HCLAM Merge Network Scalability and PRaP Throughput
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Solution Overview
Problem
Existing multi-way merge sort hardware implementations face challenges in scalability and achieving high throughput, particularly when dealing with large problem sets, as they require exponentially more resources and struggle to maintain parallelization and full throughput in sorted output reduction.
Innovation Solution
The implementation of a custom hardware design that combines advanced comparison techniques, hybrid memory-based binary trees, radix pre-sort parallelization, and full throughput reducers, such as Hybrid Comparison Look Ahead Merge (HCLAM) and Parallelization by Radix Pre-sorter (PRaP), to enhance scalability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional binary tree hardware implementation is used for multi-way merge sort, then basic sorting functionality is achieved, but resource requirements grow exponentially with problem size and scalability is lost
Solution Approach 1:
The patent divides the multi-way merge sort problem into multiple independent binary merge operations organized in a tree structure. Each node in the tree performs a simple binary merge of two sorted streams, and these nodes are distributed across multiple hardware pipelines. This segmentation allows the system to handle large N-way merges by composing many small binary merges, avoiding exponential resource growth while maintaining scalability.
Solution Approach 2:
The patent introduces parallelization across multiple hardware pipelines operating simultaneously, adding a temporal and spatial dimension to the merge operations. Multiple binary merge trees operate in parallel, each handling a subset of the data, with results combined through inter-pipeline communication. This dimensional expansion allows the system to scale to larger problem sizes without proportionally increasing resources in any single pipeline.
2Quantity of substance
If more hardware resources are allocated to handle larger problem sets, then sorting capacity increases, but parallelization efficiency decreases and throughput is not maintained
Solution Approach 1:
The patent performs preliminary sorting of input data into N sorted streams before the multi-way merge phase. This pre-sorting action distributes data evenly across the N input streams, ensuring balanced workloads for subsequent binary merge operations. By preparing data in advance, the system avoids bottlenecks during the merge phase and maintains high throughput even as problem size increases.
Solution Approach 2:
The patent implements continuous streaming data flow through the merge tree pipelines, with data constantly moving from input streams through multiple merge stages to output. Buffering mechanisms maintain continuous operation even when data arrival rates vary, ensuring that hardware resources remain consistently utilized and throughput is maximized throughout the sorting process.
3Power
If reduction operations are performed on sorted output values, then aggregate results are produced, but full throughput is not achieved due to reduction bottlenecks
Solution Approach 1:
The patent combines multiple sorted output streams from parallel merge trees into a single consolidated sorted stream before reduction operations. This merging of streams allows reduction operations to process data in a more efficient sequential manner, avoiding the need for complex parallel reduction logic. The consolidated stream maintains full throughput by leveraging the already-sorted nature of the combined data, enabling efficient aggregate computations.
Data Source
AI summary
Disclosed herein is a novel multi-way merge network, referred to herein as a Hybrid Comparison Look Ahead Merge (HCLAM), which incurs significantly less resource consumption as scaled to handle larger problems. In addition, a parallelization scheme is disclosed, referred to herein as Parallelization by Radix Pre-sorter (PRaP), which enables an increase in streaming throughput of the merge network. Furthermore, high performance reduction scheme is disclosed to achieve full throughput.