学术英语论文
英语学术论文作业
Hybrid Parallel Programming on GPU Clusters
Abstract—Nowadays, NVIDIA’s CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. In this paper, we propose a hybrid parallel programming approach using hybrid CUDA and MPI programming, which partition loop iterations according to the number of C1060 GPU nodes in a GPU cluster which consists of one C1060 and one S1070. Loop iterations assigned to one MPI process are processed in parallel by CUDA run by the processor cores in the same computational node.
Keywords: CUDA, GPU, MPI, OpenMP, hybrid, parallel programming
I. INTRODUCTION
Nowadays, NVIDIA’s CU DA [1, 16] is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA [1, 16] to achieve dramatic speedups on production and research codes.
In NVDIA the CUDA chip, all to the core of hundreds of ways to construct their chips, in here we will try to use NVIDIA to provide computing equipment for parallel computing. This paper proposes a solution to not only simplify the use of hardware acceleration in conventional general purpose applications, but also to keep the application code portable. In this paper, we propose a parallel programming
approach using hybrid CUDA, OpenMP and MPI [3] programming, which partition loop iterations according to the performance weighting of multi-core [4] nodes in a cluster. Because iterations assigned to one MPI process are processed in parallel by OpenMP threads run by the processor cores in the same computational node, the number of loop iterations allocated to one computational node at each scheduling step depends on the number of processor cores in that node.
In this paper, we propose a general approach that uses performance functions to estimate performance weights for each node. To verify the proposed approach, a heterogeneous cluster and a homogeneous cluster were built. In ourimplementation, the master node also participates in computation, whereas in previous schemes, only slave nodes do computation work. Empirical results show that in heterogeneous and homogeneous clusters environments, the proposed approach improved performance over all previous schemes.
The rest of this paper is organized as follows. In Section 2, we introduce several typical and well-known self-scheduling schemes, and a famous benchmark used to analyze computer system performance. In Section 3, we define our model and describe our approach. Our system configuration is then specified in Section 4, and experimental results for three types of application program are presented. Concluding remarks and future work are given in Section 5.
II. BACKGROUND REVIEW
A. History of GPU and CUDA
In the past, we have to use more than one computer to multiple CPU parallel computing, as shown in the last chip in the history of the beginning of the show does not need a lot of computation, then gradually the need for the game and even the graphics were and the need for 3D, 3D accelerator card appeared, and gradually we began to display chip for processing, began to show separate chips, and even made a similar in their CPU chips, that is GPU. We know that GPU computing could be used to get the answers we want, but why do we choose to use the GPU This slide shows the current CPU and GPU comparison. First, we can see only a maximum of eight core CPU now, but the GPU has grown to 260 core, the core number, we'll know a lot
of parallel programs for GPU computing, despite his relatively low frequency of core, we I believe a large number of parallel computing power could be weaker than a single issue. Next, we know that there are within the GPU memory, and more access to main memory and GPU CPU GPU access on the memory capacity, we find that the speed of accessing GPU faster than CPU by 10 times, a whole worse 90GB / s, This is quite alarming gap, of course, this also means that when computing the time required to access large amounts of data can have a good GPU to improve.
CPU using advanced flow control such as branch predict or delay branch and a large cache to reduce memory access latency, and GPU's cache and a relatively small number of flow control nor his simple, so the method is to use a lot of GPU computing devices to cover up the problem of memory latency, that is, assuming an access memory GPU takes 5seconds of the time, but if there are 100 thread simultaneous access to, the time is 5 seconds, but the assumption that CPU time memory access time is 0.1 seconds, if the 100 thread access, the time is 10 seconds, therefore, GPU parallel processing can be used to hide even in access memory than CPU speed. GPU is designed such that more transistors are devoted to data processing rather than data caching and flow control, as schematically illustrated by Figure 1.
Therefore, we in the arithmetic logic by GPU advantage, trying to use NVIDIA's multi-core available to help us a lot of computation, and we will provide NVIDIA with so many core programs, and NVIDIA Corporation to provide the API of parallel programming large number of operations to carry out.
We must use the form provided by NVIDIA Corporation GPU computing to run it Not really. We can use NVIDIA CUDA, ATI CTM and apple made OpenCL (Open Computing Language), is the development of CUDA is one of the earliest and most people at this stage in the language but with the NVIDIA CUDA only supports its own graphics card, from where we You can see at this stage to use GPU graphics card with the operator of almost all of NVIDIA, ATI also has developed its own language of CTM, APPLE also proposed OpenCL (Open Computing Language), which OpenCL has been supported by NVIDIA and ATI, but ATI CTM has also given up the language of another,
by the use of the previous relationship between the GPU, usually only support single precision floating-point operations, and in science, precision is a very important indicator, therefore, introduced this year computing graphics card has to support a Double precision floating-point operations.
