BNU-SYS Lab

BNU-SYS Lab

Research on computer architecture, parallel and high-performance computing, code analysis/optimization, and LLM inference optimization.

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conference-report

葛丹颖参加 Euro-Par 2026 并作论文报告

less than 1 minute read

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

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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.

高建花参加 APPT 2026 并作论文报告

less than 1 minute read

Published:

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

1 minute read

Published:

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.

news

葛丹颖参加 Euro-Par 2026 并作论文报告

less than 1 minute read

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

1 minute read

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.

高建花参加 APPT 2026 并作论文报告

less than 1 minute read

Published:

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

1 minute read

Published:

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.

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