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Trace generation tool for Macsim

This tool is not yet stable, so please send me a teams message (Euijun Chung) if there are some issues.

Usage

❗️Warning❗️ The trace generation tool may generate thousands of GBs of macsim traces on your machine in just a few minutes, so use at your own risk.

Installation

If you are working on rover, you can skip this step.

git clone and go to tools/main directory and make. You'll be able to see main.so if successfully compiled.

Usage

This tool works with any GPU programs including CUDA binaries and Tensorflow/Pytorch libraries. However, you should carefully choose the workload because even a very small workload can be too big for the tool to generate traces. For instance, training a very small CNN with a few iterations may fill hundreds of GBs and eventually blow up the storage of rover. This will impact other rover users so please use this tool wisely.

To generate traces, simply add a few lines to your original command. For instance, if you want to run python3 cnn_train.py, the following command will inject trace-generating instructions to the original workload and start generating Macsim traces. /fast_data/echung67/nvbit_release/tools/main/main.so is the path to the trace-generating tool that I wrote, so you can use it freely when you are using rover.

CUDA_INJECTION64_PATH=/fast_data/echung67/nvbit_release/tools/main/main.so python3 cnn_train.py

There are a few arguments that you can use:

  • INSTR_BEGIN: Beginning of the instruction interval on each kernel where to apply instrumentation. (default = 0)
  • INSTR_END: End of the instruction interval on each kernel where to apply instrumentation. (default = UINT32_MAX)
  • KERNEL_BEGIN: Beginning of the kernel interval where to generate traces. (default = 0)
  • KERNEL_END: End of the kernel interval where to generate traces. (default = UINT32_MAX)
  • TOOL_VERBOSE: Enable verbosity inside the tool. (default = 0)
  • TRACE_PATH: Path to trace file. (default = './')
  • COMPRESSOR_PATH: Path to the compressor binary file. (default = '/fast_data/echung67/nvbit_release/tools/main/compress')
  • DEBUG_TRACE: Generate human-readable debug traces together when this value is 1. (default = 0)
  • OVERWRITE: Overwrite the previously generated traces in TRACE_PATH directory when this value is 1. (default = 0)

Use with CUDA Kernel Sampling

More details about kernel sampling is coming soooooooooon..

python3 kernel_sample.py --cmd "python3 for-macsim/$name.py" --name "$name" \
    --threshold 50 --min_n 30 --device_id 1 \
    --trace_generate --trace_path /data/echung67/trace_sampled/nvbit/"$name"

Example

Please check out run.sh for examples.

$ CUDA_INJECTION64_PATH=/fast_data/echung67/nvbit_release/tools/main/main.so \
  TRACE_PATH=./ \
  KERNEL_END=5 \
  DEBUG_TRACE=1 \
  OVERWRITE=1 \
  python3 m.py

This command will generate traces for the first 5 CUDA kernels of the workload python3 m.py. Also, the tool will overwrite the previous traces and generate the debug traces as well.

Using the python script

  1. Open nvbit.py
  2. Go to if __name__ == '__main__': line
  3. There are three functions inside the main function right now: rodinia(), fast_tf(), tango(), corresponding to each benchmark suites.
  • If the fast argument of each function is True, it means that the traces will be saved in /fast_data/echung67/trace/nvbit/....
  • If False, it will be saved in /data/echung67/trace/nvbit/....
  • The trace becomes extremely huge for certain benchmarks such as FasterTransformer, so I had to use the /data/... (HDD) to store the traces.
  1. Decomment function that you want to run, or you can create a new function (such as gunrock()) if you want to create traces for a new benchmark.
  2. Run $ python3 nvbit.py and the traces will be first generated in run/<bench_name>/<bench_config>/, compressed with zlib (source: tools/main/compress.cc), then will be moved to /fast_data/echung67/trace/....
  • Due to insufficient amount of GPU's VRAM, the trace generation should be done sequentially.
  1. macsim.py is for running Macsim concurrently.

Lines you should change in nvbit.py

Let's assume you are copying tango() function to create a new function that creates traces of a new benchmark suite. The following variables should be changed.

