Compute-Balanced Graph Tiling for Irregular Memory Access
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Solution Overview
Problem
Conventional computer architectures are inefficient when processing web-scale graphs due to their inability to handle sparse and irregular data movement, uneven node degrees, and irregular memory accesses, leading to impractical scaling and compute inefficiencies.
Innovation Solution
A hardware and software architecture that processes web-scale graphs by breaking graph connectivity information into compute balanced tiles, using single instruction multiple data compute units, banked embedding buffers, and graph scheduler circuitry to manage and perform computations efficiently, accommodating non-uniform data distribution and reducing idle cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computer architectures are used to process web-scale graphs, then general-purpose computing is maintained, but processing efficiency and scalability deteriorate due to inability to handle sparse and irregular data movement
Solution Approach 1:
The patent segments graph data into compute balanced tiles (CBTs) that organize nodes and edges into manageable units. Each CBT contains a subset of graph connectivity information that can be processed independently, enabling parallel processing while maintaining data locality. This segmentation resolves the contradiction by transforming the monolithic graph processing task into discrete, efficiently manageable segments that exploit hardware parallelism without requiring complete graph data in memory simultaneously.
Solution Approach 2:
The patent introduces specialized graph processing hardware architecture that acts as an intermediary between conventional CPUs and the graph data. This intermediary architecture includes dedicated components such as graph processing units (GPUs), custom memory hierarchies, and data movement engines optimized for graph workloads. These intermediary components handle the sparse and irregular data movement patterns, freeing conventional CPUs from inefficiencies while achieving high processing throughput.
2Speed
If conventional memory access patterns are used, then standard memory hierarchy is maintained, but performance deteriorates due to irregular memory accesses and uneven node degrees
Solution Approach 1:
The patent implements a non-uniform memory access architecture where different memory regions are optimized for different types of graph data access patterns. Hot data (frequently accessed nodes and edges) are placed in faster memory regions closer to processing units, while cold data resides in slower, higher-capacity memory. The memory controller dynamically adapts access patterns based on node degree and access frequency, providing local quality optimization that accelerates data access without requiring complete memory reconfiguration.
Solution Approach 2:
The patent employs pre-fetching mechanisms that anticipate future memory access needs based on graph traversal patterns. Before processing nodes are actually accessed, the system pre-loads relevant data into faster memory regions. This preliminary action reduces wait times during graph processing by ensuring data is available when needed, effectively hiding memory latency without requiring synchronous data availability.
3Productivity
If graph data is processed in monolithic fashion, then data integrity is maintained, but scalability deteriorates due to impractical memory and compute requirements
Solution Approach 1:
The patent divides large-scale graphs into compute balanced tiles that partition the graph data into manageable segments. Each CBT contains a controlled subset of nodes and edges that can be processed with bounded memory requirements. This segmentation enables processing of graphs that exceed available memory capacity by loading only relevant portions into memory during each computation phase, achieving scalability without sacrificing data integrity through coordinated processing across multiple tiles.
Solution Approach 2:
The patent introduces a temporal dimension to graph processing by implementing iterative processing phases. Instead of attempting to process the entire graph simultaneously in space, the system processes graph data across multiple time steps, repeatedly loading and processing different CBTs. This dimensional transformation from spatial to temporal processing enables handling of arbitrarily large graphs with fixed memory resources by cycling through data in organized passes.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to process web-scale graphs are disclosed. An example apparatus comprises: at least one memory; instructions; and processor circuitry to execute the instructions to: retrieve a compute based tile (CBT) from a first external memory, the CBT to include source and destination nodes of a graph; assign a stripe of the CBT to a single instruction multiple data compute unit, the stripe including a first tile and a second tile, the first tile to include first destination nodes and first source nodes, the second tile to include the first destination nodes and second source nodes; retrieve source node embeddings of the stripe based on a node identifier to source node embedding lookup; and provide the source node embeddings to the single instruction multiple data compute unit.