Details

Autor(en) / Beteiligte

• Li, Haofeng
• Meng, Haining
• Zheng, Hengjie
• Cao, Liqing
• Lu, Jie
• Li, Lian
• Gao, Lin

Titel

Scaling up the IFDS algorithm with efficient disk-assisted computing

Ist Teil von

• 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), 2021, p.236-247

Ort / Verlag

Piscataway, NJ, USA: IEEE Press

Erscheinungsjahr

2021

Quelle

Access via ACM Digital Library

Beschreibungen/Notizen

• The IFDS algorithm can be memory-intensive, requiring a memory budget of more than 100 GB of RAM for some applications. The large memory requirements significantly restrict the deployment of IFDS-based tools in practise. To improve this, we propose a disk-assisted solution that drastically reduces the memory requirements of traditional IFDS solvers. Our solution saves memory by 1) recomputing instead of memorizing intermediate analysis data, and 2) swapping inmemory data to disk when memory usages reach a threshold. We implement sophisticated scheduling schemes to swap data between memory and disks efficiently. We have developed a new taint analysis tool, DiskDroid, based on our disk-assisted IFDS solver. Compared to FlowDroid, a state-of-the-art IFDS-based taint analysis tool, for a set of 19 apps which take from 10 to 128 GB of RAM by FlowDroid, DiskDroid can analyze them with less than 10GB of RAM at a slight performance improvement of 8.6%. In addition, for 21 apps requiring more than 128GB of RAM by FlowDroid, DiskDroid can analyze each app in 3 hours, under the same memory budget of 10GB. This makes the tool deployable to normal desktop environments. We make the tool publicly available at https://github.com/HaofLi/DiskDroid.