Fused Data Generation for Targeted Multi-Processor Communication
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Solution Overview
Problem
Scenarios involving increased amounts of communication between processors, such as in machine learning, significantly affect overall device operation and computational efficiency, particularly in large-scale deep neural networks that rely on distributed training and techniques like data-parallel training, leading to inefficiencies in data generation and communication.
Innovation Solution
Implementing fused data generation and communication techniques using augmented components like a targeted communication module, data generation and communication tracking module, and updates convergence unit to programmatically manage and synchronize data across processors, leveraging near/in-memory processing for efficient synchronization and concurrent utilization of compute/network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data generation and communication are performed separately with sequential execution, then each operation can be optimized independently, but the overall time steps required increase and computational efficiency decreases
Solution Approach 1:
The patent merges data generation and communication operations into a single fused execution context. The processor executes both operations concurrently within the same time step, eliminating sequential dependencies and reducing overall execution time while maintaining optimization capabilities for both operations independently through programmable control.
Solution Approach 2:
The system performs communication operations in advance or in parallel with data generation by establishing communication contexts before data is fully generated. This allows communication to be prepared ahead of time or executed concurrently, reducing the critical path and overall time steps required.
2Productivity
If multiple separate kernel launches are used for data generation and communication, then each kernel can be optimized for its specific function, but the number of kernel launches increases reducing efficiency
Solution Approach 1:
The patent combines multiple kernel launches into a single fused kernel execution that handles both data generation and communication operations. This reduces the number of kernel launches while maintaining functional optimization through programmable control parameters that can be adjusted for different operation types within the same execution context.
Solution Approach 2:
The fused execution context serves multiple functions simultaneously - it can execute data generation, communication, and coordination operations within a single kernel launch. The processor is designed to handle different operation types through programmable control, making the system more versatile and reducing the need for separate specialized kernels.
3Productivity
If compute and network resources are utilized sequentially, then resource management is simpler, but concurrent utilization is prevented reducing overall performance
Solution Approach 1:
The patent merges compute and network resource utilization into a concurrent execution model where both types of operations can proceed simultaneously. The processor manages both compute and network contexts within the same execution framework, enabling overlapping operations and improving overall performance while handling complexity through unified resource management.
Solution Approach 2:
The system dynamically adjusts resource allocation and execution timing based on operational requirements. The programmable control allows flexible scheduling of compute and network operations, enabling concurrent utilization when beneficial while maintaining simplicity through adaptive management rather than rigid sequential protocols.
Data Source
AI summary
Fused data generation and associated communication techniques are described. In an implementation, a system includes processing system having a plurality of processors. A data generation and communication tracking module is configured to track programmatically defined data generation and associated communication as performed by the plurality of processors. A targeted communication module is configured to trigger targeted communication of data between the plurality of processors based on the tracked programmatically defined data generation and associated communication.