为什么cuda-gdb在CUDA内核中无断点执行同一个程序比gdb慢很多？

Question

我在使用 cuda-gdb 时遇到问题。 我的程序从 python 开始，它加载了一个包含 tensorflow 和 cuda 代码的共享库。 我用来启动 cuda-gdb 的命令是cuda-gdb --args python test_cr_bbp_tf2.py 。 在cuda-gdb中输入run后，我等了大约10分钟才执行完毕，随着新线程信息的出现，程序挂了很长时间。 这个长时间的挂起使得 cuda-gdb 在调试我的程序时毫无用处。 另一个使用gdb --args python test_cr_bbp_tf2.py执行只用了 10 秒。 来自 cuda-gdb 的日志如下所示（cuda-gdb 和 gdb 显示相同的日志）：

Starting program: /usr/bin/python test_cr_bbp_tf2.py
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/usr/lib/x86_64-linux-gnu/libthread_db.so.1".
2022-05-04 19:44:17.020132: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcudart.so.11.0
[Detaching after fork from child process 61729]
warning: Cannot parse .gnu_debugdata section; LZMA support was disabled at compile time
warning: Cannot parse .gnu_debugdata section; LZMA support was disabled at compile time
warning: Cannot parse .gnu_debugdata section; LZMA support was disabled at compile time
testmatching
2022-05-04 19:44:18.724780: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcudart.so.11.0
2022-05-04 19:44:19.270957: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcuda.so.1
[Detaching after fork from child process 61762]
[New Thread 0x7fff83ec3700 (LWP 61868)]
2022-05-04 19:44:23.413363: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:23.414389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 1 with properties: 
pciBusID: 0000:41:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:23.415424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 2 with properties: 
pciBusID: 0000:61:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:23.416413: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 3 with properties: 
pciBusID: 0000:81:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:23.417823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 4 with properties: 
pciBusID: 0000:c1:00.0 name: NVIDIA A40 computeCapability: 8.6
coreClock: 1.74GHz coreCount: 84 deviceMemorySize: 44.56GiB deviceMemoryBandwidth: 648.29GiB/s
2022-05-04 19:44:23.417844: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcudart.so.11.0
2022-05-04 19:44:23.460000: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcublas.so.11
2022-05-04 19:44:23.460044: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcublasLt.so.11
2022-05-04 19:44:23.484264: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcufft.so.10
2022-05-04 19:44:23.507764: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcurand.so.10
2022-05-04 19:44:23.531913: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcutensor.so.1
2022-05-04 19:44:23.554745: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcusolver.so.11
2022-05-04 19:44:23.577344: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcusparse.so.11
2022-05-04 19:44:23.598496: I tensorflow/stream_executor/platform/default/dso_loader.cc:54] Successfully opened dynamic library libcudnn.so.8
2022-05-04 19:44:23.610402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1872] Adding visible gpu devices: 0, 1, 2, 3, 4
[New Thread 0x7fff8349a700 (LWP 61869)]
[New Thread 0x7fff82c99700 (LWP 61870)]
[New Thread 0x7fff82498700 (LWP 61871)]
[New Thread 0x7fff81c97700 (LWP 61872)]
