QZRAM: A Transparent Kernel Memory Compression System Design for Memory-Intensive Applications with QAT Accelerator Integration

QZRAM: A Transparent Kernel Memory Compression System Design for Memory-Intensive Applications with QAT Accelerator Integration

In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes to exit without warning, resulting in negative...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 18; p. 10526
Main Authors: Gao, Chi, Xu, Xiaofei, Yang, Zhizou, Lin, Liwei, Li, Jian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-09-2023
Subjects:
Algorithms
Computational linguistics
Computers
Data compression
Evaluation
Integrated circuits
Language processing
Linux
Machine learning
memory compression
memory-intensive application
Natural language interfaces
NLP
OOM
Operating systems
QAT
Semiconductor chips
Semiconductor industry
Software
Technology application
United States
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Summary:In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes to exit without warning, resulting in negative effects on a system’s performance and stability. To mitigate OOM errors, many operating systems implement memory compression (e.g., Linux’s ZRAM) to provide flexible and larger memory space. However, these schemes incur two problems: (1) high-compression algorithms consume significant CPU resources, which inevitably degrades application performance; and (2) compromised compression algorithms with low latency and low compression ratios result in insignificant increases in memory space. In this paper, we propose QZRAM, which achieves a high-compression-ratio algorithm without high computing consumption through the integration of QAT (an ASIC accelerator) into ZRAM. To enhance hardware and software collaboration, a page-based parallel write module is introduced to serve as a more efficient request processing flow. More importantly, a QAT offloading module is introduced to asynchronously offload compression to the QAT accelerator, reducing CPU computing resource consumption and addressing two challenges: long CPU idle time and low usage of the QAT unit. The comprehensive evaluation validates that QZRAM can reduce CPU resources by up to 49.2% for the FIO micro-benchmark, increase memory space (1.66×) compared to ZRAM, and alleviate the memory overflow phenomenon of the Redis benchmark.
ISSN:2076-3417
2076-3417
DOI:10.3390/app131810526