Programming Massively Parallel Processors: A Hands-on Approach (Applications of GPU Computing Series)
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Programming Massively Parallel Processors discusses basic concepts about parallel programming and GPU architecture. ""Massively parallel"" refers to the use of a large number of processors to perform a set of computations in a coordinated parallel way. The book details various techniques for constructing parallel programs. It also discusses the development process, performance level, floating-point format, parallel patterns, and dynamic parallelism. The book serves as a teaching guide where parallel programming is the main topic of the course. It builds on the basics of C programming for CUDA, a parallel programming environment that is supported on NVI- DIA GPUs.
Composed of 12 chapters, the book begins with basic information about the GPU as a parallel computer source. It also explains the main concepts of CUDA, data parallelism, and the importance of memory access efficiency using CUDA.
The target audience of the book is graduate and undergraduate students from all science and engineering disciplines who need information about computational thinking and parallel programming.
- Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing.
- Utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for massively parallel environments.
- Shows you how to achieve both high-performance and high-reliability using the CUDA programming model as well as OpenCL.
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Channels that connect to multiple memory banks. The combined bandwidth improvement of multiple channels and special memory structures gives the frame buffers much higher bandwidth than their contemporaneous system memories. Such high memory bandwidth has continued to this day and has become a distinguishing feature of modern GPU design. During the past two decades, each generation of hardware and its corresponding generation of API brought incremental improvements to the various stages of the.
Introduced in 2006, NVIDIA’s GeForce 8800 GPU mapped the separate programmable graphics stages to an array of unified processors; the logical graphics pipeline is physically a recirculating path that visits these processors three times, with much fixed-function graphics logic between visits. This is illustrated in Figure 2.5. The unified processor array allows dynamic partitioning of the array to vertex shading, geometry processing, and pixel processing. Since different rendering algorithms.
Exposed through task decomposition of applications. For example, a simple application may need to do a vector addition and a matrix–vector multiplication. Each of these would be a task. Task parallelism exists if the two tasks can be done independently. In large applications, there are usually a larger number of independent tasks and therefore a larger amount of task parallelism. For example, in a molecular dynamics simulator, the list of natural tasks includes vibrational forces, rotational.
Value will have subscript B. For example, 0.5D (5×10−1 since the place to the right of the decimal point carries a weight of 10−1) is the same as 0.1B (1×2−1 since the place to the right of the decimal point carries a weight of 2−1). Normalized Representation of M Equation (7.1) requires that all values are derived by treating the mantissa value as 1.M, which makes the mantissa bit pattern for each floating-point number unique. For example, the only one mantissa bit pattern allowed for.
Standardized library function interfaces have been created to perform this operation under the name SpMV (sparse matrix–vector multiplication). We will use SpMV to illustrate the important trade-offs between different storage formats of sparse computation. Figure 10.3 A small example of matrix–vector multiplication and accumulation. A sequential implementation of SpMV based on CSR is quite straightforward, which is shown in Figure 10.4. We assume that the code has access to (1) num_rows, a.