijing Waterworks Group Co.,Ltd.

Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.