SHARP Protocol Tree Structure for High Performance Computing Latency Reduction
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Solution Overview
Problem
Current high performance computing systems face significant overhead and latency due to software algorithms used for operations like gather, all-gather, gather V, and all-gather V, which involve extensive data transfer and CPU management, leading to bandwidth inefficiencies and increased latency, especially with a large number of processes.
Innovation Solution
The implementation of the SHARP protocol and tree structure, utilizing sparse data representation and optimized data formats, offloads CPU work and reduces latency and bandwidth consumption by enabling efficient data aggregation and distribution across processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If software algorithms are used for data movement commands like gather and all-gather, then the operations can be performed with flexible control, but the CPU overhead and latency increase significantly
Solution Approach 1:
The patent introduces a hardware-based data movement unit that acts as an intermediary between the CPU and memory systems, handling gather and all-gather operations in hardware rather than software. This mediator offloads the time-consuming data movement tasks from the CPU, reducing latency while maintaining operational flexibility through programmable control interfaces.
Solution Approach 2:
The patent replaces the software-based mechanical control system with a hardware-based parallel processing architecture. By implementing data movement commands in hardware logic and dedicated processing units, the system achieves simultaneous execution of multiple data operations, dramatically reducing latency compared to sequential software execution while preserving control flexibility through hardware configuration interfaces.
2Reliability
If extensive data transfer is performed for gather and all-gather operations, then complete data aggregation is achieved, but bandwidth consumption increases
Solution Approach 1:
The patent segments the data aggregation process into multiple parallel stages using a tree-structured architecture. Instead of transferring all data through a single bottleneck path, the system divides the data movement into hierarchical segments where intermediate aggregation points combine data locally before final collection. This segmentation reduces the total bandwidth consumption while ensuring complete data aggregation through the distributed segment structure.
Solution Approach 2:
The patent transforms the traditional linear data aggregation path into a multi-dimensional hierarchical structure. By organizing aggregation nodes in a tree topology with multiple levels and branches, the system creates additional dimensional pathways for data flow. This dimensional transformation distributes the bandwidth load across multiple parallel channels, reducing consumption on any single channel while maintaining complete data aggregation capability.
3Productivity
If a large number of processes participate in collective operations, then computational power increases, but system overhead and latency worsen
Solution Approach 1:
The patent segments the large number of participating processes into hierarchical groups organized in a tree structure. Each node in the hierarchy manages a subset of processes, performing local aggregation before passing results upward. This segmentation allows computational power to scale with the number of processes while reducing system overhead by distributing coordination tasks across multiple hierarchical levels rather than requiring centralized management of all processes.
Solution Approach 2:
The patent adds a hierarchical dimension to the process organization, transforming a flat one-dimensional process list into a multi-dimensional tree structure. This dimensional change enables parallel operation at multiple hierarchical levels simultaneously, allowing computational power to increase with process count while system overhead grows more slowly due to the distributed hierarchical coordination model.
Data Source
AI summary
A method including providing a SHARP tree including a plurality of data receiving processes and at least one aggregation node, designating a data movement command, providing a plurality of data input vectors to each of the plurality of data receiving processes, respectively, the plurality of data receiving processes each passing on the respective received data input vector to the at least one aggregation node, and the at least one aggregation node carrying out the data movement command on the received plurality of data input vectors. Related apparatus and methods are also provided.