From 978a6e5e9b2d64ac6c5007eb51846d852ece623a Mon Sep 17 00:00:00 2001 From: liaogang Date: Tue, 15 Nov 2016 23:53:15 +0800 Subject: [PATCH] Update gpu profiling docs --- doc/optimization/gpu_profiling.rst | 52 ++++++++++++++++++++++++++++- doc/optimization/nvvp2.png | Bin 0 -> 495117 bytes doc/optimization/nvvp3.png | Bin 0 -> 253700 bytes doc/optimization/nvvp4.png | Bin 0 -> 283198 bytes 4 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 doc/optimization/nvvp2.png create mode 100644 doc/optimization/nvvp3.png create mode 100644 doc/optimization/nvvp4.png diff --git a/doc/optimization/gpu_profiling.rst b/doc/optimization/gpu_profiling.rst index 583c2d6caee..efdf5552c35 100644 --- a/doc/optimization/gpu_profiling.rst +++ b/doc/optimization/gpu_profiling.rst @@ -8,6 +8,7 @@ This tutorial will guide you step-by-step through how to conduct profiling and p - How to do profiling? - Profile tools - Hands-on Tutorial +- Profiling tips What's profiling? ================= @@ -68,10 +69,59 @@ respectively to avoid program crashes when CPU version of PaddlePaddle invokes t Hands-on Approach ================= +To use this command line profiler :code:`nvprof`, you can simply issue the command: + +.. code-block:: bash + + nvprof ./paddle/math/tests/test_GpuProfiler + +Then, you can get the following profiling result: + .. image:: nvprof.png :align: center :scale: 30% +For visual profiler :code:`nvvp`, you can either import the output of :code:`nvprof –o ...` or +run application through GUI. + .. image:: nvvp1.png :align: center - :scale: 30% \ No newline at end of file + :scale: 30% + +From the perspective of kernel functions, :code:`nvvp` can even illustrate why does an operation take a long time? +As shown in the following figure, kernel's block usage, register usage and shared memory usage from :code:`nvvp` +allow us to fully utilize all warps on the GPU. + +.. image:: nvvp2.png + :align: center + :scale: 30% + +From the perspective of application, :code:`nvvp` can give you some suggestions to address performance bottleneck. +For instance, some advice in data movement and compute utilization from the below figure can guide you to tune performance. + +.. image:: nvvp3.png + :align: center + :scale: 30% + +.. image:: nvvp4.png + :align: center + :scale: 30% + +Profiling tips +============== + +- The :code:`nvprof` and :code:`nvvp` output is a very good place to start +- The timeline is a good place to go next +- Only dig deep into a kernel if it’s taking a significant amount of your time. +- Where possible, try to match profiler output with theory. + 1) For example, if I know I’m moving 1GB, and my kernel takes 10ms, I expect the profiler to report 100GB/s. + 2) Discrepancies are likely to mean your application isn’t doing what you thought it was. +- Know your hardware: If your GPU can do 6 TFLOPs, and you’re already doing 5.5 TFLOPs, you won’t go much faster! + + +Profiling is a key step in optimisation. Sometimes quite simple changes can lead to big improvements in performance. +Your mileage may vary! + +Reference +========= +Jeremy Appleyard, `GPU Profiling for Deep Learning `_, 2015 diff --git a/doc/optimization/nvvp2.png b/doc/optimization/nvvp2.png new file mode 100644 index 0000000000000000000000000000000000000000..177c9db708da6863d1075f3e615f5962dbe18b29 GIT binary patch literal 495117 zcmeFYcUTn9*Du)Q93>;55+#TtNl6NlL_{S@lq7K&$$3UaB!grH0TmEXaU@F|$pRvh zWr#zRJOeTeOkf+o;dh_+xzFC+ecs)F_7-&ahtsD{pHqFR>eT7t=;zTKz z0Dx!U4{(GA0(3(>?gN0aF(3^906joWApp>T2xI{?DFpw;dK6Ls)gL+~0K|9z)PM8b z1h2;hl=i32UsuXkl>czhzkEgY52kr@%zCs5sNQmi20;DYp$|@7Ja-;Y)iyMyK9&fU zKUn+^WFHYqoJavGsaGE|Jm0`;!;a?Jt{Cd*INZ8pqGxzR_m3fL=N%tB@TO!30Ekb3 z-yMDJQ&xAaPtnZ*i~uFT1jqsx9G(3iT)lPc#<9$QuE+R41vmCbcVJlVSk`~&|CbPl zi>tpgX#6dZ+sVb>83N*J0HEe`e&81X05sn~x@d5~gJX;VF_#~xAOO%F+sozl7v?+0 zPJdz9KRjl4bU>af;45Kvbo6rr0M=vuPX#%6XU0@ef{e9goEfwyDmpFjRPPY~}wu)Y42 zGvJxo-Ou0-c;y1yt<(!@bc~rn+~nhQ(*(qvApYtBTJ$$Yxd$4UgZP+#!`WX4Y)gO# 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