[New Thread 0x7fff81496700 (LWP 61873)]
[New Thread 0x7fff80c95700 (LWP 61874)]
[New Thread 0x7fff0fa23700 (LWP 61875)]
[New Thread 0x7fff0f222700 (LWP 61876)]
[New Thread 0x7fff0ea21700 (LWP 61877)]
[New Thread 0x7fff0e220700 (LWP 61878)]
[New Thread 0x7fff0da1f700 (LWP 61879)]
[New Thread 0x7fff0d21e700 (LWP 61880)]
[New Thread 0x7fff0ca1d700 (LWP 61881)]
[New Thread 0x7ffedffff700 (LWP 61882)]
[New Thread 0x7ffedf7fe700 (LWP 61883)]
[New Thread 0x7ffedeffd700 (LWP 61884)]
[New Thread 0x7ffede7fc700 (LWP 61885)]
[New Thread 0x7ffeddffb700 (LWP 61886)]
[New Thread 0x7ffedd7fa700 (LWP 61887)]
[New Thread 0x7ffedcff9700 (LWP 61888)]
[New Thread 0x7ffebbfff700 (LWP 61889)]
[New Thread 0x7ffebb7fe700 (LWP 61890)]
[New Thread 0x7ffebaffd700 (LWP 61891)]
[New Thread 0x7ffeba7fc700 (LWP 61892)]
[New Thread 0x7ffeb9ffb700 (LWP 61893)]
[New Thread 0x7ffeb97fa700 (LWP 61894)]
[New Thread 0x7ffeb8ff9700 (LWP 61895)]
[New Thread 0x7ffe9bfff700 (LWP 61896)]
[New Thread 0x7ffe9b7fe700 (LWP 61897)]
[New Thread 0x7ffe9affd700 (LWP 61898)]
[New Thread 0x7ffe9a7fc700 (LWP 61899)]
[New Thread 0x7ffe99ffb700 (LWP 61900)]
[New Thread 0x7ffe997fa700 (LWP 61901)]
[New Thread 0x7ffe98ff9700 (LWP 61902)]
[New Thread 0x7ffe7bfff700 (LWP 61903)]
[New Thread 0x7ffe7b7fe700 (LWP 61904)]
[New Thread 0x7ffe7affd700 (LWP 61905)]
[New Thread 0x7ffe7a7fc700 (LWP 61906)]
[New Thread 0x7ffe79ffb700 (LWP 61907)]
[New Thread 0x7ffe797fa700 (LWP 61908)]
[New Thread 0x7ffe78ff9700 (LWP 61909)]
[New Thread 0x7ffe63fff700 (LWP 61910)]
[New Thread 0x7ffe637fe700 (LWP 61911)]
[New Thread 0x7ffe62ffd700 (LWP 61912)]
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[New Thread 0x7ffe60ff9700 (LWP 61916)]
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[New Thread 0x7ffe3f7fe700 (LWP 61918)]
[New Thread 0x7ffe3effd700 (LWP 61919)]
[New Thread 0x7ffe3e7fc700 (LWP 61920)]
[New Thread 0x7ffe3dffb700 (LWP 61921)]
[New Thread 0x7ffe3d7fa700 (LWP 61922)]
[New Thread 0x7ffe3cff9700 (LWP 61923)]
[New Thread 0x7ffe1bfff700 (LWP 61924)]
[New Thread 0x7ffe1b7fe700 (LWP 61925)]
[New Thread 0x7ffe1affd700 (LWP 61926)]
[New Thread 0x7ffe1a7fc700 (LWP 61927)]
[New Thread 0x7ffe19ffb700 (LWP 61928)]
[New Thread 0x7ffe197fa700 (LWP 61929)]
[New Thread 0x7ffe18ff9700 (LWP 61930)]
[New Thread 0x7ffdfbfff700 (LWP 61931)]
[New Thread 0x7ffdf3fff700 (LWP 61932)]
[New Thread 0x7ffdfb7fe700 (LWP 61933)]
[New Thread 0x7ffdfaffd700 (LWP 61934)]
[New Thread 0x7ffdfa7fc700 (LWP 61935)]
[New Thread 0x7ffdf9ffb700 (LWP 61936)]
[New Thread 0x7ffdf97fa700 (LWP 61937)]
[New Thread 0x7ffdf8ff9700 (LWP 61938)]
[New Thread 0x7ffdf37fe700 (LWP 61939)]
[New Thread 0x7ffdf2ffd700 (LWP 61940)]
[New Thread 0x7ffdf27fc700 (LWP 61941)]
[New Thread 0x7ffdf1ffb700 (LWP 61942)]
[New Thread 0x7ffdf17fa700 (LWP 61943)]
[New Thread 0x7ffdf0ff9700 (LWP 61944)]
[New Thread 0x7ffdbbfff700 (LWP 61945)]
[New Thread 0x7ffdbb7fe700 (LWP 61946)]
[New Thread 0x7ffdbaffd700 (LWP 61947)]
[New Thread 0x7ffdba7fc700 (LWP 61948)]
[New Thread 0x7ffdb9ffb700 (LWP 61949)]
[New Thread 0x7ffdb97fa700 (LWP 61950)]
[New Thread 0x7ffdb8ff9700 (LWP 61951)]
[New Thread 0x7ffd9bfff700 (LWP 61952)]
