Elcomsoft Distributed Password Recovery Updated

Headquartered in Moscow, Russia, ElcomSoft Co. Ltd. updated Elcomsoft Distributed Password Recovery, adding GPU acceleration to recover PGP Disk passwords up to 200 times faster. The new GPU-accelerated product uses standard NVIDIA video cards to accelerate password recovery, and costs less than products using dedicated hardware. The new cost-effective PGP Disk password recovery solution is not only more affordable than competition, but delivers results 10 to 200 times faster than CPU-only algorithms and faster than products based on dedicated hardware such as Xilinx FPGAs.

Computational complexity of breaking PGP Disk encryption makes algorithms using exclusively computers’ CPUs only effective when executed concurrently on numerous workstations. Costs of hardware and software licenses make non-accelerated PGP recovery products slowest and extremely cost-ineffective. Products based on dedicated hardware such as Xilinx FPGAs to break PGP encryption are faster, yet demanding the premium due to their steep prices starting in the $4,000 range.

GPU-Accelerated PGP Disk Password Recovery
ElcomSoft offers a cost-effective hardware-accelerated solution to recover passwords protecting PGP-encrypted disks. Its solution not only costs less than competition but delivers the results faster.

Elcomsoft Distributed Password Recovery is the first commercially available password recovery tool supporting inexpensive video cards to speed up the recovery of passwords protecting encrypted PGP Disks. The patent pending acceleration technology reduces time needed to obtain PGP Disk passwords 10 to 200 times as compared to CPU-only algorithms. Supporting up to four NVIDIA GeForce GTX 295 cards per node and up to 10,000 concurrent workstations, Elcomsoft Distributed Password Recovery crunches numbers about 100 times faster than algorithms running on Intel Q6600 (Core 2 Quad 2.4 GHz) CPUs, while a single NVIDIA GeForce GTX 285 board provides a reasonable 15-fold acceleration over the same CPU.