GPU Cache Compression for Lower Latency Memory Access

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current graphics processing units (GPUs) face challenges in maximizing parallel processing efficiency and cache access latency, particularly in handling diverse graphics and machine-learning operations, due to limitations in programmable computational units and cache management.

Innovation Solution

The implementation of a graphics processing unit (GPU) with a parallel processor architecture that includes a scheduler for efficient workload distribution, dynamic byte size most frequent value compression, and write operation acceleration techniques, along with a memory controller for optimized cache management, to enhance processing efficiency and reduce latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If dynamic byte size most frequent value compression is implemented, then cache access latency is reduced, but device complexity increases

Engineering Contradiction:

Improvecache access latencyVSAvoidcompression logic complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The compression logic automatically identifies and compresses sequences of identical bytes without requiring external control or configuration. The system self-adjusts compression parameters based on the actual data patterns encountered, performing dynamic byte size most frequent value compression autonomously to reduce cache access latency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The compression logic dynamically changes the byte size parameter based on the frequency of identical values in the data stream. When sequences of identical bytes are detected, the system adapts its compression approach by varying the byte size according to the most frequent value pattern, optimizing compression efficiency for different data types

Inventive Principle:

Principle #35Parameter changes

2Productivity

If parallel processing is increased, then productivity is improved, but difficulty of detecting and measuring increases

Engineering Contradiction:

Improveparallel processing efficiencyVSAvoidworkload distribution monitoring

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The scheduler incorporates feedback mechanisms that continuously monitor workload distribution across parallel processing threads. By tracking performance metrics and workload patterns, the scheduler adjusts task allocation dynamically to maintain optimal parallel processing efficiency while providing visibility into system state for detection and measurement purposes

Inventive Principle:

Principle #23Feedback

3Speed

If write operation acceleration is implemented, then speed is improved, but device complexity increases

Engineering Contradiction:

Improvewrite operation speedVSAvoidmemory controller complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The memory controller performs preliminary actions by pre-fetching and pre-processing data before write operations are completed. This includes anticipating future write needs and preparing memory structures in advance, thereby accelerating write operations without requiring complex real-time processing during the actual write phase

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12093210B2Compression techniques

Publication Date: 2024.09.17 INTEL CORP

AI summary

Methods and apparatus relating to techniques for data compression. In an example, an apparatus comprises a processor receive a data compression instruction for a memory segment; and in response to the data compression instruction, compress a sequence of identical memory values in response to a determination that the sequence of identical memory values has a length which exceeds a threshold. Other embodiments are also disclosed and claimed.