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Compute Graph Framework

An experimental implementation of a compute graph framework similar to TensorFlow using C++ and OpenCL.

Implemented during a term project for the course Multicore Programming Fundamentals during the winter semester 2017 at Yonsei University.

Applications using the framework can represent arbitrary computations on data as hardware-agnostic computation graphs using the Node classes exposed by the framework API. The framework is then able to compile that graph for a specific platform on which it can then be executed on specific input data.

Currently, all node operations are implemented on a sequential CPU and OpenCL/GPU platform.

In addition to the brief information on usage provided in this readme, you can find the project report which explains some of the fundamental design choices and concepts implemented and also provides a brief performance evaluation in the wiki.

Repository Structure

The repository contains the actual framework code in the cglib subdirectory as well as a plethora of small tests and examples in the tests subdirectory.

The framework code is split into general includes and platform-independent code in the src directories and the platform-specific implementations in the src-cpu and src-gpu subdirectories of the cglib folder.

All test exectuables are single C++-files and reside directly within the tests folder. They adhere to the following naming convention: (0s|1g)(c|p)<sequence nr>[b[g]]-<descriptive name>

  • 0s and 1g denote whether the executable tests a single node implementation or a full graph
  • c and p denote whether it is a correctness/sanity or a performance test.
  • <sequence nr> is a sequentially increasing number for ordering the samples
  • b and bg optionally denote baseline and OpenCL/GPU baseline implementations not making use of the framework

Compilation

Compilation is driven by CMake which will automatically detect OpenCL and PAPI. Finding PAPI might require setting the PAPI_PREFIX variable for CMake to the installation path of PAPI. PAPI is not required for compilation of the framework, but the performance test exectuables will not be built if PAPI cannot be found.

Building will produce to versions of the library, cgLibCPU and cgLibGPU as well as all test exectuables linked against both platform-specific libraries, identified with an additional c or g to the prefix-code denoting CPU or OpenCL/GPU code.

To build, create a separate build directory and invoke cmake as follows:

mkdir cglib-build && cd cglib-build
cmake [-DPAPI_PREFIX=<papi_directory>] ../computegraphlib
make

Make sure to specify Release or Debug build settings during the invocation of CMake if required.

Using the Framework

The following gives a very brief example on how to use the framework to compute z = a * (x + y).

#include "types.hpp"
#include "nodes/nodes.hpp"
#include "GraphCompiler.hpp"
#include "CompiledGraph.hpp"
#include "GraphCompilationPlatformFactory.hpp"

int main(int argc, const char* argv[])
{

    // create graph structure
    InputNode x("x");
    InputNode y("y");
    VariableNode a("a");

    VectorAddNode add(&x, &y);
    VectorMultNode z(&a, &add);

    // specify dimensions of inputs and compile graph to platform
    InputDimensionsMap inputDimensions;
    inputDimensions.emplace("x", MemoryDimensions({1, 5}));
    inputDimensions.emplace("y", MemoryDimensions({1, 5}));
    inputDimensions.emplace("a", MemoryDimensions({1, 1}));

    GraphCompiler compiler(std::unique_ptr<const GraphCompilationPlatformFactory>(new GraphCompilationPlatformFactory));
    const std::unique_ptr<CompiledGraph> graph = compiler.Compile(&z, inputDimensions);

    // in real code: have buffers holding the input data. here just stubs
    float dataA = 2.0f;
    float dataX[5] = { 1.f, 2.f, 3.f, 4.f, 5.f };
    float dataY[5] = { 5.f, 4.f, 3.f, 2.f, 1.f };
    float dataZ[5] = { 0.f, 0.f, 0.f, 0.f, 0.f };

    // initialize variables
    InputDataMap variableMap;
    variableMap.emplace("a", &dataA);
    graph->InitializeVariables(variableMap);

    // run graph on input data
    InputDataMap inputMap;
    inputMap.emplace("x", dataX);
    inputMap.emplace("y", dataY);
    graph->Evaluate(inputMap);

    // get output data
    graph->GetNodeData(&z, dataZ);

    return 0;
}

Selecting the Platform

Selecting the platform is currently done by simply linking against the desired library version of the framework. This will supply a platform-specific implementation of GraphCompilationPlatformFactory.

Code Quality

Unfortunately, the framework code is not the cleanest right now. There are plenty of code duplications, inconsistent use of syntax and unclean responsibilities which are in need of major refactoring. Please don't judge too hard ><.

Third Party Code

The repository includes files from the MNIST loader published at https://github.com/wichtounet/mnist under the MIT license used in the MNIST classifier example executables. These files are located in tests/mnist.