Compression Accelerator Offload for Zstandard Frame Generation
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Solution Overview
Problem
Existing data processing systems face challenges in efficiently offloading cryptographic and compression tasks from processors to accelerator devices, leading to increased computational loads and latency in generating compressed data frames compatible with standards like Zstandard.
Innovation Solution
Accelerator devices perform data compression by replacing data sequences with shorter representations using dictionaries and generate compressed data frames with frame headers and footers, offloading the post-processing to reduce processor load and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processors perform data compression tasks locally, then compression can be performed, but computational load and latency increase
Solution Approach 1:
The system divides compression tasks between the processor and accelerator device. The accelerator handles the computationally intensive compression operations while the processor manages coordination and control, segmenting the workload to reduce processor burden and overall latency.
Solution Approach 2:
An accelerator device is introduced as an intermediary component between the processor and the compression task. This intermediary offloads compression operations from the processor to a dedicated hardware unit, reducing processor latency and improving overall system throughput.
2Power
If accelerator devices are used for compression offloading, then processor load decreases, but system complexity increases
Solution Approach 1:
The accelerator device is designed to handle multiple compression algorithms and formats, making it a universal solution that can adapt to different compression needs. This multi-functionality reduces the need for multiple specialized components, thereby managing system complexity while providing comprehensive compression capabilities.
Solution Approach 2:
The system uses software drivers and interfaces that replicate processor compression logic in the accelerator device. This copying of functional behavior allows the accelerator to understand and execute compression tasks without requiring complex custom hardware designs, simplifying the overall system architecture.
3Productivity
If dictionaries are used for pattern replacement, then compression efficiency improves, but memory requirements increase
Solution Approach 1:
The system uses partial dictionaries that store only the most relevant and frequently occurring patterns rather than attempting to store all possible patterns. This partial action approach maintains high compression efficiency for common cases while significantly reducing memory requirements compared to complete dictionary methods.
Solution Approach 2:
The dictionary structure is made dynamic, allowing it to adapt and evolve based on the data being compressed. The dictionary can be updated, pruned, and reconfigured during compression operations to optimize the balance between compression efficiency and memory usage for each specific workload.
Data Source
AI summary
Examples described herein relate to an accelerator configured to: based on receipt of a request from a requester to offload performance of data compression to an accelerator, compress data and generate a compressed data frame consistent with a data compression format comprising a header and footer.