B. CUDA Programming
CUDA (an acronym for Compute Unified Device Architecture) is a parallel computing [2] architecture developed by NVIDIA. CUDA is the computing engine in NVIDIA graphics processing units or GPUs that is accessible to software developers through industry standard programming languages. The CUDA software stack is composed of several layers as illustrated in Figure 2: a hardware driver, an application programming interface (API) and its runtime, and two higher-level mathematical libraries of common usage, CUFFT [17] and CUBLAS [18]. The hardware has been designed to support lightweight driver and runtime layers, resulting in high performance. CUDA architecture supports a range of computational interfaces including OpenGL [9] and Direct Compute. CUDA’s parallel programming model is designed to overcome this challenge while maintaining a low learning curve for programmer familiar with standard programming languages such as C. At its core are three key abstractions – a hierarchy of thread groups, shared memories, and barrier synchronization – that are simply exposed to the programmer as a minimal set of language extensions.
These abstractions provide fine-grained data parallelism and thread parallelism, nested within coarse-grained data parallelism and task parallelism. They guide the programmer to partition the problem into coarse sub-problems that can be solved independently in parallel, and then into finer pieces that can be solved cooperatively in parallel. Such a decomposition preserves language expressivity by allowing threads to cooperate when solving each sub-problem, and at the same time enables transparent scalability since each sub-problem can be scheduled to be solved on any of the available processor cores: A compiled CUDA program can therefore execute on any number of processor cores, and only the runtime system needs to know the physical processor count.
C. CUDA Processing flow
In follow illustration, CUDA processing flow is described as Figure 3 [16]. The first step: copy data from main memory to GPU memory, second: CPU instructs the process to GPU, third: GPU execute parallel in each core, finally: copy the result from GPU memory to main memory.
III. SYSTEM HARDWARE
A.Tesla C1060 GPU Computing Processor
The NVIDIA? Tesla? C1060 transforms a workstation into a high-performance computer that outperforms a small cluster. This gives technical professionals a dedicated computing resource at their desk-side that is much faster and more energy-efficient than a shared cluster in the data center. The NVIDIA? Tesla? C1060 computing processor board which consists of 240 cores is a PCI Express 2.0 form factor computing add-in card based on the NVIDIA Tesla T10 graphics processing unit (GPU). This board is targeted as high-performance computing (HPC) solution for PCI Express systems. The Tesla C1060 [15] is capable of 933GFLOPs/s[13] of processing performance and comes standard with 4GB of GDDR3 memory at 102 GB/s bandwidth.
A computer system with an available PCI Express *16 slot is required for the Tesla C1060. For the best system bandwidth between the host processor and the Tesla C1060, it is recommended (but not required) that theTesla C1060 be installed in a PCI Express ×16 Gen2 slot. The Tesla C1060 is based on the massively parallel, many-core Tesla processor, which is coupled with the standard CUDA C Programming
[14] environment to simplify many-core programming.
B. Tesla S1070 GPU Computing System
The NVIDIA? Tesla? S1070 [12] computing system speeds the transition to energy-efficient parallel computing [2]. With 960 processor cores and a standard simplifies application development, Tesla solve the world’s most important computing challenges--more quickly and accurately. The NVIDIAComputing System is a rack-mount Tesla T10 computing processors. This system connects to one or two host systems via one or two PCI Express cables. A Host Interface Card (HIC) [5] is used
to connect each PCI Express cable to a host. The host interface cards are compatible with both PCI Express 1x and PCI 2x systems.
The Tesla S1070 GPU computing system is based on the T10 GPU from NVIDIA. It can be connected to a single host system via two PCI Express connections to that connected to two separate host systems via connection to each host. Each NVID corresponding PCI Express cable connects to GPUs in the Tesla S1070. If only one PCI connected to the Tesla S1070, only two of the GPUs will be used.
VI COCLUSIONS
In conclusion, we propose a parallel programming approach using hybrid CUDA and MPI programming, hich partition loop iterations according to the number of C1060 GPU nodes n a GPU cluster which consist of one C1060 and one S1070.
During the experiments, loop progress assigned to one MPI processor cores in the same experiments reveal that the hybrid parallel multi-core GPU currently processing with OpenMP and MPI as a powerful approach of composing high performance clusters.
V CONCLUSIONS
[2] D. G?ddeke, R. Strzodka, J. Mohd-Yusof, P. McCormick, S. uijssen,M. Grajewski, and S. Tureka, “Exploring weak scalability for EM calculations on a GPU-enhanced cluster,” Parallel Computing,
vol. 33, pp. 685-699, Nov 2007.
[3] P. Alonso, R. Cortina, F.J. Martínez-Zaldívar, J. Ranilla “Neville limination on multi- and many-core systems: OpenMP, MPI and UDA”, Jorunal of Supercomputing,
[4] Francois Bodin and Stephane Bihan, “Heterogeneous multicore arallel programming for graphics processing units”, Scientific rogramming, Volume 17, Number 4 / 2009, 325-336, Nov. 2009.
[5] Specification esla S1070 GPU Computing System
[7] Message Passing Interface (MPI)
[8] MPICH, A Portable Implementation of MPI
[9] OpenGL, D. Shreiner, M. Woo, J. Neider and T. Davis, OpenGL(R) rogramming Guide: The Official Guide to Learning OpenGL(R), Addison-Wesley, Reading, MA,
August 2005. 10] (2008) Intel 64 Tesla Linux Cluster Lincoln webpage. [Online] Available:
[11] Romain Dolbeau, Stéphane Bihan, and Fran?ois Bodin, HMPP: Alti-core Parallel Programming Environment
[12] The NVIDIA Tesla S1070 1U Computing System - Scalable Many Core Supercomputing for Data Centers
[13] Top 500 Super Computer Sites, What is Gflop/s,
[17] CUFFT, CUDA Fast Fourier Transform (FFT) library.
