Compiler-Guided Tensor Slicing for Low-Latency Parallel Execution
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Solution Overview
Problem
Existing neural network execution methods face inefficiencies due to suboptimal tensor slicing options, leading to increased computational latency and overhead in parallel processing, particularly in tensor contraction operations, which can adversely impact performance when mismatched slicing leads to excessive data rearrangement.
Innovation Solution
A compiler is employed to manage tensor parallel execution by analyzing neural network models, identifying tensor contraction operations, and using a cost function to determine optimal slicing dimensions, considering the impact on subsequent operations and propagating slicing options to minimize communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tensor slicing is performed without compiler management, then parallel processing can be implemented, but computational latency increases due to suboptimal slicing options
Solution Approach 1:
The compiler performs preliminary analysis of the neural network model to identify tensor contraction operations and determine optimal slicing dimensions before execution. This advance planning allows the system to prepare the most efficient parallel execution strategy, avoiding runtime decisions that would increase computational latency.
Solution Approach 2:
The cost function evaluates different slicing options by considering the impact on subsequent operations and data rearrangement requirements. This feedback mechanism allows the compiler to select slicing strategies that minimize overall computational latency while maintaining parallel processing benefits.
2Productivity
If mismatched slicing is used in tensor contraction operations, then parallel execution can be achieved, but overhead increases due to excessive data rearrangement
Solution Approach 1:
The compiler analyzes the computational flow and propagates slicing options through the network before execution. By determining consistent slicing dimensions in advance for related tensor operations, the system avoids runtime data rearrangement and reduces overhead associated with mismatched slicing.
Solution Approach 2:
The cost function evaluates different slicing dimension parameters and selects the configuration that minimizes data rearrangement overhead. By optimizing these parameters beforehand, the system achieves parallel execution efficiency without the penalty of excessive data movement during runtime.
3Productivity
If suboptimal tensor slicing options are used, then parallel processing can be implemented, but memory usage increases due to inefficient data distribution
Solution Approach 1:
The compiler performs preliminary optimization by determining optimal slicing dimensions that balance parallel processing requirements with memory constraints. This advance planning ensures efficient data distribution across compute engines, reducing redundant data storage and lowering overall memory usage.
Solution Approach 2:
The cost function evaluates slicing parameters to find the optimal configuration that minimizes memory usage while maintaining parallel processing capability. By adjusting these parameters beforehand, the system achieves efficient data distribution without excessive memory consumption.
Data Source
AI summary
Techniques for implementing tensor parallel execution can include identifying a first tensor contraction operation in a compute flow, and slicing the first tensor contraction operation into a first set of multiple tensor contraction portions to have each compute engine of multiple compute engines perform a portion of the first tensor contraction operation. A set of slicing options can then be determined for a second tensor contraction operation that operates on a tensor result of the first tensor contraction operation. A cost for each slicing option is determined, and a slicing option having the lowest cost is selected. The second tensor contraction operation is sliced according to the selected slicing option to have each compute engine perform a portion of the second tensor contraction operation. Collective compute operations can be inserted in the compute flow for the first and second tensor contraction operations.