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eBay
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Description

High Efficiency Convolution Kernel for Maxwell GPU Architecture

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maxDNN

High Efficiency Convolution Kernel for NVIDIA Maxwell GPU Architecture

Introduction

maxDNN is an existence proof that high efficiency convolution GPU kernels are possible. We solve the problem for the forward propagation phase of convolutional neural networks, and achieve roughly 95% computational efficiency for a typical network layer. This is a significant improvement over the state of the art which performs in the 30%-75% range.

The kernel runs on NVIDIA Maxwell GPUs (eg, Geforce GTX980) and is a derivative work of the SGEMM kernel from the Maxas Maxwell Assembler project.

Technical details and performance analysis of the maxDNN kernel are documented in the report maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Cite as:

@Article{Lavin:2015md,
     author    = "Andrew Lavin",
     title     = "{maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs}",
     year      = "2015",
     archivePrefix = "arXiv",
     eprint        = "1501.06633",
     primaryClass  = "cs.NE",
}

Requirements

  • NVIDIA GPU of compute capability 5.0 or better (i.e., Maxwell GPU)
  • Linux (tested with Ubuntu 12.04, 14.04)
  • CUDA Toolkit (tested with version 6.5): https://developer.nvidia.com/cuda-downloads
  • UnitTest++ unit testing framework: https://github.com/unittest-cpp/unittest-cpp
  • cuDNN library version 2: https://developer.nvidia.com/cuDNN
  • MaxAs Assembler for NVIDIA Maxwell Architecture: https://github.com/NervanaSystems/maxas

Install

  1. Get the software.

    git clone https://github.com/eBay/maxDNN.git
    
  2. Install the external requirements above. Add the CUDA Toolkit bin and lib64 directories to the PATH and LDLIBRARYPATH environment variables, respectively. For example:

    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
    export PATH=$PATH:/usr/local/cuda/bin
    
  3. Set the PERL5LIB environment variable to point to the maxas folder. For example:

    export PERL5LIB=~/develop/externals/maxas
    
  4. Edit maxdnn/Makefile to ensure that CUDAPATH, CUDNNPATH, UNITTESTPATH, and MAXASPATH are correct for your system.

  5. Build.

    cd maxDNN/maxdnn
    make all
    
  6. Run the convolution suite unit tests:

    ./maxdnn_test.bin suite convolution
    
  7. or run all unit tests:

    ./maxdnn_test.bin
    
  8. Optionally, run the tests again, checking result accuracy against the result of CPU convolution. Do so by setting the path where generated reference data should be stored. Generating reference data will take about 30 minutes, because the CPU convolution is very slow, but subsequent runs will just read it from disk instead of generating it again.

    export maxdnn_test_data=/path/to/test/data/storage
    ./maxdnn_test.bin suite convolution
    
  9. If you want to run maxdnntest from another directory, you must set the maxdnncubindir environment variable to point to the directory that contains the file multiconvolution64.cubin

Benchmark

You can use nvprof to measure the efficiency of GPU kernels. The following sample gathers all the statistics that were used in the tech report to measure computational efficiency for Alexnet layer conv1:

nvprof -s --metrics flop_sp_efficiency,inst_fp_32,inst_integer,inst_bit_convert,inst_control,inst_misc,inst_executed,tex_cache_hit_rate,l2_tex_read_hit_rate --events elapsed_cycles_sm ./maxdnn_test.bin convolve_maxdnn_alexnet_conv1

==13380== NVPROF is profiling process 13380, command: ./maxdnn_test.bin convolve_maxdnn_alexnet_conv1 Running test convolve_maxdnn_alexnet_conv1 ==13380== Warning: Some kernel(s) will be replayed on device 0 in order to collect all events/metrics. Success: 1 tests passed. Test time: 87.08 seconds. ==13380== Profiling application: ./maxdnn_test.bin convolve_maxdnn_alexnet_conv1 ==13380== Profiling result: ==13380== Event result: Invocations Event Name Min Max Avg Device "GeForce GTX 980 (0)" Kernel: multiconvolution_64 10 elapsed_cycles_sm 75288048 75580076 75451292

==13380== Metric result: Invocations Metric Name Metric Description Min Max Avg Device "GeForce GTX 980 (0)" Kernel: multiconvolution_64 10 inst_executed Instructions Executed 320480600 320480600 320480600 10 inst_fp_32 FP Instructions(Single) 9144115200 9144115200 9144115200 10 inst_integer Integer Instructions 240064000 240064000 240064000 10 inst_bit_convert Bit-Convert Instructions 0 0 0 10 inst_control Control-Flow Instructions 24006400 24006400 24006400 10 inst_misc Misc Instructions 26329600 26329600 26329600 10 tex_cache_hit_rate Texture Cache Hit Rate 10.36% 10.74% 10.48% 10 l2_tex_read_hit_rate L2 Hit Rate (Texture Reads) 84.56% 85.43% 85.09% 10 flop_sp_efficiency FLOP Efficiency(Peak Single) 94.39% 94.76% 94.55%

Add Convolution Layers

Add convolution layers by editing the file networks/conf.cfg. You must recompile maxDNN each time you change the layer configuration.

Test individual network layers by naming them on the command line. The format of the name is convolve[maxdnn|cudnn][layer]

For example, to test just layer alexnet_conv2 using maxdnn, run

    ./maxdnn_test.bin convolve_maxdnn_alexnet_conv2

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