[18] CUBLAS, BLAS(Basic Linear Algebra Subprograms) on CUDA
GPU集群的混合并行编程
摘要——目前，NVIDIA的CUDA是一种用于编写高度并行的应用程序的通用的可扩展的并行编程模型。它提供了几个关键的抽象—线程阻塞，共享内存和屏障同步的层次结构。这在编程模式已经被证明在多线程多核GP 和透明的扩展数以百计的核心的编程上相当成功：在整个工业界和学术界的科学家已经使用CUDA实现在生产上的显着的速度提升和代码研究。在本文中，我们提出了一个使用混合CUDA和MPI编程的混合编程方法，分区循环迭代在一个GPU集群中的C1060 GPU节点的数目，其中包括在一个C1060和S1070。循环迭代分配给一个由处理器在相同的计算节点的核心运行的CUDA并行处理过的MPI进程。
关键词：CUDA，GPU，MPI，OpenMP，混合，并行编程
1.介绍
如今，NVIDIA?（英伟达?）的CUDA[1，16]是一种通用的编写高度可扩展的并行编程并行应用程序的模型。它提供了几个关键的抽象—层次的线程块，共享内存和障碍同步。这种模式已经被证实在多线程多核心GPU编程和从小规模扩展到数百个内核是非常成功的：科学家在工业界和学术界都已经使用CUDA[1，16]，生产和研发代码有实现显着的速度提升。在NVDIA 的CUDA芯片的所有的数百种方法构建自己的芯片里，在这里我们将尝试使用NVIDIA?（英伟达?）提供用于并行计算的计算设备。
本文提出了一个解决方案不仅简化在传统的硬件加速通用应用程序的使用,而且还保持应用程序代码的便携性。在这篇论文里,我们提出一种使用混合CUDA,OpenMP和MPI[3]的并行编程方法, 根据在一个集群中的性能叠加的多核[4]节点，它会分区循环迭代。因为迭代处理分配给一个MPI 进程是在并行的相同的计算节点上由OpenMP线程的处理器内核运行的，则循环迭代的次数分配给一个计算节点，每个调度步骤取决于在该节点的处理器内核的数量。
在本文中，我们提出了一种通用的方法，使用性能函数估计每个节点的性能权重。为了验证所提出的方法，我们建立了不同种类的集群和一个同构集群。在我们的实现中，主节点也参与计算，而在以往的计划，只有
从节点做计算工作。实证结果显示，在异构和同构集群环境中，提出的方法改进性能超过以往任何计划。
本文的其余部分安排如下：在第2节，我们介绍几种典型和着名的自排计划，和一个着名的用于分析计算机性能的基准系统。在第3节中，我们定义我们的模型和说明我们的方法。然后我们的系统配置的三种类型放在第4节，同时在第4节还有实验结果的应用程序。结束语和今后的工作安排在第5节。
2.背景回顾
A.GPU和CUDA的历史
在过去，我们必须使用多台计算机的多个CPU并行计算，如所示的最后一个芯片中历史开始并不需要大量的计算，然后逐渐人们有了游戏的需求，最后图形和3D，3D加速器卡的需要出现了，渐渐地，我们开始显示芯片的加工，开始表现出独立的芯片，甚至在他们的CPU芯片中做了一个类似的显示芯片，这是GPU。
我们知道，用GPU计算可以得到我们想要的答案，但为什么我们选择使用GPU？这幻灯片显示了当前CPU和GPU的比较。首先，我们可以看到最多只有八核心CPU，但是GPU已发展到260核心，从核心数量上，我们就可以知道有很多GPU上的并行程序，尽管他有个比较低频率的核心，我们认为大量的并行计算能力可能会弱于单独的一个。第二方面，我们知道，在GPU的存储器内，有更多访问主存储器的次数。对比CPU和GPU上的访问内存容量，我们发现，GPU的访问速度比CPU快10倍，CPU整体糟糕90GB/ s，这是相当惊人的差距，当然，这也意味着，当计算访问大量的数据能有一个良好的GPU来改善所需时间。
CPU采用了先进的流量控制，如分支预测或延迟分支和大容量高速缓存，以减少内存访问延迟，GPU的高速缓存和一个相对较小的数流量控制，也没有他的简单，所以这种方法是使用大量的GPU计算设备，掩盖了内存的问题，即，假设存取存储器GPU需要5秒的时间，但如果有100个线程同时获取的时间为5秒，但假设CPU时间存储器访问时间为0.1秒，如果100个线程访问时，时间是10秒。因此，GPU并行处理可以用来隐藏缺点甚至超过存取记忆体的CPU的速度。 GPU被设计，使得更多的晶体管致力于数据处理，而非数据缓存和流控制，如由图1示意性地示出。
B. CUDA编程
CUDA（统一计算设备架构的缩写）是一个由NVIDIA?（英伟达?）开发的并行计算架构。在NVIDIA图形处理单元或者GPUs中，CUDA是计算引擎，它是可以通过业界标准来使用的软件开发编程语言。CUDA软件栈几个层组成，如在图2中示出：一个硬件驱动程序，应用程序编程接口（API）和它的运行时间，两个较高级别的数学库常见的用法，CUFFT[17]和CUBLAS[18]。硬件被设计为支持轻量级的驱动程序和运行时间层，因此有高性能的表现。 CUDA架构支持一系列的计算接口包括OpenGL[9]和直接计算等。CUDA的并行编程模型是为了克服这一挑战的同时保持了较低的学习曲线，使熟悉标准编程语言（如C）的程序员便于学习。其核心是三个关键抽象—线程的层次结构团体，共享存储，和屏障同步，即以最小的语言扩展简单地接触到程序员。
C. CUDA处理流程
在后续说明，第一步：将数据从主内存拷贝到GPU内存；第二，CPU
发送指示到GPU；第三，GPU并行执行；第四，拷贝GPU内存中的结果到主内存中。
3. 系统硬件
A.Tesla C1060 GPU计算处理器