  • trace_path_base: path to the directory that this tool will save the traces
  • tango_bin: this used to be the path to the tango binary, so you should change it to path to the new benchmark's binary file. (change the name too!)
  • nvbit_bin: path to the nvbit tool that generates traces. don't forget to $ cd tools && make to generate this binary file.
  • compress_bin: path to the zlib compress tool.
  • result_dir: path to the directory that will store all the results and logs. It is set to ./run/ by default.
  • benchmark_names: name of the individual benchmarks in the benchmark suite.
  • benchmark_configs: configuration arguments for each benchmark in the benchmark suite.

What the for-loop does:

  1. Create directory for one benchmark and one configuration. If the trace generation has failed, it will try to regenerate and overwrite the traces.
  2. Copy macsim files to each directories.
  3. Create nvbit.py file in each directories. This python script will run the nvbit tool in the directory , compress the traces, and move it to trace_path_base.
  4. Run nvbit.py in each directories.

Current issues with the tool

  1. The NVBit tool randomly creates segfault before it terminates. This issue is usually gone if we run the tool several times, so in the script I made the trace generation tool to run again if there is a segfault in the result log. However, I couldn't figure out why the segfault happens randomly..

NVBit (NVidia Binary Instrumentation Tool)

NVIDIA Corporation

NVBit is covered by the same End User License Agreement as that of the NVIDIA CUDA Toolkit. By using NVBit you agree to End User License Agreement described in the EULA.txt file.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing

Introduction

NVBit (NVidia Binary Instrumentation Tool) is a research prototype of a dynamic binary instrumentation library for NVIDIA GPUs.

NVBit provides a set of simple APIs that enable writing a variety of instrumentation tools. Example of instrumentation tools are: dynamic instruction counters, instruction tracers, memory reference tracers, profiling tools, etc.

NVBit allows writing instrumentation tools (which we call NVBit tools) that can inspect and modify the assembly code (SASS) of a GPU application without requiring recompilation, thus dynamic. NVBit allows instrumentation tools to inspect the SASS instructions of each function (__global__ or __device__) as it is loaded for the first time in the GPU. During this phase is possible to inject one or more instrumentation calls to arbitrary device functions before (or after) a SASS instruction. It is also possible to remove SASS instructions, although in this case NVBit does not guarantee that the application will continue to work correctly.

NVBit tries to be as low overhead as possible, although any injection of instrumentation function has an associated cost due to saving and restoring application state before and after jumping to/from the instrumentation function.

Because NVBit does not require application source code, any pre-compiled GPU application should work regardless of which compiler (or version) has been used (i.e. nvcc, pgicc, etc).

Requirements

  • SM compute capability: >= 3.5 && <= 8.6
  • Host CPU: x86_64, ppc64le, aarch64
  • OS: Linux
  • GCC version : >= 5.3.0 for x86_64; >= 7.4.0 for ppc64le and aarch64
  • CUDA version: >= 10.1
  • CUDA driver version: <= 510.xx

Currently no Embedded GPUs or ARMs host are supported.

Getting Started with NVBit

NVBit is provided in a .tgz file containing this README file and three folders:

  1. A core folder, which contains the main static library libnvbit.a and various headers files (among which the nvbit.h file which contains all the main NVBit APIs declarations).
  2. A tools folder, which contains various source code examples of NVBit tools. A new user of NVBit, after familiarizing with these pre-existing tools will typically make a copy of one of them and modify appropriately.
  3. A test-apps folder, which contains a simple application that can be used to test NVBit tools. There is nothing special about this application, it is a simple vector addition program.

To compile the NVBit tools simply type make from inside the tools folder (make sure nvcc is in your PATH). Compile the test application by typing make inside the test-apps folder. Note: if you are making your own tool, make sure you link it to c++ standard library, which is required by NVBit, otherwise, you might see missing symbol errors. nvcc does it by default, but if you specify your own host compiler using nvcc -ccbin=<compiler>, you need to point to a c++ compiler or add -lstdc++.

Using an NVBit tool

Before running an NVBit tool, make sure nvdisasm is in your PATH. In Ubuntu distributions this is typically done by adding /usr/local/cuda/bin or /usr/local/cuda-"version"/bin to the PATH environment variable.

