Researchers Accelerated Pollard Rho Algorithm on GPUs
New benchmarks on NVIDIA Blackwell hardware show significant speed improvements for cryptographic challenge solving.
Updated on Oct. 14, 2026 in Quantum Computing
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Researchers have successfully accelerated the Pollard rho algorithm using NVIDIA Blackwell GPUs. This development significantly improves performance for solving the Certicom challenge ECC2K-130 compared to previous hardware benchmarks.
Why it matters
Enhancing computational power for cryptographic algorithms helps demonstrate the evolving capabilities of modern hardware architectures. These improvements provide a clearer understanding of current limits in breaking complex elliptic curve security standards.
The RTX PRO 6000 Blackwell card achieved 15.88 billion iterations per second, representing a 17.2 times performance gain over the previous bitsliced control build. This configuration utilized packed normal-basis representation and carry-less multiply instructions.
The players
NVIDIA
NVIDIA is a global technology company known for designing graphics processing units and hardware platforms used in accelerated computing.
AWS
AWS provides cloud computing platforms and services that include high-performance infrastructure for research and testing.
The details
The implementation was optimized through specific batch sizing, launch geometry, and compact tiled state configurations. Experts estimate that five cards could solve the ECC2K-130 challenge within a year, while a cluster of 1,000 cards would reduce that timeframe to approximately three days.
Timeline
The best published GPU kernel performance baseline was recorded in 2010.
This research was published on October 14, 2026.
The Tech Race
This development represents a major shift in how commodity hardware competes with specialized FPGA engines in the realm of high-performance cryptographic solving. The performance headroom on Blackwell architecture positions these GPUs as increasingly potent tools against legacy encryption standards.
While this research focuses on specific cryptographic challenges, the optimization methods can influence how developers approach high-intensity parallel processing tasks. Users may see these gains translated into faster performance for data-heavy applications that leverage similar hardware acceleration.
The takeaway
These findings highlight how architectural optimizations can dramatically extend the useful lifespan of modern hardware in specialized computing tasks. Implementing efficient batch sizing and geometry remains a critical factor for achieving high-performance benchmarks.
Further reading
For more information on the evolution of hardware and cryptographic benchmarks, see the latest updates in Quantum Computing.
More information
The researchers have released the Code and data repository for further technical review.
Source note: This article includes information reported by Cryptology Eprint Archive.
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Do you believe rapid improvements in computing hardware threaten the long-term security of current encryption?
