Implementation of Computation Graph Generation Algorithm

[Sahil2004]

Description

We need to implement an algorithm for generating computation graphs. This algorithm should efficiently create and manage computation graphs, which are essential for various computational tasks such as neural network training, automatic differentiation, and more. The primary objectives are to ensure correctness, optimize performance, and provide flexibility for different types of computations.

Requirements

1. Graph Construction:

   • Develop the core algorithm to construct computation graphs from a given set of operations.
   • Ensure support for common operations and their dependencies.

2. Node and Edge Management:

   • Implement mechanisms to add, remove, and manage nodes and edges within the graph.
   • Ensure the integrity and consistency of the graph structure.

3. Optimization:

   • Integrate techniques to optimize the computation graph for efficient execution.
   • Include support for operation fusion, graph pruning, and other optimization strategies.

4. Execution Engine:

   • Develop an execution engine to traverse and execute the computation graph.
   • Ensure support for parallel and distributed execution where applicable.

Tasks

1. Graph Construction

   • [ ] Develop the core algorithm for constructing computation graphs.
   • [ ] Ensure support for common operations (e.g., matrix multiplication, addition).
   • [ ] Handle dependencies and ordering of operations.

2. Node and Edge Management

   • [ ] Implement functions to add and remove nodes and edges.
   • [ ] Ensure the graph maintains integrity and consistency.
   • [ ] Implement mechanisms to visualize the graph structure.

3. Optimization

   • [ ] Integrate operation fusion techniques to optimize graph execution.
   • [ ] Develop graph pruning strategies.
   • [ ] Explore and integrate additional optimization techniques.

4. Execution Engine

   • [ ] Develop an engine to execute the computation graph.
   • [ ] Ensure support for parallel execution.
   • [ ] Implement distributed execution capabilities if applicable.

Additional Information

• Target Applications: The computation graph generation algorithm should be applicable to neural network training, automatic differentiation, and other computational tasks.
• Performance Metrics: Measure and report metrics such as execution time, memory usage, and scalability.
• Flexibility: Ensure the algorithm can be easily extended to support new operations and optimization techniques.

References

• Computation Graphs in Deep Learning
• Automatic Differentiation
• Graph Optimization Techniques

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Please comment if you have any suggestions or questions regarding this implementation plan.

[rajputdikshant]

assign me this

[Sahil2004]

assign me this

Alright. You are assigned this issue. Looking forward to a PR asap following all the guidelines :)

[UJJWALTHAKUR28]

assign me

[Sahil2004]

assign me

Assigned. Looking forward to a collaborative PR.