Multicast Memory Model for Lower-Latency Data Coordination
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Solution Overview
Problem
Existing memory models for computing processes involving multiple processing elements, such as GPUs, face increased latency and bandwidth requirements due to the need for multiple memory access requests, especially in operations like all-reduce, which are frequent in deep learning.
Innovation Solution
Implementing a memory model that supports multicasting, allowing a single request to be propagated to multiple physical addresses across processing elements, with options for transparent or explicit multicasting instructions, and enforcing constraints to maintain coherency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple memory access requests are used for coordinated processing operations, then data coordination between processing elements is achieved, but latency and bandwidth requirements increase
Solution Approach 1:
The patent combines multiple memory access requests into a single multicast request that is simultaneously delivered to multiple processing elements. This merging approach maintains data coordination reliability while reducing the time spent on sequential requests, directly addressing the latency issue in coordinated processing operations.
Solution Approach 2:
The memory subsystem is enhanced with multicast capability that serves multiple functions: it can perform traditional unicast operations and new multicast operations, support both reading and writing to multiple addresses, and maintain backward compatibility with existing programming models. This multi-functionality enables efficient coordinated processing without requiring complete system redesign.
2Reliability
If multiple memory access requests are used for coordinated processing operations, then data coordination between processing elements is achieved, but bandwidth requirements increase
Solution Approach 1:
The patent merges multiple individual memory access requests into a single multicast request that simultaneously reaches multiple processing elements. This consolidation reduces the total quantity of data transmitted over the memory subsystem, directly reducing bandwidth requirements while maintaining effective data coordination across all processing elements.
3Loss of time
If multicasting is implemented to reduce memory requests, then latency and bandwidth requirements decrease, but memory model complexity increases
Solution Approach 1:
The memory subsystem dynamically adapts its behavior based on the type of memory access operation. It automatically determines whether to use unicast or multicast based on the request characteristics, and can switch between different memory models (coherent or non-coherent) as needed. This dynamic adaptability manages complexity by only activating multicast functionality when beneficial, while maintaining simple unicast behavior for traditional operations.
Solution Approach 2:
The system changes key parameters of the memory subsystem including the number of active memory channels, the type of memory access (read/write), and the target addresses based on the incoming request. This parameter-based control allows the system to flexibly configure multicast or unicast behavior without requiring complex hardware redesign, thus managing model complexity while achieving performance improvements.
4Ease of operation
If multicasting is exposed to processes with separate instructions, then multicasting control is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal memory access instruction that can function as either a unicast or multicast operation depending on configuration parameters. This single instruction type handles both access patterns, eliminating the need for separate dedicated instructions for each mode. The instruction set remains simple and unified, avoiding the complexity that would arise from adding separate instruction types while still providing full multicasting control capability.
Data Source
AI summary
In various examples, a memory model may support multicasting where a single request for a memory access operation may be propagated to multiple physical addresses associated with multiple processing elements (e.g., corresponding to respective local memory). Thus, the request may cause data to be read from and/or written to memory for each of the processing elements. In some examples, a memory model exposes multicasting to processes. This may include providing for separate multicast and unicast instructions or shared instructions with one or more parameters (e.g., indicating a virtual address) being used to indicate multicasting or unicasting. Additionally or alternatively, whether a request(s) is processed using multicasting or unicasting may be opaque to a process and/or application or may otherwise be determined by the system. One or more constraints may be imposed on processing requests using multicasting to maintain a coherent memory interface.