By Gregory Ruetsch
CUDA Fortran for Scientists and Engineers exhibits how high-performance software builders can leverage the facility of GPUs utilizing Fortran, the established language of clinical computing and supercomputer functionality benchmarking. The authors presume no earlier parallel computing event, and canopy the fundamentals in addition to top practices for effective GPU computing utilizing CUDA Fortran.
To assist you upload CUDA Fortran to latest Fortran codes, the booklet explains tips on how to comprehend the objective GPU structure, establish computationally in depth elements of the code, and regulate the code to regulate the information and parallelism and optimize functionality. All of this is often performed in Fortran, with no need to rewrite in one other language. each one thought is illustrated with real examples so that you can instantly overview the functionality of your code in comparison.
- Leverage the facility of GPU computing with PGI's CUDA Fortran compiler
- Gain insights from individuals of the CUDA Fortran language improvement team
- Includes multi-GPU programming in CUDA Fortran, masking either peer-to-peer and message passing interface (MPI) approaches
- Includes complete resource code for the entire examples and several other case experiences
- Download resource code and slides from the book's significant other website
Read Online or Download CUDA Fortran for Scientists and Engineers. Best Practices for Efficient CUDA Fortran Programming PDF
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Extra resources for CUDA Fortran for Scientists and Engineers. Best Practices for Efficient CUDA Fortran Programming
2 Batching Small Data Transfers . . . . . . . 1 Explicit Transfers Using cudaMemcpy() . . . 3 Asynchronous Data Transfers (Advanced Topic) . . . 1 Hyper-Q . . . . . . . . . . . 2 Profiling Asynchronous Events . . . . . . 2 Device Memory . . . . . . . . . . . . 1 Declaring Data in Device Code . . . . . . . 2 Coalesced Access to Global Memory . . . . . . 1 Misaligned Access . . . . . . . . 2 Strided Access . . . . . . .
Though not CUDA specific, other compiler options are the -v and -V. Compiling with the -v option provides verbose output of the compilation and linking steps. 10 version of the PGI compilers. 1 Separate compilation CUDA Fortran has always allowed host code to launch kernels that are defined in multiple modules, whether these modules are in the same or different files. The host code needs to simply use each of the modules that contain kernels that are launched. Likewise, sharing device data between modules is relatively straightforward and available on GPUs of any compute capability.