Data Transform Accelerator Metadata Caching for Latency Reduction
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Solution Overview
Problem
Current data transform operations in systems that utilize data transform accelerators face inefficiencies due to repeated metadata retrieval and lack of effective metadata sharing across multiple data transform commands, leading to increased latency and resource utilization.
Innovation Solution
Implementing a method where metadata is shared across multiple data transform commands, reducing duplicate metadata storage and retrieval by caching it in the data transform accelerator's memory, and using existing metadata to configure data transform pipelines for subsequent operations, thereby optimizing resource usage and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If metadata is retrieved for each data transform command separately, then data transform operations can be executed independently, but latency increases and resource utilization decreases
Solution Approach 1:
The system performs preliminary actions by retrieving and caching metadata before data transform commands are executed. The metadata retrieval unit fetches metadata in advance and stores it in a cache structure, so when commands arrive, the metadata is already available for immediate use, eliminating retrieval latency during command execution.
Solution Approach 2:
A cache structure acts as an intermediary between the metadata storage and the data transform commands. The cache receives metadata from the storage location and provides it to commands that need it, mediating the interaction and eliminating the need for direct repeated retrieval operations.
2Reliability
If duplicate metadata is stored for each data transform command, then each command has complete information, but memory resource utilization increases
Solution Approach 1:
Multiple data transform commands that share common metadata are merged into a single metadata entry in the cache. Instead of storing separate metadata for each command, the system combines them into one shared metadata structure, reducing storage volume while maintaining completeness through reference sharing.
Solution Approach 2:
The cache structure serves multiple functions: it stores metadata, manages memory resources, and provides shared access to multiple commands. This universal structure eliminates the need for separate metadata storage for each command, achieving both resource optimization and operational completeness.
3Ease of operation
If metadata is retrieved and processed for each command sequentially, then processing is simple, but overall processing time increases
Solution Approach 1:
The system performs preliminary metadata retrieval and processing before commands are executed. By fetching and preparing metadata in advance, the system simplifies the command execution process while improving throughput, as commands can proceed directly to execution without sequential metadata processing delays.
Solution Approach 2:
The metadata retrieval and caching operation continues in the background while commands are processed, maintaining continuous useful action. This allows metadata to be prepared and ready for multiple commands without blocking their execution, improving overall processing throughput while keeping the operation flow simple and continuous.
Data Source
AI summary
A method includes determining a communication interface between a host computing unit and a data transform accelerator, hosting one or more virtual machines on a host operating system of the host computing unit; partitioning a collection of container data structures into multiple sets; partitioning a memory of the data transform accelerator into multiple partitions; submitting one or more data transform commands for processing by the data transform accelerator device; in response to a determination that an address is in a particular container data structure, accessing the data transform command based on the address; obtaining metadata based on information in the data transform command; and configuring a data transform pipeline based on the metadata.