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"This best-selling guide to CUDA and GPU parallel programming has been revised with more parallel programming examples, commonly-used libraries, and explanations of the latest tools. With these improvements, the book retains its concise, intuitive, practical approach based on years of road-testing in the authors' own parallel computing courses. "Programming Massively Parallel Processors: A Hands-on Approach" shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. Updates in this edition include: new coverage of CUDA 4.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies explore the latest applications of CUDA and GPUs for scientific research and high-performance computing."

"Parallel MATLAB for Multicore and Multinode Computers is the first book on parallel MATLAB and the first parallel computing book focused on the design, code, debug, and test techniques required to quickly produce well-performing parallel programs.MATLAB is currently the dominant language of technical computing with one million users worldwide, many of whom can benefit from the increased power offered by inexpensive multicore and multinode parallel computers. MATLAB is an ideal environment for learning about parallel computing, allowing the user to focus on parallel algorithms instead of the details of implementation."

"This state-of-the-art survey features topics related to the impact of multicore, manycore, and coprocessor technologies in science and for large-scale applications in an interdisciplinary environment. The papers cover issues of current research in mathematical modeling, design of parallel algorithms, aspects of microprocessor architecture, parallel programming languages, hardware-aware computing, heterogeneous platforms, manycore technologies, performance tuning, and requirements for large-scale applications. The contributions presented in this volume offer a survey on the state of the art, the concepts and perspectives for future developments. They are an outcome of an inspiring conference conceived and organized by the editors at the Karlsruhe Institute Technology (KIT) in September 2011. The twelve revised full papers presented together with two contributed papers focus on combination of new aspects of microprocessor technologies, parallel applications, numerical simulation, and software development; thus they clearly show the potential of emerging technologies in the area of multicore and manycore processors that are paving the way towards personal supercomputing and very likely towards exascale computing."

"Scientific computing has often been called the third approach to scientific discovery, emerging as a peer to experimentation and theory. Historically, the synergy between experimentation and theory has been well understood: experiments give insight into possible theories, theories inspire experiments, experiments reinforce or invalidate theories, and so on. As scientific computing has evolved to produce results that meet or exceed the quality of experimental and theoretical results, it has become indispensable.Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering."

"Tingginya tingkat kompleksitas program menyebabkan program memiliki waktu eksekusi yang lama jikalau tidak dijalankan pada mesin berkomputasi tinggi. Masalah ini dapat diatasi salah satunya dengan cara menjalankan berbagai proses pada program tersebut secara simultan sehingga program dapat semakin cepat tereksekusi. Metode ini dikenal dengan istilah parallel computing. Untuk lebih mempercepat waktu eksekusi program, parallel computing tersebut dapat diimplementasikan pada arsitektur High Performance Computing HPC. Metode parallel computing dalam HPC tersebut diimplementasikan ke dalam program Sistem Penilaian Esai Otomatis Simple-O. Simple-O merupakan program penilai esai otomatis yang merupakan pengembangan dari Departemen Teknik Elektro. Dengan menerapkan parallel computing dan menjalankan program pada HPC, eksekusi yang dibutuhkan untuk memeriksa jawaban esai dapat semakin cepat. Parallel computing atau parallelism akan diterapkan pada salah satu bagian dari Simple-O yaitu pada algoritma pembelajaran dalam Simple-O, lebih tepatnya pada Self Organizing Map SOM atau Learning Vector Quantization LVQ. Parallelism dalam SOM dan LVQ diterapkan dengan metode network partition dimana node komputasi Euclidean distance dilakukan secara parallel. Pada penelitian ini didapatkan bahwa kecepatan program serial 1,28 kali lebih cepat dibandingkan program parallel.

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The escalation of program complexity nowadays means slower run time when it is not executed in high performance machine. One way to address this issue is to execute the processes in the program simultaneously so the program may be executed quicker, known as parallel computing. To further accelerate the program parallel computing can be implemented in High Performance Computing HPC architecture. This method of applicating parallel computing with HPC is implemented in Automatic Essay Grading System, known as Simple O. Simple O is an automatic essay grading system developed by Department of Electrical Engineering Universitas Indonesia. The purpose of applicating the aforementioned method to Simple O is to accelerate the speed of essay grading execution. Parallel computing will be implemented to one of Simple O rsquo s part of program, which is in the learning algorithm. The learning algorithm applied in Simple O is Self Organizing Map SOM and Learning Vector Quantization LVQ. The implementation of parallelism in the learning algorithm uses network partition method, where the calculation of Euclidean distance is done in parallel. Through this research, it can be concluded that the the speed of serial program is 1.28 times quicker than the parallelized program."

"This book constitutes the thoroughly refereed proceedings of the 18th International Conference, Euro-Par 2012, held in Rhodes Islands, Greece, in August 2012. The 75 revised full papers presented were carefully reviewed and selected from 228 submissions. The papers are organized in topical sections on support tools and environments, performance prediction and evaluation, scheduling and load balancing, high-performance architectures and compilers, parallel and distributed data management, grid, cluster and cloud computing, peer to peer computing, distributed systems and algorithms, parallel and distributed programming, parallel numerical algorithms, multicore and manycore programming, theory and algorithms for parallel computation, high performance network and communication, mobile and ubiquitous computing, high performance and scientific applications, GPU and accelerators computing."