Optimized Password Recovery for Encrypted RAR on GPUs

Xiaojing An, Haipeng Jia, Yunquan Zhang

Abstract: Files are often compressed for efficiency. RAR is a common archive file format that supports data compression, error recovery and file spanning. RAR uses classic symmetric encryption algorithm SHA-1 hashing and AES algorithm for encryption. These two algorithms cannot be cracked, so the only method of password recovery is brute force, which is very time-consuming. In this paper, we present an approach using GPUs to speed up the password recovery process. However, because the major calculation and time-consuming part, SHA-1 hashing, is hard to be parallelized, so this paper adopts coarse granularity parallel. That is, one GPU thread is responsible for the validation of one password. We mainly use four optimization methods to optimize this parallel version: asynchronous parallel between CPU and GPU, redundant calculations and conditional statements reduction, data locality by using LDS, and the usage of registers optimization. Experiment result shows that the final version reaches 43~57 times speedup on an AMD FirePro W8000 GPU, compared to a well-optimized serial version on Intel Core i5 CPU. Meanwhile, linear performance acceleration is achieved when using multi-GPUs.