NVIDIA?（英伟达?）Tesla?系列C1060将工作站变为优于小的计算机集群的一个高性能计算机。这给了行业技术一个在自己的办公桌边一个比在数据中心的共享的群集更快，更高效的专门的计算资源。NVIDIA?（英伟达?）Tesla?系列C1060计算处理器板由240个核心组成，是一个PCI Express2.0的NVIDIA?（英伟达?）Tesla T10图形处理单元（GPU）的基础上的图形计算附加卡。此板有针对性的用于PCI Express的高性能计算（HPC）解决方案系统。特斯拉C1060[15]有933GFLOPs/秒的处理性能，标配4 GBGDDR3内存，102 GB/ s的带宽。
Tesla C1060需要一个可用的PCI Express×16插槽的计算机系统。为了获得主机的处理器和Tesla C1060之间最佳的系统带宽，建议（但不要求）是Tesla C1060安装在PCI Express×16第二代插槽。特斯拉C1060基于大规模并行，多核心的Tesla处理器，再加上标准的CUDA的 C语言编程的环境，以简化多核心编程。
B.特斯拉S1070 GPU的计算系统
NVIDIA?（英伟达?）Tesla?系列S1070[12]计算系统加快了到节能高效的并行计算的过度。拥有960个处理器内核和一个标准的C编译器，简化应用程序的开发，特斯拉S1070尺度更快，更准确的解决世界上最重要的计算难题。NVIDIA?（英伟达?）Tesla S1070计算系统是一个1U机架安装系统，它有四个特斯拉T10计算处理器。该系统通过一个或两个PCI Express的线连接到一个或两个主机系统。主机接口卡（HIC）是用来连接每个PCI Express连接到主机上。主机接口卡兼容的PCI Express1X和PCI Express2个系统。
Tesla S1070 GPU计算系统T10 NVIDIA?（英伟达?）GPU的基础上。它可以通过两个PCI Express连接到一个单独的主机系统连接到主机，或通过一个PCI Express连接到两个独立的主机系统连接到每一台主机。每NVIDIA开关和相应的PCI Express电缆连接到Tesla S1070的两个四GPU （图形处理器）。如果只有一个PCI Express电缆连接的Tesla S1070，那么只有两个GPU在使用。主机必须有两个可用的PCI Express槽和配置有两个电缆，才能连接所有在特斯拉S1070的四个GPU到单一的主机系统中。
4. 实验结论
总之，我们提出了一个使用混合CUDA和MPI编程的并行编程方法，即根据C1060 GPU节点的数目分区的在包括一个C1060和一个S1070的GPU 集群的循环迭代。实验过程中，分配给一个MPI进程的循环迭代由运行在相同的计算节点的处理器内核的CUDA并行地处理。实验表明，由OpenMP 和MPI处理的混合并行多核心GPU是目前一个强大的组成高性能集群的方法。
5参考文献
[2] D. G?ddeke, R. Strzodka, J. Mohd-Yusof, P. McCormick, S. uijssen,M. Grajewski, and S. Tureka, “Exploring weak scalability for EM calculations on a GPU-enhanced cluster,” Parallel Computing,
vol. 33, pp. 685-699, Nov 2007.
[3] P. Alonso, R. Cortina, F.J. Martínez-Zaldívar, J. Ranilla “Neville limination on multi- and many-core systems: OpenMP, MPI and UDA”, Jorunal of Supercomputing, [4] Francois Bodin and Stephane Bihan, “Heterogeneous multicore arallel programming for graphics processing units”, Scienti fic rogramming, Volume 17, Number 4 / 2009, 325-336, Nov. 2009.
[5] Specification esla S1070 GPU Computing System
[7] Message Passing Interface (MPI),
[8] MPICH, A Portable Implementation of MPI
[9] OpenGL, D. Shreiner, M. Woo, J. Neider and T. Davis, OpenGL(R) rogramming Guide: The Official Guide to Learning OpenGL(R), Addison-Wesley, Reading, MA, August 2005. 10] (2008) Intel 64 Tesla Linux Cluster Lincoln webpage. [Online] Available: lCluster/
[11] Romain Dolbeau, Stéphane Bihan, and Fran?ois Bodin, HMPP: Alti-core Parallel Programming Environment
[12] The NVIDIA Tesla S1070 1U Computing System - Scalable Many Core Supercomputing for Data Centers
[13] Top 500 Super Computer Sites, What is Gflop/s, h
[14]
[15]
[17] CUFFT, CUDA Fast Fourier Transform (FFT) library.
