Exercise 2: adding more thread blocks. cuda gpus have several parallel processors called streaming multiprocessors or sms. each sm consists of multiple parallel processors and can run multiple concurrent thread blocks. to take advantage of cuda gpus, kernel should be launched with multiple thread blocks. Cuda tutorial 02: cuda in actions : notes. github gist: instantly share code, notes, and snippets.
Docs » tutorials » tutorial 02: cuda in actions tutorial 02: cuda in actions introduction in tutorial 01, we implemented vector addition in cuda using only one gpu thread. however, the strength of gpu lies in its massive parallelism. in this tutorial, we will explore how to exploit gpu parallelism. Following is a list of available tutorials and their description. name. description. level. tutorial 01: say hello to cuda. introduction to cuda c programming. beginner. tutorial 02: cuda in actions. In cuda terminology, this is called "kernel launch". we will discuss about the parameter (1,1) later in this tutorial 02. compiling cuda programs. compiling a cuda program is similar to c program. nvidia provides a cuda compiler called nvcc in the cuda toolkit to compile cuda code, typically stored in a file with extension .cu. for example. Session 1: an introduction to numba and cuda python. session 1 files are in the session 1 folder. contents: presentation: the presentation for this session, along with notes. mandelbrot example: see the readme for exercises. cuda kernels notebook: in the exercises folder. open the notebook using jupyter. ufuncs notebooks in the exercises folder.
In cuda terminology, this is called "kernel launch". we will discuss about the parameter (1,1) later in this tutorial 02. compiling cuda programs. compiling a cuda program is similar to c program. nvidia provides a cuda compiler called nvcc in the cuda toolkit to compile cuda code, typically stored in a file with extension .cu. for example. Session 1: an introduction to numba and cuda python. session 1 files are in the session 1 folder. contents: presentation: the presentation for this session, along with notes. mandelbrot example: see the readme for exercises. cuda kernels notebook: in the exercises folder. open the notebook using jupyter. ufuncs notebooks in the exercises folder. This chapter introduces the main concepts behind the cuda programming model by outlining how they are exposed in c . an extensive description of cuda c is given in programming interface. full code for the vector addition example used in this chapter and the next can be found in the vectoradd cuda sample. 2.1. Cuda c c keyword global indicates a function that: runs on the device is called from host code nvcc separates source code into host and device components device functions (e.g. mykernel()) processed by nvidia compiler host functions (e.g. main()) processed by standard host compiler gcc, cl.exe.
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