[New Thread 0x7ffd9b7fe700 (LWP 61953)]
[New Thread 0x7ffd9affd700 (LWP 61954)]
[New Thread 0x7ffd9a7fc700 (LWP 61955)]
[New Thread 0x7ffd99ffb700 (LWP 61956)]
[New Thread 0x7ffd997fa700 (LWP 61957)]
[New Thread 0x7ffd98ff9700 (LWP 61958)]
[New Thread 0x7ffd7bfff700 (LWP 61959)]
[New Thread 0x7ffd737fe700 (LWP 61960)]
[New Thread 0x7ffd7b7fe700 (LWP 61961)]
[New Thread 0x7ffd7affd700 (LWP 61962)]
[New Thread 0x7ffd7a7fc700 (LWP 61963)]
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[New Thread 0x7ffd727fc700 (LWP 61986)]
[New Thread 0x7ffd71ffb700 (LWP 61987)]
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[New Thread 0x7ffd70ff9700 (LWP 61989)]
[New Thread 0x7ffd41fff700 (LWP 62007)]
[New Thread 0x7ffd417fe700 (LWP 62008)]
2022-05-04 19:44:32.857957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:32.859130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 1 with properties: 
pciBusID: 0000:41:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:32.862621: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 2 with properties: 
pciBusID: 0000:61:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:32.864706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 3 with properties: 
pciBusID: 0000:81:00.0 name: NVIDIA A10 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 72 deviceMemorySize: 22.20GiB deviceMemoryBandwidth: 558.88GiB/s
2022-05-04 19:44:32.866416: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1734] Found device 4 with properties: 
pciBusID: 0000:c1:00.0 name: NVIDIA A40 computeCapability: 8.6
coreClock: 1.74GHz coreCount: 84 deviceMemorySize: 44.56GiB deviceMemoryBandwidth: 648.29GiB/s
2022-05-04 19:44:32.877182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1872] Adding visible gpu devices: 0, 1, 2, 3, 4
2022-05-04 19:46:31.513555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-05-04 19:46:31.513646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 1 2 3 4 
2022-05-04 19:46:31.513655: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N Y Y Y Y 
2022-05-04 19:46:31.513660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 1:   Y N Y Y Y 
2022-05-04 19:46:31.513664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 2:   Y Y N Y Y 
2022-05-04 19:46:31.513668: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 3:   Y Y Y N Y 
2022-05-04 19:46:31.513672: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 4:   Y Y Y Y N 
2022-05-04 19:46:32.375262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 20566 MB memory) -> physical GPU (device: 0, name: NVIDIA A10, pci bus id: 0000:01:00.0, compute capability: 8.6)
[New Thread 0x7ffd40ffd700 (LWP 63758)]
[New Thread 0x7ffcf3fff700 (LWP 63759)]
2022-05-04 19:46:33.726341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 20566 MB memory) -> physical GPU (device: 1, name: NVIDIA A10, pci bus id: 0000:41:00.0, compute capability: 8.6)
[New Thread 0x7ffcf37fe700 (LWP 63788)]
[New Thread 0x7ffcf2f14700 (LWP 63789)]
2022-05-04 19:46:35.095419: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 20566 MB memory) -> physical GPU (device: 2, name: NVIDIA A10, pci bus id: 0000:61:00.0, compute capability: 8.6)
[New Thread 0x7ffcf2713700 (LWP 63813)]
