Shared Memory Architecture for Parallel AI Processing
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Solution Overview
Problem
Current machine learning systems are inflexible and inefficient in handling large data sets and real-time parallel processing tasks, particularly in maintaining state across multiple parallel processes.
Innovation Solution
A system and method that utilize a shared memory architecture allowing multiple devices to access and store data streams, including images, video, and sound, while applying AI-enhanced analysis techniques, with predictive load balancing to optimize processing across a cluster of computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple parallel processing tasks access separate memory spaces, then process independence is maintained, but data access efficiency decreases and memory utilization is poor
Solution Approach 1:
The patent merges multiple separate memory spaces into a single shared memory space that is accessible by all parallel processing tasks. This unified memory architecture allows multiple GPU processes to simultaneously access the same data without requiring separate memory allocations, thereby improving memory utilization and data access efficiency while maintaining process independence through software-based task management.
Solution Approach 2:
The shared memory architecture serves multiple functions simultaneously: it acts as common data storage for all parallel tasks, enables efficient data sharing between processes, provides a unified address space for memory management, and supports both read and write operations by multiple tasks. This multi-functional design eliminates the need for separate memory spaces while maintaining process independence.
2Speed
If data is stored in separate memory spaces for each process, then data security is improved, but data access speed and processing throughput decrease
Solution Approach 1:
The patent combines multiple process-specific memory spaces into a single shared memory space that is accessible by all parallel processing tasks. This unified architecture eliminates the need for data copying between separate memory spaces, thereby significantly improving data access speed and processing throughput while maintaining data security through software-based access control and process isolation mechanisms.
3Productivity
If large data sets are processed in real-time, then processing capability is improved, but memory bandwidth requirements and latency increase
Solution Approach 1:
The patent merges multiple memory spaces into a single shared memory space that is directly accessible by all parallel processing tasks. This eliminates the need for data copying and reduces memory access latency by allowing tasks to read and write data directly from the shared memory without intermediate storage or data transfer operations, thereby enabling efficient real-time processing of large data sets.
4Measurement precision
If hand-tuned special purpose systems are used, then specific problem solving accuracy is improved, but system flexibility and adaptability decrease
Solution Approach 1:
The patent implements a universal shared memory architecture that can support multiple different parallel processing tasks and AI algorithms. This flexible memory system can be dynamically allocated to different processing needs without requiring hand-tuned special purpose systems for each specific problem, thereby maintaining high problem-solving accuracy while significantly improving system flexibility and adaptability to emerging application areas.
Data Source
AI summary
The present disclosure relates to systems and methods to implement efficient high-bandwidth shared memory systems particularly suited for parallelizing and operating large scale machine learning and AI computing systems necessary to efficiently process high volume data sets and streams.