GPU API for Asynchronous Data Movement and Tensor Transformations
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Solution Overview
Problem
Current data movement operations in parallel computing platforms are inefficient in terms of time and resource utilization, particularly when performing asynchronous reduction and copy operations across memory spaces within GPUs.
Innovation Solution
The implementation of an Application Programming Interface (API) that utilizes asynchronous data movement hardware and manual transaction accounting to manage data movement operations, including tensor map generation and in-place transformations, allowing for efficient data movement and processing across different memory locations within GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data movement operations are used in parallel computing platforms, then data can be moved between memory spaces, but the operations consume significant time and computing resources
Solution Approach 1:
The system performs preliminary actions by pre-fetching data into caches before it is actually needed for processing. The API enables asynchronous data movement operations that can be initiated in advance, allowing data to be transferred between memory spaces (e.g., from global memory to cache memory) while the computing device continues executing other computational tasks. This preliminary data positioning reduces waiting time and improves overall productivity.
Solution Approach 2:
The patent implements continuous useful action through asynchronous data movement operations that do not block computational workflows. While data is being moved between memory spaces, the computing device continues to perform computations without interruption. The API enables overlapping of data movement and computation operations, ensuring that useful actions (both data transfer and computation) continue simultaneously rather than sequentially, thereby reducing total time consumption.
2Productivity
If traditional data movement operations are used in parallel computing platforms, then data can be moved between memory spaces, but significant computing resources are consumed
Solution Approach 1:
The system maintains continuous computational work while data movement operations are performed in the background through asynchronous mechanisms. This allows the computing resources to remain productively engaged in computations rather than being idle during data transfers, or being over-utilized in sequential data movement operations. The API enables efficient resource utilization by allowing computations and data transfers to proceed concurrently.
Solution Approach 2:
The patent introduces cache memory as an intermediary between source and destination memory spaces. Data can be moved through this intermediate storage layer, which reduces the burden on direct memory-to-memory transfer operations. The API leverages this intermediary structure to perform data movement in a way that consumes fewer computing resources, as the cache acts as a buffer that can be accessed more efficiently than direct transfers between large memory spaces.
3Loss of time
If asynchronous data movement operations are implemented, then time and resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms through automatic synchronization and completion tracking provided by the API. Asynchronous data movement operations are initiated and then managed automatically by the system without requiring continuous manual intervention. The API handles the complexity of tracking operation completion, managing data dependencies, and coordinating between different memory spaces, allowing the user to simply initiate operations without managing the underlying complexity.
Data Source
AI summary
Apparatuses, systems, and techniques to transform information corresponding to one or more memory transactions. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information corresponding to one or more memory transactions resulting from performance of the API to be transformed.