High-Speed Parallel Implementations of the Rainbow Method in a Heterogeneous System

The computing power of graphics processing units (GPU) has increased rapidly, and there has been extensive research on generalpurpose computing on GPU (GPGPU) for cryptographic algorithms such as RSA, ECC, NTRU, and AES. With the rise of GPGPU, commodity computers have become complex heterogeneous GPU+CPU systems. This new architecture poses new challenges and opportunities in highperformance computing. In this paper, we present high-speed parallel implementations of the rainbow method, which is known as the most efficient time-memory tradeoff, in the heterogeneous GPU+CPU system. We give a complete analysis of the effect of multiple checkpoints on reducing the cost of false alarms, and take advantage of it for load balancing between GPU and CPU. Our implementation with multiple checkpoints requires no more time on average for resolving false alarms and it actually finishes earlier than generating all online chains unlike other implementations on GPU.