To use an NVBit tool we simply LD_PRELOAD the tool before the application execution command. Alternatively, you can use CUDA_INJECTION64_PATH instead if LD_PRELOAD does not work for you. Because some workloads, such as pytorch would overwrite LD_PRELOAD internally, making the NVBit tool not loaded.

NOTE: NVBit uses the same mechanism as nvprof, nsight system, and nsight compute, thus they cannot be used together.

For instance if the application vector add runs natively as:

./test-apps/vectoradd/vectoradd

and produces the following output:

Final sum = 100000.000000; sum/n = 1.000000 (should be ~1)

we would use the NVBit tool which performs instruction count as follow:

LD_PRELOAD=./tools/instr_count/instr_count.so ./test-apps/vectoradd/vectoradd

or

CUDA_INJECTION64_PATH=./tools/instr_count/instr_count.so ./test-apps/vectoradd/vectoradd

The output for this command should be the following:

------------- NVBit (NVidia Binary Instrumentation Tool) Loaded --------------
NVBit core environment variables (mostly for nvbit-devs):
            NVDISASM = nvdisasm - override default nvdisasm found in PATH
            NOBANNER = 0 - if set, does not print this banner
-----------------------------------------------------------------------------
         INSTR_BEGIN = 0 - Beginning of the instruction interval where to apply instrumentation
           INSTR_END = 4294967295 - End of the instruction interval where to apply instrumentation
        KERNEL_BEGIN = 0 - Beginning of the kernel launch interval where to apply instrumentation
          KERNEL_END = 4294967295 - End of the kernel launch interval where to apply instrumentation
    COUNT_WARP_LEVEL = 1 - Count warp level or thread level instructions
    EXCLUDE_PRED_OFF = 0 - Exclude predicated off instruction from count
        TOOL_VERBOSE = 0 - Enable verbosity inside the tool
----------------------------------------------------------------------------------------------------
kernel 0 - vecAdd(double*, double*, double*, int) - #thread-blocks 98,  kernel instructions 50077, total instructions 50077
Final sum = 100000.000000; sum/n = 1.000000 (should be ~1)

As we can see, before the original output, there is a print showing the kernel call index "0", the kernel function prototype "vecAdd(double*, double*, double*, int)", total number of thread blocks launched in this kernel "98", the number of executed instructions in the kernel "50077", and for the all application "50077".

When the application starts, also two banners are printed showing the environment variables (and their current values) that can be used to control the NVBit core or the specific NVBit Tool. Mostly of the NVBit core environment variable are used for core debugging/development purposes. Set the environment value NOBANNER=1 to disable the core banner if that information is not wanted.

Examples of NVBit Tools

As explained above, inside the tools folder there are few example of NVBit tools. Rather than describing all of them in this README file we refer to comment in the source code of each one them.

The natural order (in terms of complexity) to learn these tools is:

  1. instr_count: Perform thread level instruction count. Specifically, a function is injected before each SASS instruction. Inside this function the total number of active threads in a warp is computed and a global counter is incremented.

  2. opcode_hist: Generate an histogram of all executed instructions.

  3. mov_replace: Replace each SASS instruction of type MOV with an equivalent function. This tool make use of the read/write register functionality within the instrumentation function.

  4. instr_countbb: Perform thread level instruction count by instrumenting basic blocks. The final result is the same as instr_count, but mush faster since less instructions are instrumented (only the first instruction in each basic block is instrumented and the counter).

  5. mem_printf: Print memory reference addresses for each global LOAD/STORE using the GPU side printf. This is accomplished by injecting an instrumentation function before each SASS instruction performing global LOAD/STORE, passing the register values and immediate used by that instruction (used to compute the resulting memory address) and performing the printf.

  6. mem_trace: Trace memory reference addresses. This NVBit tool works similarly to the above example but instead of using a GPU side printf it uses a communication channel (provided in utils/channel.hpp) to transfer data from GPU-to-CPU and it performs the printf on the CPU side.

We also suggest to take a look to nvbit.h (and comments in it) to get familiar with the NVBit APIs.