Multicore GPU State Caching to Reduce Redundant Transfers
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Solution Overview
Problem
Existing graphics processing units (GPUs) face inefficiencies in processing image rendering tasks due to redundant transmission of state information across cores, leading to increased competition for communication resources and reduced performance.
Innovation Solution
A multicore GPU architecture with a master unit that distributes image rendering tasks and state information selectively to individual cores, ensuring each core receives only the necessary state information for its assigned tasks, thereby reducing unnecessary data transmission and competition for communication resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If state information is transmitted to all cores in a multicore GPU, then each core can independently process any image rendering task, but redundant data transmission occurs and communication resources are wasted
Solution Approach 1:
The patent implements a selective state distribution mechanism where each core receives only the specific state information required for its assigned tasks. The master unit tracks which cores need which state elements and transmits only those, rather than broadcasting all state information to all cores. This localizes the information distribution to match actual processing needs, eliminating redundant transmissions while maintaining each core's ability to process its designated tasks efficiently.
2Productivity
If a central hub distributes tasks to multiple cores, then parallel processing capability is achieved, but competition for communication resources increases and performance decreases
Solution Approach 1:
The master unit performs preliminary analysis of the task set and identifies which specific state elements are required by which cores before initiating task distribution. By pre-determining the state information needs of each core based on the task characteristics, the system avoids unnecessary communication overhead during task execution. This preliminary planning enables efficient parallel processing by setting up the communication structure in advance, preventing resource contention during actual task processing.
3Reliability
If all state information is transmitted to each core, then task execution independence is ensured, but data transmission volume increases and efficiency decreases
Solution Approach 1:
The system extracts and identifies only the specific state elements that are actually required for executing assigned tasks at each core, separating them from the complete state information set. The master unit maintains tracking of which state elements are needed by which cores and transmits only those extracted necessary elements. This extraction approach ensures each core has sufficient information for independent task execution while dramatically reducing the volume of transmitted data compared to sending complete state information to all cores.
Data Source
AI summary
A set of image rendering tasks and state information are distributed in a graphics processing unit (GPU) having a plurality of cores. A first master unit in one of the cores receives the set of image rendering tasks and the state information, and stores the state information in a memory. The first master unit splits the set of image rendering tasks into a first subset of tasks and a second subset of tasks, wherein the first subset of tasks is assigned to the first core, and the second subset of tasks is assigned to the second core. At least a first portion of the state information is transmitted to the first core, and at least a second portion of the state information is transmitted to the second core. The first subset of tasks is transmitted to the first core, and the second subset of tasks is transmitted to the second core.