[New Thread 0x7ffcf1f12700 (LWP 63814)]
2022-05-04 19:46:36.507977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 20566 MB memory) -> physical GPU (device: 3, name: NVIDIA A10, pci bus id: 0000:81:00.0, compute capability: 8.6)
[New Thread 0x7ffcf1711700 (LWP 63878)]
[New Thread 0x7ffcf0f10700 (LWP 63879)]
2022-05-04 19:46:37.914435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:4 with 43434 MB memory) -> physical GPU (device: 4, name: NVIDIA A40, pci bus id: 0000:c1:00.0, compute capability: 8.6)
[New Thread 0x7ffc9bfff700 (LWP 63908)]
[New Thread 0x7ffc9b7fe700 (LWP 63909)]
2022-05-04 19:46:39.270650: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 96. Tune using inter_op_parallelism_threads for best performance.
[New Thread 0x7ffc9affd700 (LWP 63910)]
[New Thread 0x7ffc9a7fc700 (LWP 63911)]
[New Thread 0x7ffc99ffb700 (LWP 63912)]
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[New Thread 0x7ffb4d7fa700 (LWP 63990)]
[New Thread 0x7ffb4cff9700 (LWP 63991)]
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[New Thread 0x7ffb0d7fa700 (LWP 64004)]
[New Thread 0x7ffb0cff9700 (LWP 64005)]
inBox: [   8 1527   56  839]
outBox: [   9 1526   57  838]
msdiffGPUBBP 9.787128717049706 8.836871411244479 8.792443641292834 1.0240484074218186
msdiffGPUBBP 8.284130262488215 8.504984482311862 8.468490694873381 0.9001938761471568
msdiffGPUBBP 7.607680601497159 8.326243672859956 8.302741359766436 0.7882571994561002
msdiffGPUBBP 9.01851150047646 8.826698188773078 8.800456119590438 0.852269245604869
msdiffGPUBBP 10.002452782253538 9.02661007092474 9.003068931959348 0.7974960688804729
msdiffGPUBBP 9.362822708408418 8.672302889066888 8.648869787310186 0.8399175541051185
msdiffGPUBBP 13.275880608755909 13.290950955327459 13.297561482034173 0.5154759081244825
mean (GPU-BBP) 0.8168083228200026
mean diff Median (GPU-BBP) 0.0
[Thread 0x7ffb4f7fe700 (LWP 63979) exited]
[Thread 0x7ffb0dffb700 (LWP 64003) exited]
[Thread 0x7ffb0e7fc700 (LWP 64002) exited]
[Thread 0x7ffb167fc700 (LWP 63996) exited]
[Thread 0x7ffb157fa700 (LWP 63998) exited]
[Thread 0x7ffb0ffff700 (LWP 64000) exited]
[Thread 0x7ffb177fe700 (LWP 63994) exited]
[Thread 0x7ffb0f7fe700 (LWP 63993) exited]
[Thread 0x7ffb0d7fa700 (LWP 64004) exited]
[Thread 0x7ffb0effd700 (LWP 64001) exited]
[Thread 0x7ffb15ffb700 (LWP 63997) exited]
[Thread 0x7ffb0cff9700 (LWP 64005) exited]
[Thread 0x7ffb14ff9700 (LWP 63999) exited]
[Thread 0x7ffb16ffd700 (LWP 63995) exited]
[Thread 0x7ffb17fff700 (LWP 63992) exited]
[Thread 0x7ffb4cff9700 (LWP 63991) exited]
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我很困惑，在我的理解中，如果不在 CUDA 内核中设置断点，gdb 和 cuda-gdb 应该大致相同。 那为什么cuda-gdb执行程序比gdb慢很多呢？ 此外，有没有办法提高性能？

Answer 1

....在 CUDA 内核中不设置断点，gdb 和 cuda-gdb 应该大致相同。

这是一个假设，你自己被证明是错误的。

CUDA GPU 上的调试和分析实现的内部工作并不公开，但有大量经验证据表明调试需要与正常 kernel 执行不同的执行模式。 这包括使用硬件或软件抢占，以便在主机上运行的调试器可以逐条指令控制 gpu 的执行，捕获错误并捕获 GPU 堆栈跟踪，并执行寄存器文件镜像，以便 GPU state 可以在运行时检查，例如。

这些都不是免费的。 如果您运行 cuda-gdb，您将获得所有这些额外的检测和诊断功能，但会以性能成本为代价。 如问题中所述，这是否取决于 GPU 端的断点已被您自己的基准测试证明是错误的。

如果您不运行 cuda-gdb，而是使用无法询问或控制 GPU 执行的纯主机端调试器，则不会招致这些性能损失。 这是你的选择。

Answer 2

在 GPU 代码中支持断点的调试工具必须在编译时就位，因为放置或删除断点是您在运行时（或调试时，如果您愿意）做出的选择。