Distributed Machine Learning Data Parallelism and Halo Exchange
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Solution Overview
Problem
Current data processing systems, particularly in graphics processing, face challenges in efficiently handling diverse operations and maximizing parallel processing, especially with the increasing complexity of graphics and machine-learning workloads.
Innovation Solution
The integration of a general-purpose graphics processing unit (GPU) communicatively coupled to host/processor cores, which accelerates graphics operations, machine-learning operations, and general-purpose GPU functions by utilizing dedicated circuitry and optimized interconnects like PCIe or NVLink.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is distributed across multiple GPUs for parallel processing, then processing throughput is improved, but communication overhead and latency between GPUs increases
Solution Approach 1:
The patent divides the computational workload and data into segments that can be processed independently by different GPUs. Each GPU handles a specific portion of the data parallelism operations, allowing simultaneous processing while minimizing inter-GPU communication requirements through localized computations.
Solution Approach 2:
The patent introduces an intermediary communication mechanism that optimizes data transfer between GPUs. This intermediary layer manages the exchange of data and control signals, reducing communication overhead by batching transfers and optimizing the timing of inter-GPU interactions.
2Productivity
If more processing elements are added to increase parallelism, then processing capacity is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal control mechanism that manages multiple processing elements through a standardized interface. This allows the system to scale to different numbers of GPUs while maintaining the same control logic, reducing system complexity despite increased processing capacity.
Solution Approach 2:
The patent combines multiple processing elements into unified functional blocks that share common control and memory resources. This merging approach reduces the overall system complexity by eliminating redundant components while maintaining parallel processing capabilities.
3Speed
If data is partitioned across multiple devices for parallel processing, then processing speed is improved, but data exchange overhead increases
Solution Approach 1:
The patent performs preliminary data preparation and organization before distribution to multiple GPUs. Data is pre-processed and formatted in a way that minimizes the need for extensive data exchange during computation, reducing communication overhead while maintaining processing speed.
Solution Approach 2:
The patent optimizes data locality by keeping frequently accessed data close to the processing elements that need it. Each GPU maintains local data structures and caches that reduce the frequency and volume of data exchanges over the interconnect, lowering communication overhead.
Data Source
AI summary
One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising multi-dimensionally partitioning data of a feature map across multiple nodes for distributed training of a convolutional neural network; performing a parallel convolution operation on the multiple partitions to train weight data of the neural network; and exchanging data between nodes to enable computation of halo regions, the halo regions having dependencies on data processed by a different node.