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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
葛丹颖参加 Euro-Par 2026 并作论文报告
Published:
葛丹颖在第 32 届欧洲并行与分布式计算会议（Euro-Par 2026）上汇报了面向检索增强生成（RAG）的推理加速工作 ApproxRAG，通过协同优化磁盘、主机内存与设备内存的数据流动，实现最高 3.66 倍推理加速。
Danying Ge Presented at Euro-Par 2026
Published:
At the 32nd International European Conference on Parallel and Distributed Computing (Euro-Par 2026), Danying Ge presented ApproxRAG, an inference acceleration work for retrieval-augmented generation (RAG) that jointly optimizes data movement among disk, host memory, and device memory to achieve up to a 3.66× inference speedup.
BNU-SYS「PiedPiper」队在 2026 先导杯（智能计算创新设计赛）中获三等奖
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在 2026 全国大学生计算机系统能力大赛-智能计算创新设计赛（先导杯）全国总决赛中，BNU-SYS「PiedPiper」队荣获三等奖；高建花、石剑君两位老师获优秀指导教师。
BNU-SYS ‘PiedPiper’ Team Wins Third Prize at the 2026 XianDaoBei Contest
Published:
At the national finals of the 2026 XianDaoBei (Intelligent Computing Innovation Design Contest), the BNU-SYS ‘PiedPiper’ team won the Third Prize, while Jianhua Gao and Jianjun Shi received the Outstanding Instructor Award.
高建花参加 APPT 2026 并作论文报告
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课题组高建花老师在比利时布鲁塞尔举行的 APPT 2026 上作题为 ARROW 的学术报告，提出面向 SpMV 的自适应行重组方法，在张量核上实现 warp 级负载均衡，性能优于 cuSPARSE、DASP 与 Spaden 等主流方法。
Jianhua Gao Presented at APPT 2026
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Jianhua Gao from the group presented the ARROW study at APPT 2026 in Brussels, Belgium, proposing an adaptive row reorganization method for SpMV that achieves warp-level load balancing on Tensor Cores and outperforms mainstream methods such as cuSPARSE, DASP, and Spaden.
BNU-SYS 团队在 ASC26 世界大学生超级计算机竞赛中荣获一等奖与超级团队奖
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在 ASC26 世界大学生超级计算机竞赛总决赛中，BNU-SYS 团队荣获一等奖（First Prize），并与多所高校联合摘得超级团队奖（Group Competition Award）。
BNU-SYS Team Wins First Prize and Group Competition Award at ASC26
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At the ASC26 Student Supercomputer Challenge finals, the BNU-SYS team won the First Prize and, together with several other universities, the Group Competition Award.
BNU-SYS「追云者战队」在第十届华为 ICT 大赛中国总决赛·挑战赛中获三等奖
Published:
在第十届华为 ICT 大赛中国总决赛·挑战赛中，BNU-SYS「追云者战队」（指导教师：高建花）荣获全国总决赛挑战赛三等奖。
BNU-SYS ‘Zhuiyunzhe’ Team Wins Third Prize at the 10th Huawei ICT Competition China Finals
Published:
At the 10th Huawei ICT Competition China Finals (Challenge Track), the BNU-SYS ‘Zhuiyunzhe’ team, advised by Jianhua Gao, won the Third Prize.
BNU-SYS 举办【2025年终总结】活动，回顾2025，展望2026
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BNU-SYS 组织开展 2025 年终总结活动，系统回顾年度科研与团队建设成果，明确 2026 年重点方向与协同机制。
BNU-SYS Holds 2025 Year-End Review: Looking Back at 2025, Looking Ahead to 2026
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BNU-SYS held its 2025 year-end review, systematically recapping the year’s research and team-building achievements and setting the key directions and collaboration mechanisms for 2026.
Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
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publications
A Systematic Survey of General Sparse Matrix-Matrix Multiplication
Published in ACM Computing Surveys, 2020
This paper is a systematic survey of SpGEMM.
Recommended citation: Jianhua Gao, Weixing Ji, Fangli Chang, Shiyu Han, Bingxin Wei, Zeming Liu, and Yizhuo Wang. (2023). "A Systematic Survey of General Sparse Matrix-Matrix Multiplication." ACM Computing Surveys. 55(12), 244:1-36. https://doi.org/10.1145/3571157
Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing
Published in IEEE Access, 2020
This paper proposes a fast piecewise Polynomial Fitting method of time-series data.
