Neural Network CUDA Streams for Parallel Pipeline Execution
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Solution Overview
Problem
Existing application programming interfaces for executing neural networks can result in inefficient code due to their complexity, leading to suboptimal performance.
Innovation Solution
An application programming interface that utilizes CUDA execution streams to optimize the execution of neural networks by subdividing operations into concurrent streams, leveraging techniques such as loop fusion, loop unrolling, data-flow analysis, and parallelization, and synchronizing basic blocks to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional APIs are used to execute neural networks, then the implementation is straightforward, but the execution efficiency is suboptimal due to code complexity and lack of parallelization
Solution Approach 1:
The patent segments the neural network execution into multiple independent CUDA streams, where each stream handles specific operations (e.g., stream 0 for weight bias loading, stream 1 for activation computations, stream 2 for output computations). This segmentation enables parallel execution of otherwise sequential operations, improving execution efficiency while managing complexity through structured stream organization.
Solution Approach 2:
The patent introduces the dimension of parallel execution streams to transform the traditionally sequential neural network execution into concurrent operations. By adding the stream dimension, operations that were previously executed one after another can now proceed simultaneously across multiple streams, fundamentally changing the execution model from sequential to parallel.
2Loss of time
If operations are executed sequentially in a single stream, then the code is simpler to implement, but the execution time increases due to lack of overlapping memory copies and kernel executions
Solution Approach 1:
The patent performs preliminary actions by pre-loading weights and biases into device memory before the main computation begins. This preliminary data preparation in stream 0 allows subsequent computation streams to proceed without waiting for memory transfers, reducing execution time by overlapping data preparation with computation in other streams.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining multiple active CUDA streams that execute computations continuously without idle waiting periods. While one stream is performing kernel computations, other streams simultaneously perform memory copies or preparatory operations, eliminating gaps in useful work and reducing overall execution time.
3Productivity
If multiple CUDA streams are used for parallel execution, then the execution efficiency improves through overlapping operations, but the synchronization and management complexity increases
Solution Approach 1:
The patent implements feedback mechanisms through synchronization points where streams wait for dependencies to be resolved before proceeding. This feedback-based synchronization ensures that streams coordinate their execution based on actual completion status of dependent operations, maintaining correctness while enabling parallel execution. The synchronization acts as feedback that regulates the flow between streams.
4Productivity
If loop fusion and unrolling techniques are applied, then the execution performance improves through better data reuse and reduced overhead, but the code complexity and memory requirements increase
Solution Approach 1:
The patent applies loop fusion by merging multiple sequential operations into single fused kernels where possible. This merging allows intermediate results to be reused without writing back to memory, improving data reuse and execution performance. The fused operations are executed in-stream, reducing memory traffic while maintaining the benefits of parallel stream execution.
Data Source
AI summary
Apparatuses, systems, and techniques to perform a neural network. In at least one embodiment, an application programming interface schedules two or more graph nodes to be performed by two or more parallel processing pipelines based, at least in part, on an order of layers in the neural network.