[18] CUBLAS, BLAS(Basic Linear Algebra Subprograms) on CUDA
英文学术论文常用词句
英语学术论文常用词句 1. 常用词汇与短语 差别：gaps between, differentiate between, discrepancies，no intergroup difference 存在，出现：occurred, occurrence ,existed, existence, presence, present 多数，少数：the overwhelming majority of, in the majority of cases ，a marked majority, handful 方法：approaches, avenues, methods, techniques, means, tools 发生率：Incidence, frequency, prevalence 发现，阐明，报道，证实：verify, confirm, elucidate, identify, define, characterize, clarify, establish, ascertain, explain, observe, illuminate, illustrate，demonstrate, show, indicate, exhibit, presented, reveal, display, manifest，suggest, propose, estimate, prove, imply, disclose，report, describe，facilitate the identification of ，screening ，isolation 改变：change, alteration, 高，增加：high, enhanced, elevated, increased, forced 各种，多种：in multiple types of, in various types of, in a variety of 关系，相关，参与：closely involved in, associated, 广泛的：in an extensive survey 执行：perform, carry out 降，少，缺：decrease, reduction, reduced, diminish, loss, suppression, deficient, low, weak, faint, light, absence, absent, undetectable, lack ,defective, negative,poor,impaired, greatly reduced or completely absent, frequently lost or down-expressed 角色，起作用：role, part （limited, potential, early, possible role） 可能性：feasibility 密切地：intimately 难理解的，似谜的：enigmatic (x remains enigmatic)
学术英语论文
英语学术论文作业 Hybrid Parallel Programming on GPU Clusters Abstract—Nowadays, NVIDIA’s CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. In this paper, we propose a hybrid parallel programming approach using hybrid CUDA and MPI programming, which partition loop iterations according to the number of C1060 GPU nodes in a GPU cluster which consists of one C1060 and one S1070. Loop iterations assigned to one MPI process are processed in parallel by CUDA run by the processor cores in the same computational node. Keywords: CUDA, GPU, MPI, OpenMP, hybrid, parallel programming I. INTRODUCTION Nowadays, NVIDIA’s CU DA [1, 16] is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions – a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA [1, 16] to achieve dramatic speedups on production and research codes. In NVDIA the CUDA chip, all to the core of hundreds of ways to construct their chips, in here we will try to use NVIDIA to provide computing equipment for parallel computing. This paper proposes a solution to not only simplify the use of hardware acceleration in conventional general purpose applications, but also to keep the application code portable. In this paper, we propose a parallel programming
前沿学术英语论文题目参考
前沿学术英语论文题目参考 学术英语即专家学者、教授学生在学术论文研究与写作中使用的英语，学术英语具有科学严谨性，用词考究，论文写作中应避免英语口语化。接下来跟着小编一起来看看学术英语论文题目有哪些。 1、浅议系统功能语言学理论指导下的英语专业学术论文摘要的翻译 2、“教学学术”视角下开放大学英语教师专业发展的思考 3、课程生态需求分析模式下的“学术英语”课程定位 4、CBI理论视域下学术英语读写教学研究 5、基于微课的通用学术英语写作课翻转课堂教学模式设计与实践 6、基于课堂读写任务的学术英语写作引用特征研究 7、基于语类的英语学术论文写作教学路径研究--以“文献综述”写作教学为例 8、基于需求分析学术英语教学模式 9、学术英语阅读能力的界定与培养 10、学术英语的词块教学法研究 11、英语专业本科毕业论文学术失范现象的成因与对策 12、浅析批判性思维下大学学术英语课程模块的构建
13、关于中文学术期刊使用英语的规范性问题 14、医学学术英语口语课程构建的探索 15、学术英语写作中词汇衔接探究 16、学习者学术英语写作中的引用行为研究 17、浅探理工院校学术英语改革实践 18、学术论文写作中的英语负迁移现象研究 19、学术英语写作教学体系的构建与实践 20、学术英语口头报告对批判性思维的影响探究 21、“学术读写素养”范式与学术英语写作课程设计 22、中国高校学术英语存在理论依据探索 23、学术英语教育对大学生就业的影响研究 24、学术道德教育和学术英语能力一体化培养 25、非英语专业研究生学术英语交际能力现状与对策研究--以延安大学为例 26、关于研究生学术英语教学定位研究 27、理工科学术英语视野下的批判性思维能力培植 28、面向学术英语的实验平台建构与探索 29、学术英语有效教学 30、学术英语写作课程环境下的写前计划效应探究 31、元话语视角下英语学术论文中的转述动词与语类结构研究 32、基于自建语料库的学术英语中语块结构的研究 33、以学术英语为新定位的大学英语教学转型问题的对策研究
英文版通用学术英语论文格式样张
英文版通用学术英语论文格式样张 封面页
主要内容页 The Researches on Rs Method for Discrete Membership Functions ---------------subtitle(副标题12号字加黑右对齐) （空一行） ZHANG Xiaoya, LI Dexiang (题目14号字加黑居中) School of Management, Dalian University, P.R.China,116622 (10 号字居中) yuanfengxiangsheng@https://www.360docs.net/doc/077972307.html, (10号字加黑) (空一行) Abstract Mizumoto used to advance a fuzzy reasoning method ,Rs, which fits the…… Key words IDSS, Fuzzy reasoning,……号字) (空一行) 1 Introduction (一级标题12号字加黑) We know that the approaches of implementation of intelligent decision support systems(IDSS)have become variable……(正文均用10号字) (空一行) 2 An Example According to the definition of Rs, we can construct the fuzzy relation matrix, as shown in table 1 Table 1 A Fuzzy Relation Rs (9号字加黑居中) U2U3 U10.00 0.10 0.40 0.70…… 0.00 1.00 1.00 1.00 1.00 …… 0.20 0.00 0.00 1.00 1.00 ……(表中用9号字) .….. ………………… (空一行)Figure 1 Functions of……(9号字加黑居中) 3 The Improved Method (空一行) 3.1 Method one (二级标题10号字加黑) ………… 3.1.1 Discussing about method one (三级标题10号字) ………… （空一行） 3.2 Method two…………
