By Gregory Ruetsch
CUDA Fortran for Scientists and Engineers indicates how high-performance software builders can leverage the ability of GPUs utilizing Fortran, the established language of medical computing and supercomputer functionality benchmarking. The authors presume no past parallel computing adventure, and canopy the fundamentals in addition to most sensible practices for effective GPU computing utilizing CUDA Fortran.
To assist you upload CUDA Fortran to present Fortran codes, the ebook explains easy methods to comprehend the objective GPU structure, establish computationally extensive components of the code, and adjust the code to regulate the information and parallelism and optimize functionality. All of this can be performed in Fortran, with no need to rewrite in one other language. each one suggestion is illustrated with genuine examples so that you can instantly overview the functionality of your code in comparison.
- Leverage the ability of GPU computing with PGI's CUDA Fortran compiler
- Gain insights from contributors of the CUDA Fortran language improvement team
- Includes multi-GPU programming in CUDA Fortran, protecting either peer-to-peer and message passing interface (MPI) approaches
- Includes complete resource code for all of the examples and a number of other case reports
- Download resource code and slides from the book's spouse website
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Additional info for CUDA Fortran for Scientists and Engineers. Best Practices for Efficient CUDA Fortran Programming
6 Instruction Optimization . . . 1 Device Intrinsics . . . . 1 Directed Rounding . 2 C Intrinsics . . . 3 Fast Math Intrinsics . 2 Compiler Options . . . . 3 Divergent Warps . . . . 7 Kernel Loop Directives . .
1 Declaring Data in Device Code . . . . . . . 2 Coalesced Access to Global Memory . . . . . . 1 Misaligned Access . . . . . . . . 2 Strided Access . . . . . . . . . 3 Texture Memory . . . . . . . . . . . 4 Local Memory . . . . . . . . . . . 1 Detecting Local Memory Use (Advanced Topic) . 5 Constant Memory . . . . . . . . . . 3 On-Chip Memory . . . . . . . . . . . . 1 L1 Cache . . . . . . . . . . . . 2 Registers . .
31 32 32 34 35 36 39 39 41 42 A prerequisite to performance optimization is a means to accurately time portions of a code and subsequently describe how to use such timing information to assess code performance. In this chapter we first discuss how to time kernel execution using CPU timers, CUDA events, and the Command Line Profiler as well as the nvprof profiling tool. We then discuss how timing information can be used to determine the limiting factor of kernel execution.
CUDA Fortran for Scientists and Engineers. Best Practices for Efficient CUDA Fortran Programming by Gregory Ruetsch