Programming Massively Parallel Processors: A Hands-on Approach (Applications of GPU Computing Series)
Format: PDF / Kindle (mobi) / ePub
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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This article by outlining the future directions we see OpenACC going in. 15.2 Execution Model The OpenACC target machine has a host and an attached accelerator device, such as a GPU. Most accelerator devices can support multiple levels of parallelism. Figure 15.2 illustrates a typical accelerator that supports three levels of parallelism. At the outermost coarse-grain level, there are multiple execution units. Within each execution unit, there are multiple threads. At the innermost level,.
Compiler finds it is not safe to parallelize the loop or cannot decide whether it is safe due to lack of information, then the compiler will not parallelize the loop and hence will not generate a kernel for the loop construct. The other reason is performance. The ultimate goal for using OpenACC directive is to get speedup. The compiler may decide not to parallelize and execute a loop on the accelerator if it finds doing so will only slow down the program. Since the compiler will mostly take care.
Resource limitations in the GPU hardware. Therefore, the main hardware resource provisions in each GPU are typically exposed to applications in a standardized system called compute capability. The general specifications and features of a compute device depend on its compute capability. For CUDA, the compute capability starts at Compute 1.0, and at the time of this writing the latest version is Compute 3.5. Each higher level of compute capability indicates a newer generation of GPU devices with a.
The limiting factor(s). a. 8 blocks with 128 threads each on a device with compute capability 1.0 b. 8 blocks with 128 threads each on a device with compute capability 1.2 c. 8 blocks with 128 threads each on a device with compute capability 3.0 d. 16 blocks with 64 threads each on a device with compute capability 1.0 e. 16 blocks with 64 threads each on a device with compute capability 1.2 f. 16 blocks with 64 threads each on a device with compute capability 3.0 4.8 A CUDA programmer says.
Detection of using uninitialized data during program execution. The current generation of GPU hardware does not support sNaN. This is due to the difficulty of supporting accurate signaling during massively parallel execution. A qNaN generates another qNaN without causing an exception when used as input to arithmetic operations. For example, the operation (1.0 + qNaN) generates a qNaN. Quiet NaNs are typically used in applications where the user can review the output and decide if the application.