GPU-Based All-to-All Communication for Distributed Clusters
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Solution Overview
Problem
Conventional all-to-all communication algorithms in distributed computing systems are limited by data packet creation overhead for small messages and network contention due to bandwidth constraints for large messages, particularly in CPU-based systems.
Innovation Solution
The proposed solution exploits GPU parallelism to enhance all-to-all data communication by distributing data across clusters in a distributed system, utilizing GPU-initiated network operations to reduce packet creation latency and network contention, with a procedure that includes intra-cluster parallel data communication, inter-cluster data exchange, and intra-cluster data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional all-to-all algorithms are used in CPU-based systems, then data communication can be performed, but data packet creation overhead limits performance for small data messages
Solution Approach 1:
The patent replaces CPU-based packet creation with GPU-based packet creation. The GPU's parallel architecture allows multiple processing elements to generate data packets simultaneously, eliminating the sequential overhead that plagues CPU-based systems. This substitution of the mechanical processing system (CPU) with a parallel processing system (GPU) directly resolves the packet creation overhead problem for small messages.
Solution Approach 2:
The patent divides the communication system into clusters of processing elements within GPUs. Each processing element independently generates and manages its own data packets, allowing parallel packet creation across multiple segments (processing elements) simultaneously. This segmentation eliminates the single-point bottleneck in CPU-based packet creation.
2Quantity of substance
If conventional all-to-all algorithms are used with bandwidth constraints, then communication can occur, but network contention limits performance for large data messages
Solution Approach 1:
The patent segments the network communication into intra-cluster and inter-cluster stages. Within each cluster, multiple processing elements communicate in parallel through high-bandwidth interconnects, reducing the burden on external network bandwidth. This segmentation allows large data messages to be handled efficiently by distributing the communication load across multiple parallel paths rather than contending for limited external bandwidth.
Solution Approach 2:
The patent adds a spatial dimension to communication by organizing processing elements into clusters with hierarchical communication paths. Data can flow through multiple dimensions: within-cluster communication using high-bandwidth links, and between-cluster communication using network infrastructure. This multi-dimensional communication architecture reduces network contention by providing alternative communication paths and reducing dependency on any single network bottleneck.
3Loss of time
If GPU parallelism is exploited for all-to-all communication, then packet creation latency is reduced for small messages, but system complexity increases
Solution Approach 1:
The patent implements self-service by allowing each GPU processing element to independently generate and manage its own data packets without requiring centralized CPU intervention. The processing elements autonomously handle packet creation, routing information generation, and communication coordination, which reduces packet creation latency while the modular nature of this self-service approach keeps system complexity manageable through distributed autonomy.
4Loss of time
If inter-cluster messages are reduced for large data communication, then network contention is reduced, but communication protocol complexity increases
Solution Approach 1:
The patent segments communication into two distinct phases: intra-cluster communication for data aggregation and inter-cluster communication for data exchange. This segmentation allows the system to minimize inter-cluster messages by performing as much communication as possible within clusters using high-bandwidth interconnects, thereby reducing network contention. The protocol complexity is managed by clearly defining the boundaries and purposes of each communication phase.
Solution Approach 2:
The patent performs preliminary data aggregation and processing within clusters before initiating inter-cluster communication. By pre-processing and consolidating data within clusters, the system reduces the volume and number of messages that need to traverse the network, thereby reducing network contention. The communication protocol is designed to handle this two-stage process efficiently, with the complexity justified by the significant reduction in network overhead.
Data Source
AI summary
In accordance with described techniques for PE-centric all-to-all communication, a distributed computing system includes processing elements, such as graphics processing units, distributed in clusters. An all-to-all communication procedure is performed by the processing elements that are each configured to generate data packets in parallel for all-to-all data communication between the clusters. The all-to-all communication procedure includes a first stage of intra-cluster parallel data communication between respective processing elements of each of the clusters; a second stage of inter-cluster data exchange for all-to-all data communication between the clusters; and a third stage of intra-cluster data distribution to the respective processing elements of each of the clusters.