Recommended citation: Jianhua Gao, Weixing Ji, Lulu Zhang, Senhao Shao, Yizhuo Wang, Feng Shi. (2020). "Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing." IEEE Access. 8:43764-43775. https://ieeexplore.ieee.org/abstract/document/9016024/
Cube-Based Incremental Outlier Detection for Streaming Computing
Published in Information Sciences, 2020
This paper proposes a cube-based outlier detection method CB-ILOF.
Recommended citation: Jianhua Gao, Weixing Ji, Lulu Zhang, Anmin Li, Yizhuo Wang, Zongyu Zhang. (2020). "Cube-Based Incremental Outlier Detection for Streaming Computing." Information Sciences. 517:361-376. https://doi.org/10.1016/j.ins.2019.12.060
AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU
Published in 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), 2021
This paper proposes a new GPU-based SpMV algorithm AMF-CSR.
Recommended citation: Jianhua Gao, Weixing Ji, Senhao Shao, Yizhuo Wang, Feng Shi. (2021). "AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU." 2021 IEEE 27th International Conference on Parallel and Distributed Systems. 418-425. https://ieeexplore.ieee.org/document/9763779
Towards Optimal Fast Matrix Multiplication on CPU-GPU Platforms
Published in International Conference on Parallel and Distributed Computing: Applications and Technologies (PDCAT), 2021
This paper proposes a CPU-GPU heterogenous implementation for the Winograd algorithm.
Recommended citation: Senhao Shao, Yizhuo Wang, Weixing Ji, Jianhua Gao. (2022). "Towards Optimal Fast Matrix Multiplication on CPU-GPU Platforms." International Conference on Parallel and Distributed Computing: Applications and Technologies (PDCAT). 223–236. https://link.springer.com/chapter/10.1007/978-3-030-96772-7_21
MMSparse: 2D Partitioning of Sparse Matrix Based on Mathematical Morphology
Published in Mathematical Morphology, 2022
This paper proposes a 2D partition of sparse matrix based on Mathematical Morphology.
Recommended citation: Zhaonian Tan, Weixing Ji, Jianhua Gao, Yizhuo Wang, Feng Shi. (2020). "MMSparse: 2D Partitioning of Sparse Matrix Based on Mathematical Morphology." Future Generation Computer Systems. 108:521-532. Future Generation Computer Systems
TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU
Published in IEEE Transactions on Parallel and Distributed Systems, 2022
This paper proposes a new compression format for binary sparse matrix.
Recommended citation: Jianhua Gao, Weixing Ji, Zhaonian Tan, Yizhuo Wang, Feng Shi. (2022). "TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU." IEEE Transactions on Parallel and Distributed Systems. 33(12):3732-3745. https://ieeexplore.ieee.org/document/9763312
A Systematic Literature Survey of Sparse Matrix-Vector Multiplication
Published in arXiv, 2024
This survey is intended to provide researchers with a comprehensive understanding of SpMV optimization on modern architectures and provide guidance for future work.
Recommended citation: Jianhua Gao, Bingjie Liu, Weixing Ji, Hua Huang. (2024). "A Systematic Literature Survey of Sparse Matrix-Vector Multiplication." arXiv. 2404.06047. [https://arxiv.org/abs/2404.06047](https://arxiv.org/abs/2404.06047)
talks
基于数据分布特征的稀疏矩阵向量乘优化研究
Published:
本报告所汇报的内容主要覆盖课题组两个已经发表的工作和一个正在进行的工作。两个已发表的工作分别是：“TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU”和“AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU”。第一个工作于2022年4月在国际高水平期刊TPDS上出版，第二个工作被国际高水平会议ICPADS2021收录。第三个工作“Revisiting Thread Configuration of SpMV Kernels on GPU: A Machine Learning Based Approach”是课题组目前正在进行的工作。本报告所汇报的内容致力于提升面向GPU的SpMV的计算效率，针对高数据传输开销、低缓存命中率和不均衡负载、单一线程配置方案的局限性这三个问题提出了相应的解决方案。
teaching
Parallel Progamming: Principle and Practice
Undergraduate course, Beijing Normal University, School of Artificial Intelligence, 2023
This course provides students with a comprehensive understanding of the principles, techniques, and best practices involved in developing parallel programs. Parallel programming is a fundamental aspect of high-performance computing and plays a crucial role in harnessing the power of modern computer architectures.