When training a simple two-layer Multi-Layer Perceptron (MLP) distributed across multiple devices, such as a CPU and two GPUs, the system forms a complex computational graph with strict dependencies between computation and communication. For example, a computed gradient on a GPU must be ready before it can be transferred to the CPU. Manually scheduling the parallel and sequential execution of these intertwined operations would be exceedingly difficult, which makes relying on a graph-based computing backend highly advantageous for automatic optimization.

Computational Graph of a Multi-Device Two-Layer MLP

Manually scheduling a parallel program—such as training a multi-layer neural network across a CPU and multiple GPUs—is an extremely complex process due to the intricate dependencies between computation tasks and cross-device communication. Deep learning frameworks overcome this challenge by utilizing graph-based computing backends. By analyzing the complete computational graph and its dependencies, the backend can automatically schedule and overlap independent operations across different devices, sparing developers from explicitly programming parallel execution.

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As a deep learning framework's backend continually retrieves and processes queued instructions from the frontend, it utilizes a dependency graph to dictate the proper sequence of execution. The backend meticulously monitors dependencies between distinct operations within the computational graph, guaranteeing that any task relying on the output of preceding steps pauses until those required results are computed. As a result, while independent tasks can be seamlessly parallelized for efficiency, mutually dependent operations are inherently constrained from executing simultaneously.

Backend Dependency Tracking in Computational Graphs

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To illustrate how a deep learning backend tracks dependencies in a computational graph, consider a toy example computing $$z = x * y + 2$$. When the Python frontend thread executes the assignment statements for $$x$$, $$y$$, and $$z$$, it simply returns these tasks to the backend queue without performing the mathematical operations. The Python frontend thread will only pause and wait for the C++ backend thread to finish computing the result of the variable $$z$$ when that result explicitly needs to be printed. Because the Python frontend thread is freed from performing the actual computations, this decoupled design ensures there is little impact on the program's overall performance, regardless of Python's slower execution speed.

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