GPU User Profile Processing for Large-Scale Campaign Matching
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Solution Overview
Problem
Cloud-based applications face computational burdens when processing large-scale user profiles due to the complexity and number of user interests, making CPU-based processing inefficient.
Innovation Solution
Utilizing GPUs for parallel processing by offloading user profile evaluation tasks, organizing user and campaign logic tree data structures in specialized formats to facilitate efficient GPU consumption, and employing lock-step thread execution for coalesced memory access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU-based processing is used for user profiles, then processing can be performed with simple hardware, but computational efficiency deteriorates when processing large-scale user profiles with many user interests
Solution Approach 1:
The patent segments user profile data into separate components: user identifiers, interest tags, and campaign logic trees. This segmentation allows the GPU to process different aspects of user profiles in parallel, with each thread handling specific evaluation tasks for individual users or interest combinations, thereby improving computational efficiency without requiring a complete hardware overhaul
Solution Approach 2:
The patent introduces specialized data structures as intermediaries between the raw user profile data and the GPU processing. These data structures (including interest tag arrays, campaign logic tree representations, and matching result buffers) serve as optimized interfaces that enable efficient parallel processing while maintaining data integrity and facilitating the transition from CPU to GPU architecture
2Adaptability or versatility
If the number of user interests in each user profile increases, then user profiling capability improves, but computational burden on CPUs increases
Solution Approach 1:
The patent transforms the computational problem from a single-dimension sequential CPU processing model to a multi-dimension parallel GPU processing model. By organizing user interests as arrays that can be accessed simultaneously by multiple GPU threads and by structuring campaign logic trees to enable parallel evaluation, the system achieves dimensional expansion in processing capability that handles increased user interest complexity efficiently
Solution Approach 2:
The patent performs preliminary organization of user profile data into optimized data structures before GPU processing. User interests are pre-sorted into arrays, campaign logic trees are pre-compiled into evaluation-ready formats, and data is pre-transferred to GPU memory. This preliminary action reduces the computational burden during actual processing by eliminating data preparation overhead during parallel execution
3Quantity of substance
If cloud-based applications process millions of user profiles, then service coverage improves, but processing time increases
Solution Approach 1:
The patent merges multiple processing operations into a unified GPU-based parallel processing framework. Instead of sequentially executing interest tag retrieval, campaign logic evaluation, and matching determination separately on CPUs, the system combines these operations into integrated GPU kernels that execute simultaneously, thereby reducing total processing time for millions of user profiles while maintaining comprehensive service coverage
Data Source
AI summary
Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.