Efficient Optimization of Erasure Coding for Storage Library

Xie Wenbing; Guan Ruixue; Zhang Yiming; Li Jiamei; Wang Jun

doi:10.7544/issn1000-1239.202440091

Journal of Computer Research and Development > 2025 > 62(5): 1123-1135. > DOI: 10.7544/issn1000-1239.202440091 CSTR: 32373.14.issn1000-1239.202440091

Xie Wenbing, Guan Ruixue, Zhang Yiming, Li Jiamei, Wang Jun. Efficient Optimization of Erasure Coding for Storage Library[J]. Journal of Computer Research and Development, 2025, 62(5): 1123-1135. DOI: 10.7544/issn1000-1239.202440091

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Efficient Optimization of Erasure Coding for Storage Library

Wuxi Institute of Advanced Technology, Wuxi, Jiangsu 214126

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Author Bio:
Xie Wenbing: born in 1989. PhD candidate. His main research interests include binary translation, runtime libraries optimization, program analysis, and compiler optimization

Guan Ruixue: born in 1993. Master. Her main research interests include binary instrumentation and runtime libraries optimization

Zhang Yiming: born in 1993. Master. Her main research interests include binary instrumentation, debug tools, runtime libraries optimization

Li Jiamei: born in 1996. Master. Her main research interests include program analysis and runtime libraries optimization

Wang Jun: born in 1981. Master. His main research interests include program analysis, and autonomous and controllable basic software ecology
Received Date: February 02, 2024
Revised Date: October 10, 2024
Accepted Date: October 14, 2024
Available Online: October 21, 2024

Graphical Abstract

Abstract

Abstract

In the information stage, the importance of data storage lies in ensuring the reliability, consistency, security, and real-time accessibility of information. Erasure codes (EC) play a crucial role in data storage systems due to their ability to minimize storage overhead and handle multiple component failures. However, the process of encoding and decoding EC involves intensive computation, impacting storage system efficiency. This paper focuses on optimizing EC, with a special emphasis on the Galois field (GF) multiplication within multi-layer loops, a time-consuming aspect of EC. We first evaluate the pros and cons of three methods for GF multiplication calculation: the log table searching method, the complete multiplication table searching method, and the shift decomposition method. Subsequently, a 4 b splitting (SP) method is proposed to reduce memory access overhead during table searching in GF(2⁸). We delve into the SP’s analysis and leverage the 64 b modern processor architecture and vector instruction set characteristics to introduce data-level parallelism in multi-layer loops. This involves amplifying data access granularity and implementing single instruction multiple data (SIMD) vectorization. Based on the open-source Intel storage acceleration library (ISA-L), all optimization methods are implemented and tested on Sunway processor and x86 processor. The experimental results show the effectiveness of proposed optimization in improving EC performance across different data scalability scenarios. When compared with the original ISA-L, our optimizations exhibit an average performance speedup of 3.28× on Sunway, 2.36× on x86.
- erasure codes,
- Galois field multiplication,
- 4b splitting method,
- data access granularity,
- SIMD vectorization,
- data lookup table

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References (22)

References

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