This chapter introduces CUDA dynamic parallelism, an extension to the CUDA programming model that enables a CUDA kernel to create new thread grids by launching new kernels. Dynamic parallelism allows algorithms
动态并行(Dynamic Parallelism)是 CUDA 编程模型的扩展,该特性可以使CUDA Kernel能够直接在 GPU 上创建新任务并与新任务同步,即,可以在 Kernel 中启动一个新的 Kernel。以往应用程序只能通过主机端创建并启动并行任务,随着动态并行的引入,在设备端也可以随时启动新的并行任务,注意这与在 Kernel 中调用设备函数有本质不...
个人感觉,对于一个具体的计算任务,dynamic parallelism写出来的代码性能应该是不及只用一个kernel的。例如图、排序、Join这些,我们都可以给他增加很多的优化策略,来让他们达到负载均衡,而不需要使用dynamic parallelism。但是dynamic parallelism是一种在编程模型级别解决问题的思路,具有通用性。而随着各种优化手段的加入,使用...
CUDA之Dynamic Parallelism详解(一) 1. 循环的并行化: (1)循环固定 (2)内循环依赖于外循环 without dynamic parallelism with dynamic parallelism examples: 0 踩 0 === 目录(?)[-] 编译链接 执行同步 内存一致 向Child grids传递指针
This is the first of a three part series on CUDA Dynamic Parallelism: Adaptive Parallel Computation – Dynamic Parallelism overview and example (this post); API and Principles –Advanced topics in Dynamic Parallelism, including device-side streams and synchronization; Case Study: PANDA –how Dynamic...
This chapter introduces CUDA dynamic parallelism, an extension to the CUDA programming model that enables a CUDA kernel to create more thread grids by calling other kernels. Dynamic parallelism allows algorithms that dynamically discover new work to prepare and launch grids without burdening the host ...
CUDA --- Dynamic Parallelism Dynamic Parallelism 到目前为止,所有kernel都是在host端调用,GPU的工作完全在CPU的控制下。CUDA Dynamic Parallelism允许GPU kernel在device端创建调用。Dynamic Parallelism使递归更容易实现和理解,由于启动的配置可以由device上的thread在运行时决定,这也减少了host和device之间传递数据和执行...
This post is the second in a series on CUDA Dynamic Parallelism. In my first post, I introduced Dynamic Parallelism by using it to compute images of the…
Assuming the environment variable CUDA_PATH points to CUDA Toolkit installation directory, build this example as: With NVRTC shared library: Windows: cl.exe dynamic-parallelism.cpp /Fedynamic-parallelism ^ /I "%CUDA_PATH%\include" ^ "%CUDA_PATH%"\lib\x64\nvrtc.lib "%CUDA_PATH%"\lib\...
nvprof supports CUDA Dynamic Parallelism in GPU-Trace mode. For host kernel launch, the kernel ID will be shown. For device kernel launch, the kernel ID, parent kernel ID and parent block will be shown. Here's an example: $nvprof --print-gpu-trace cdpSimpleQuicksort ==28128== NVPROF ...