学术论文常用英语总结
再也不用愁SCI了，再也不愁论文中的英文怎么写了！！！英语学术论 文常用句型 英语学术论文常用句型 Beginning 1. In this paper, we focus on the need for 2. This paper proceeds as follow. 3. The structure of the paper is as follows. 4. In this paper, we shall first briefly introduce fuzzy sets and related concepts 5. To begin with we will provide a brief background on the Introduction 1. This will be followed by a description of the fuzzy nature of the problem and a detailed presentation of how the required membership functions are defined. 2. Details on xx and xx are discussed in later sections. 3. In the next section, after a statement of the basic problem, various situations involving possibility
knowledge are investigated: first, an entirely possibility model is proposed; then the cases of a fuzzy service time with stochastic arrivals and non fuzzy service rule is studied; lastly, fuzzy service rule are considered. Review 1. This review is followed by an introduction. 2. A brief summary of some of the relevant concepts in xxx and xxx is presented in Section 2. 3. In the next section, a brief review of the .... is given. 4. In the next section, a short review of ... is given with special regard to ... 5. Section 2 reviews relevant research related to xx. 6. Section 1.1 briefly surveys the motivation for a methodology of action, while 1.2 looks at the difficulties posed by the complexity of systems and outlines the need for development of possibility methods.
通用学术英语论文
Job and Ability: Chinese College Student s’Interpersonal Communication 求职与能力：中国大学生的人际交往 Students: Ye Ying Rui Course: English for Academic Communication Date: December, 2015 Course Paper Grade 2014
Introduction What is learnt in the university? Besides knowledge, the most major one is the interpersonal communication ability; university students should train various kinds of ability, for instance: People skills, grasp information ability, learning ability etc... Among them the cultivation of interpersonal communication is particularly important, great teacher of the revolution Marx has ever said: People are the total of different social relationships, everybody does not exist in isolation, and he must have the problem: how to make these relations, how to improve life quality, these will involve social ability .As important as interpersonal skills in the job, company think a lot of integrative ability especially social ability when recruit college students. However, according to the survey there are 59% college students have difficulty in interpersonal communication, and hard to get a job. Part One the Study of Chinese College Students’Interpersonal Communication Ability In the recently many companies held a fair in colleges all over the country, some corporate director said that employees' communication ability is the power that more and more crucial for enterprise in market competition to win, so more weight is given to a candidate's emotional intelligence. In the face of recruitment conditions, more and more college students feel the importance of interpersonal skills. With a vocational adaptability of college students, according to a survey, 41.98% of students think interpersonal skills training is helpful education content to find work, significantly more than the professional ability training (14.9%), basic knowledge and skills training (17.5%), and the psychological quality education (17.5%), and other abilities. The answer by choosing a career you feel what is lack of quality particularly, chooses interpersonal skills of up to 34.8%, rankling in the back, respectively is analysis and problem solving skills (28.8%), hands-on skills (25.9%), basic knowledge and skill (4.6%). What’s the most helpful ability when you apply for a job?
