Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning

Belangrijkste concepten

TACOS is an automated synthesizer that generates topology-aware collective algorithms for common distributed machine learning collectives across arbitrary input network topologies.

Samenvatting

The paper introduces TACOS, a framework that can autonomously synthesize topology-aware collective algorithms for distributed machine learning workloads. Key highlights:

1. TACOS represents the network topology and collective patterns using a Time-Expanded Network (TEN) formulation, enabling an elegant and scalable approach to the synthesis problem.

2. TACOS supports a comprehensive array of arbitrary, heterogeneous and asymmetric topologies, including scenarios such as NPU failures or multi-tenant collectives. It incorporates network contention effects during the synthesis process.

3. TACOS employs a novel Greedy-based matching heuristic to efficiently synthesize collective algorithms, in contrast to previous NP-hard optimization-based approaches. This enables TACOS to scale to large topologies with 40K NPUs, completing synthesis in 2.52 hours.

4. Compared to state-of-the-art TACCL, TACOS achieves up to 4.27x speedup for a 64-NPU system. When applied to end-to-end training on a 256-NPU system, workloads running the TACOS-synthesized algorithm show 1.44x average speedup over the baseline.

5. TACOS is completely automated, without the need for any expert knowledge or human intervention.

Statistieken

The paper presents the following key metrics:

TACOS synthesized an All-Reduce algorithm for a heterogeneous 512-NPU system in just 6.09 minutes.
For a 40K Mesh topology, the TACOS synthesis took 2.52 hours.
TACOS exhibits a quadratic relationship between synthesis time and number of NPUs, in contrast to the NP-hard nature of previous works.
On a 64-NPU system, TACOS achieved up to 4.27x speedup over the state-of-the-art TACCL.
On a 256-NPU system, workloads running the TACOS-synthesized algorithm showed 1.44x average speedup over the baseline.

Citaten

"TACOS is the first work to introduce the notion of TEN into the space of distributed ML, enabling an elegant representation of the problem and solution."
"TACOS supports a comprehensive array of arbitrary, heterogeneous and asymmetric topologies. This includes scenarios such as NPU failures or multi-tenant collectives."
"TACOS enables collective synthesis for large-scale topologies with manageable synthesis time by approaching it as a greedy-based matching problem rather than optimization."

Belangrijkste Inzichten Gedestilleerd Uit

TACOS

by William Won,... om arxiv.org 04-01-2024

https://arxiv.org/pdf/2304.05301.pdf

Diepere vragen

How can TACOS be extended to handle more complex collective patterns beyond All-Reduce, such as All-to-All

To extend TACOS to handle more complex collective patterns like All-to-All, the framework would need to incorporate additional logic and algorithms to account for the intricate communication patterns involved. One approach could be to modify the existing Greedy-based matching heuristic to consider multiple source and destination pairs for each chunk, allowing for more flexible routing decisions. Additionally, TACOS could be enhanced to support the aggregation and distribution of data across all NPUs in a network, enabling efficient All-to-All communication. By expanding the capabilities of TACOS to encompass a broader range of collective patterns, it can provide comprehensive optimization for diverse distributed machine learning workloads.

What are the potential limitations or drawbacks of the Greedy-based matching heuristic used in TACOS, and how could it be further improved

While the Greedy-based matching heuristic used in TACOS offers a simple and efficient way to maximize network resource utilization, it may have limitations in certain scenarios. One potential drawback is that the greedy approach may not always result in the optimal solution, especially in complex network topologies with varying link costs and congestion levels. To address this, the heuristic could be enhanced by incorporating heuristics that consider future time steps or by implementing backtracking mechanisms to revisit and revise previous matching decisions. Additionally, introducing randomness or probabilistic elements in the matching process could help explore a wider solution space and potentially improve the overall performance of the algorithm.

Given the importance of topology-aware collectives, how might TACOS be integrated into existing distributed ML frameworks to seamlessly optimize communication performance

Integrating TACOS into existing distributed ML frameworks can significantly enhance communication performance and overall system efficiency. By seamlessly incorporating TACOS into the workflow, ML practitioners can leverage its automated synthesizer to generate topology-aware collective algorithms tailored to their specific network configurations. This integration could involve developing APIs or plugins that allow the ML framework to interact with TACOS, providing input parameters such as network topology and collective patterns. Furthermore, TACOS could be designed as a standalone service that can be accessed and utilized by distributed ML frameworks through standard communication protocols. By streamlining the integration process and offering easy-to-use interfaces, TACOS can become an indispensable tool for optimizing communication in distributed machine learning clusters.