innsikt - Algorithms and Data Structures - # Pipelined DNN Inference Partitioning

Optimizing Pipelined Inference of Deep Neural Networks for Maximum Throughput

Q: How can the proposed techniques be extended to handle more complex pipeline structures beyond linear pipelines, such as branching and merging

To extend the proposed techniques to handle more complex pipeline structures beyond linear pipelines, such as branching and merging, we can introduce additional constraints and variables in the optimization formulations. For branching pipelines, where a single stage may split into multiple stages, we can introduce constraints that ensure the correct flow of data between the branches and that the bottleneck is appropriately identified. This may involve modeling the dependencies between the branches and adjusting the communication costs accordingly. For merging pipelines, where multiple stages converge into a single stage, we can modify the objective function to account for the combined workload and communication costs from the different branches. By appropriately formulating the optimization problem to handle these branching and merging scenarios, we can optimize the pipeline partitioning for more complex pipeline structures.

Q: What are the implications of the hardness result for MTPP on the design of practical heuristics and approximation algorithms for this problem

The implications of the hardness result for MTPP on the design of practical heuristics and approximation algorithms are significant. Since MTPP is proven to be NP-hard, it implies that finding an optimal solution in polynomial time is unlikely. Therefore, practical heuristics and approximation algorithms are essential for solving this problem efficiently in real-world scenarios. The hardness result suggests that approximation algorithms with provable guarantees are valuable for tackling MTPP. By designing approximation algorithms that can provide near-optimal solutions within a reasonable time frame, practitioners can make informed decisions about pipeline partitioning without the need for exhaustive search. Additionally, the hardness result highlights the importance of developing heuristic algorithms that can quickly generate high-quality solutions for MTPP. These heuristics may leverage insights from the hardness result to focus on specific aspects of the problem that are more amenable to efficient optimization.

Q: Can the insights from this work on optimizing pipelined DNN inference be applied to other domains beyond machine learning, such as scientific computing or data processing pipelines

The insights from optimizing pipelined DNN inference can indeed be applied to other domains beyond machine learning, such as scientific computing or data processing pipelines. The principles of partitioning model graphs into stages to maximize throughput while minimizing communication overhead are applicable in various computational domains. In scientific computing, where complex simulations and computations are performed, optimizing the pipeline structure can improve efficiency and reduce computational time. By partitioning the computational tasks into stages and optimizing the flow of data between these stages, researchers can enhance the performance of scientific simulations. Similarly, in data processing pipelines, optimizing the pipeline structure can lead to faster data processing and analysis. By applying the techniques developed for pipelined DNN inference, data engineers can design efficient data processing pipelines that maximize throughput and minimize latency, improving overall data processing efficiency.

Grunnleggende konsepter

We optimize pipeline parallelism for deep neural network (DNN) inference by partitioning model graphs into k stages and minimizing the running time of the bottleneck stage, including communication.

Sammendrag

The authors address the problem of maximizing throughput for pipelined DNN inference by partitioning the computation graph into k stages. The key challenges are:

Minimizing the communication overhead between stages, as the inter-stage bandwidth is typically much slower than intra-stage bandwidth.
Balancing the running time across all stages to avoid a bottleneck that limits the overall throughput.

The authors formulate this as the Max-Throughput Partitioning Problem (MTPP), which is shown to be NP-hard. They propose:

Novel mixed-integer programming (MIP) formulations to compute strong lower bounds on the optimal solution, including a "three-superblock" relaxation and a "guess the bottleneck block" approach.
A fast and practical pipeline partitioning algorithm called SliceGraph that combines dynamic programming with a biased random-key genetic algorithm.

Extensive experiments on a diverse testbed of 369 production DNN models show that the MIP-based lower bounds are substantially stronger than standard combinatorial bounds. For example, for k=16 pipeline stages, the MIP formulations raised the lower bound from 0.4598 to 0.9452, expressed as a fraction of the best partition found. This closes the optimality gap by a factor of 9.855x.

The authors demonstrate that SliceGraph is highly effective in practice, producing solutions that are on average 95.5% of the optimal for k≤16, while being orders of magnitude faster to compute than the MIP-based lower bounds.

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Statistikk

The geometric mean of the best available lower bound from the MIP hierarchy, normalized by the best solution found using BKRGA, across the production dataset:
For k=2: 0.9901
For k=4: 0.9737
For k=8: 0.9588
For k=16: 0.9452
For k=32: 0.8749
For k=64: 0.7874

Sitater

"Evaluated via geometric means across our production testbed with k = 16 pipeline stages, our MIP formulations raised the lower bound from 0.4598 to 0.9452, expressed as a fraction of the best partition found. In other words, our improved lower bounds closed the optimality gap by a factor of 9.855x."

Viktige innsikter hentet fra

Practical Performance Guarantees for Pipelined DNN Inference

by Aaron Archer... klokken arxiv.org 05-06-2024

https://arxiv.org/pdf/2311.03703.pdf

Practical Performance Guarantees for Pipelined DNN Inference

Dypere Spørsmål

How can the proposed techniques be extended to handle more complex pipeline structures beyond linear pipelines, such as branching and merging

To extend the proposed techniques to handle more complex pipeline structures beyond linear pipelines, such as branching and merging, we can introduce additional constraints and variables in the optimization formulations.
For branching pipelines, where a single stage may split into multiple stages, we can introduce constraints that ensure the correct flow of data between the branches and that the bottleneck is appropriately identified. This may involve modeling the dependencies between the branches and adjusting the communication costs accordingly.
For merging pipelines, where multiple stages converge into a single stage, we can modify the objective function to account for the combined workload and communication costs from the different branches. By appropriately formulating the optimization problem to handle these branching and merging scenarios, we can optimize the pipeline partitioning for more complex pipeline structures.

What are the implications of the hardness result for MTPP on the design of practical heuristics and approximation algorithms for this problem

The implications of the hardness result for MTPP on the design of practical heuristics and approximation algorithms are significant. Since MTPP is proven to be NP-hard, it implies that finding an optimal solution in polynomial time is unlikely. Therefore, practical heuristics and approximation algorithms are essential for solving this problem efficiently in real-world scenarios.
The hardness result suggests that approximation algorithms with provable guarantees are valuable for tackling MTPP. By designing approximation algorithms that can provide near-optimal solutions within a reasonable time frame, practitioners can make informed decisions about pipeline partitioning without the need for exhaustive search.
Additionally, the hardness result highlights the importance of developing heuristic algorithms that can quickly generate high-quality solutions for MTPP. These heuristics may leverage insights from the hardness result to focus on specific aspects of the problem that are more amenable to efficient optimization.

Can the insights from this work on optimizing pipelined DNN inference be applied to other domains beyond machine learning, such as scientific computing or data processing pipelines

The insights from optimizing pipelined DNN inference can indeed be applied to other domains beyond machine learning, such as scientific computing or data processing pipelines. The principles of partitioning model graphs into stages to maximize throughput while minimizing communication overhead are applicable in various computational domains.
In scientific computing, where complex simulations and computations are performed, optimizing the pipeline structure can improve efficiency and reduce computational time. By partitioning the computational tasks into stages and optimizing the flow of data between these stages, researchers can enhance the performance of scientific simulations.
Similarly, in data processing pipelines, optimizing the pipeline structure can lead to faster data processing and analysis. By applying the techniques developed for pipelined DNN inference, data engineers can design efficient data processing pipelines that maximize throughput and minimize latency, improving overall data processing efficiency.