大学学术英语课程教学论文
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要是对特定的学科英语口语交流和书面表达、词汇语法研究提供重要的指导帮助。 英语学习在初高中阶段主要是为初步的了解英语语言文化，以及为进一步学习英语语言打下基础。在大学阶段由于学习涉及的专业课程较多，涉及领域水平较高，加之英语作为世界的通用语言在涉外专业的学习中起到重要的作用。有学者认为在大学阶段开展学术英语学习对于学生进行专业学习有重要的帮助，通过学术英语学习可以达到的提高专业英语的水平的目的。学生可以根据自身专业技术水平以及兴趣爱好采用研究型的学习方法进行学术英语学习。这有利于激发和维持学生学习英语较高的热情和劲头，大大的提升学习效率。随着国家间竞争形势的加剧，以及高等教育国际化的快速发展都要求我国大学英语教学需要重新进行定位，需要慎重考虑人才培养的各个方面间的内在联系。 学术英语无疑是我国进行高等英语教育中重要引导发展方向。学术英语作为专业英语中的重要组成部分在进行研究学习的过程中需要同通用英语进行更多的联系和交流以及相互之间进行互补。高等学院的教育领导机构应该针对大学英语教学中薄弱环节进行分析研究，制定一门过度课程将通用英语同专业英语进行有效的连接。学术英语的需求已经存在在大学英语教学中，我们应该做好提前准备给予学术英语以充分的重视，积极的开展研究学习，积极的推进学术英语
英文学术论文常用替换词
1. individuals, characters, folks 替换people , persons. 2. positive, favorable, rosy, promising, perfect, pleasurable, excellent, outstanding, superior 替换good. rosy ；adj. 蔷薇色的，玫瑰红色的；美好的；乐观的；涨红脸的 3. dreadful, unfavorable, poor, adverse, ill 替换bad（如果bad做表语，可以有be less impressive 替换。） Adverse；adj. 不利的；相反的；敌对的（名词adverseness，副词adversely） 4. an army of, an ocean of, a sea of, a multitude of, a host of, if not most 替换many. if not most:即使不是大多数 5. a slice of, quite a few 替换some. a slice of:一片，一份 6. harbor the idea that, take the attitude that, hold the view that, it is widely shared that, it is universally acknowledged that 替换think。 harbor v/n庇护；怀有 7. affair, business, matter 替换thing. 8. shared 替换common . 9. reap huge fruits 替换get many benefits. 10. for my part ,from my own perspective 替换in my opinion. 11. Increasing, growing 替换more and more（注意没有growingly这种形式。所以当修饰名词时用increasing/growing修饰形容词，副词用increasingly.） 12. little if anything或little or nothing 替换hardly. 13. beneficial, rewarding 替换helpful. 14. shopper, client, consumer, purchaser 替换customer. 15. overwhelmingly, exceedingly, extremely, intensely 替换very. 16. hardly necessary, hardly inevitable…替换unnecessary, avoidable. 17. indispensable 替换necessary. 18. sth appeals to sb., sth. exerts a tremendous fascination on sb 替换sb. take interest in / sb. be interested in. 19. capture one's attention 替换attract one's attention. 20. facet, dimension, sphere 替换aspect. 21. be indicative of, be suggestive of, be fearful of 替换indicate, suggest, fear. 22. give rise to, lead to, result in, trigger 替换cause. 23. There are several reasons behind sth. 替换…reasons for sth. 24. desire 替换want. 25. pour attention into 替换pay attention to. 26. bear in mind that 替换remember. 27. enjoy, possess 替换have（注意process是过程的意思）。 28. interaction 替换communication. 29. frown on sth 替换be against , disagree with sth. 30. as an example 替换for example, for instance. 31. next to / virtually impossible 替换nearly / almost impossible. 32. regarding / concerning 替换about. 33. crucial /paramount 替换important. 34. 第一（in the first place/the first and foremost）；
英语学术论文写作课程论文
外国语学院英语系 英语学术论文写作课程论文 题目:The Correlation between English Majors’ English Learning Motivation and Their Learning Strategies 姓名: 学号： 班级：2011级6班 日期: 2013年12月 评 语
成绩 教师签名：
Contents Abstract (i) 摘要 (ii) 1.Introduction (1) 1.1 Background of the Study (1) 1.2 Significance of the Study (2) 1.3 Purpose of the Study (2) 1.4 Overview of the Thesis (2) 2.Literature Review (3) 2.1Research on English Learning Motivation (3) 2.1.1 Definition of ELM (3) 2.1.2 Classification of ELM (4) 2.1.2.1 Integrative Motivation and Instrumental Motivation (4) 2.1.2.2 Intrinsic Motivation and Extrinsic Motivation (5) 2.1.2.3 The Relationship between the Two Motivational Dichotomies.6 2.2 Research on English Learning Strategies (6) 2.2.1 Definition of ELS (7) 2.2.2 Classification of ELS (7) 2.3 Research on the Relationship of English Learning Motivation and English Learning strategies (10) 3. Methodology (11) 3.1Question.... . (11) 3.2 Subjects (12) 3.3 Instruments (12) 3.4 Data Collection (13) 3.5 Data Analysis (13) 4.Results and Discussion (13) 4.1 Results from ELM Questionnaire (13) 4.2 Results from ELS Questionnaire (16)
英语学术论文写作常用句型
英语学术论文常用句型 Beginning 1. In this paper, we focus on the need for 2. This paper proceeds as follow. 3. The structure of the paper is as follows. 4. In this paper, we shall first briefly introduce fuzzy sets and related concepts 5. To begin with we will provide a brief background on the Introduction 1. This will be followed by a description of the fuzzy nature of the problem and a detailed presentation of how the required membership functions are defined. 2. Details on xx and xx are discussed in later sections. 3. In the next section, after a statement of the basic problem, various situations involving possibility knowledge are investigated: first, an entirely possibility model is proposed; then the cases of a fuzzy service time with stochastic arrivals and non fuzzy service rule is studied; lastly, fuzzy service rule are considered. Review 1. This review is followed by an introduction. 2. A brief summary of some of the relevant concepts in xxx and xxx is presented in Section 2. 3. In the next section, a brief review of the .... is given. 4. In the next section, a short review of ... is given with special regard to ... 5. Section 2 reviews relevant research related to xx. 6. Section 1.1 briefly surveys the motivation for a methodology of action, while 1.2 looks at the difficulties posed by the complexity of systems and outlines the need for development of possibility methods. Body 1. Section 1 defines the notion of robustness, and argues for its importance. 2. Section 1 devoted to the basic aspects of the FLC decision making logic. 3. Section 2 gives the background of the problem which includes xxx 4. Section 2 discusses some problems with and approaches to, natural language understanding.
