GPU Asynchronous Matrix Multiply-Accumulate for Bandwidth-Limited Throughput
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Solution Overview
Problem
Matrix multiply and accumulate (MMA) operations in GPU hardware are bandwidth intensive, which is a key challenge in machine learning workloads, affecting throughput.
Innovation Solution
Implementing asynchronous matrix multiply accumulate (MMA) operations to optimize GPU hardware design for improved throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional synchronous MMA operations are used in GPU hardware, then operational correctness is maintained, but throughput is limited due to bandwidth intensity and synchronization overhead
Solution Approach 1:
The patent applies preliminary action by pre-loading matrix operands into on-chip memory before the MMA operation begins. The system prepares data in advance by transferring matrices from off-chip memory to on-chip memory buffers, so that when the computation starts, all necessary data is already available in fast memory, eliminating bandwidth bottlenecks during the actual computation phase.
Solution Approach 2:
The patent segments the large matrix multiplication task into smaller sub-tasks that can be processed asynchronously. By dividing the computation into manageable chunks and processing them in parallel streams, the system reduces the memory bandwidth requirement for each individual operation while maintaining overall throughput through pipelined execution.
2Productivity
If asynchronous MMA operations are implemented to improve throughput, then operational efficiency increases, but hardware complexity increases
Solution Approach 1:
The patent introduces an intermediary command buffer mechanism that mediates between the host processor and the asynchronous MMA execution engine. The command buffer acts as an intermediary data structure that queues operations and manages resource allocation, allowing complex asynchronous operations to be orchestrated without requiring complex control logic in the hardware itself.
Solution Approach 2:
The asynchronous MMA implementation employs self-service mechanisms where the hardware automatically manages its own operation queue and resource allocation. The system includes built-in logic for automatic scheduling, resource management, and coordination between multiple compute units, reducing the need for external control and simplifying the overall system architecture.
3Productivity
If multiple async MMA operations are executed concurrently to maximize throughput, then computational efficiency improves, but synchronization difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms through event-driven synchronization where completion events from async MMA operations automatically trigger dependent operations. The system uses event buffers and dependency tracking to provide feedback about operation completion status, enabling automatic coordination of concurrent operations without requiring manual synchronization management.
Solution Approach 2:
The patent maintains continuity of useful action by implementing pipelined execution where multiple async MMA operations overlap in time. While one operation is computing, another is loading data, and a third is preparing operands, ensuring that the hardware resources are continuously utilized without idle periods, thereby maximizing throughput while simplifying synchronization through temporal separation.
Data Source
AI summary
One embodiment provides a graphics processor comprising a base die including a plurality of chiplet sockets and a plurality of chiplets coupled with the plurality of chiplet sockets. At least one of the plurality of chiplets including a plurality of processing elements, a distributed shared local memory coupled with the plurality of processing elements, a plurality of matrix engines coupled with the distributed shared local memory, and an asynchronous matrix multiply accumulate (MMA) controller configured to perform an asynchronous MMA operation via the plurality of matrix engines.