研究生英语学术论文写作考试大概
Ⅰ. Gone up → increased set up → established Put up with → tolerate looking into → investigating Figure out → determine put into practice → implement Come up with → developed make up → constitute Get rid of → eliminate keep up → maintain Gone down → decrease thinking → considering Ⅱ. Structure of Data Commentary Data commentaries usually has these elements in the following order. 1.location elements and/or summary statements 2.highlighting statements 3.discussions of implications,problems,exceptions,recommendations,or other interesting aspects of the data 可能涉及到排序题，有例如下： ①A computer virus is a program that is specifically and maliciously designed to attack a computer system,destroying data.②As business have become inceasingly dependent on computers,e-mail,and the Internet,concern over the potential destructiveness of such viruses has also grown.③Table X shows the most common sources of infection for U.S. businesses.④As can be seen, in a great majority of cases,the entry point of the virus infection can be detected,with e-mail attachments being responsible for nearly 9 out of 10 viruses.⑤This very high percentage is increasingly alarming,especially since with a certain amount of caution such infections are largely preventable.⑥In consequence,e-mail users should be wary of all attachments,even thoes from a trusted colleague or a known sender.⑦In addition,all computers used for e-mail need to have a current version of a good antivirus progarm whose virus definitions are updated regularly.⑧While it may be possible to lessen the likelihood of downloading an infected file,businesses are still vulnerable to computer virus problems because of human error and the threat of new,quickly spreading viruses that cannot be identified by antivvirus software. ①②→Theory and common beliefs. ③→The start ④⑤⑥⑦⑧→Implications Ⅲ.信息性摘要 An informative abstract,as its name implies,summarizes the key points in the RP.It is an overview that briefly state the purpose,methods,results and conclutions with quantitative information. 信息性摘要主要报道论文的研究目的、研究方法、研究结果与结论。它是论文全文的高度浓缩，相当于论文的简介或概要，但它又不是简单对原文篇幅进行按比例的缩减，而是要进行深入加工。 比较流行的信息性摘要架构有： ①Objective→Methodology→Results→Conclusions ②Background→Purpose and aim→Methods→Results→Conclusions
9个常用的国外英文论文文献数据库
9个常用的国外英文论文文献数据库9个论文文献数据库，科研搬砖，阅读涨姿势，论文写作小帮手！先说说什么是数据库：学术科研中说的「数据库」和「文献数据库」，往往是一种网站的形式，这个网站的贮存了大量文献数据（比如论文）可以简单的理解为一个网络图书馆。 数据库中的论文往往都是耗费了大量的时间和精力整理出 来的，还有很多是需要购买版权才可以放在互联网上的，再加上维护这个网站本身就耗费颇多，因此这些数据库通常不是完全免费的，你可以在上面免费查找文献，浏览摘要等简介内容，但是如果你要下载文献，就要付钱。 大学因为科研和教学需要，常年要下载大量的论文材料，所以就会和数据库的经营者签订很多协议，例如包年，就是给一定量的钱，然后就可以无限制下载论文。也有按照下载的数量进行计费。那英语作为世界第一学术语言，有哪些数据库是值得大家分享的呢？1、Wiley InterScience(英文文献期刊)Wiley InterScience是John Wiely & Sons公司创建的动态在线内容服务，1997年开始在网上开通。通过InterScience，Wiley 学术期刊集成全文数据库（Academic Search Premier，简称ASP）：包括有关生物科学、工商经济、资讯科技、通讯传播、工程、教育、艺术、文学、医药学等领域的七千多种期刊，
其中近四千种全文刊。 学术研究图书馆（Academic Research Library，简称ARL）综合参考及人文社会科学期刊论文数据库，涉及社会科学、人文科学、商业与经济、教育、历史、传播学、法律、军事、文化、科学、医学、艺术、心理学、宗教与神学、社会学等学科，收录2，300多种期刊和报纸，其中全文刊占三分之二，有图像。可检索1971年来的文摘和1986年来的全文。商业信息数据库（ABI/INFORM）ABI即为Abstracts of Business Information的缩写，世界着名商业及经济管理期刊论文数据库，收录有关财会、银行、商业、计算机、经济、能源、工程、环境、金融、国际贸易、保险、法律、管理、市场、税收、电信等主题的1，500多种商业期刊，涉及这些行业的市场、企业文化、企业案例分析、公司新闻和分析、国际贸易与投资、经济状况和预测等方面，其中全文刊超过50%，其余为文摘，有图像。 医学电子期刊全文数据库（ProQuest Medical Library）该数据库收录有220种全文期刊，文献全文以PDF格式或文本加图像格式存储；收录范围包括所有保健专业的期刊，有护理学、儿科学、神经学、药理学、心脏病学、物理治疗及其它方面。 6. BlackwellBlackwell出版公司是世界上最大的期刊出版商之一(总部设在英国伦敦的牛津)，以出版国际性期刊为主，