The kangaroo method for the Pollard's rho algorithm provides a powerful way to solve discrete log problems. There exist parameters for it that allow it to be optimized in such a way as to prevent what are known as "useless collisions" in exchange for the limitation that the number of parallel resources used must be both finite and known ahead of time. This thesis puts forward an analysis of the situation and examines the potential acceleration that can be gained through the use of parallel resources beyond those initially utilized by an algorithm so configured.
In brief, the goal in doing this is to reconcile the rapid rate of increase in parallel processing capabilities present in consumer level hardware with the still largely sequential nature of a large portion of the algorithms used in the software that is run on that hardware. The core concept, then, would be to allow "spare" parallel resources to be utilized in an advanced sort of guess-and-check to potentially produce occasional speedups whenever, for lack of a better way to put it, those guesses are correct.
The methods presented in this thesis are done so with an eye towards expanding and reapplying them to this broadly expressed problem, however herein the discrete log problem has been chosen to be utilized as a suitable example of how such an application can proceed. This is primarily due to the observation that Pollard's parameters for the avoidance of so-called "useless collisions" generated from the kangaroo method of solving said problem are restrictive in the number of kangaroos used at any given time. The more relevant of these restrictions to this point is the fact that they require the total number of kangaroos to be odd. Most consumer-level hardware which provides more than a single computational core provides an even number of such cores, so as a result it is likely the utilization of such hardware for this purpose will leave one or more cores idle.
While these idle compute cores could also potentially be utilized for other tasks given that we are expressly operating in the context of consumer-level hardware, such considerations are largely outside the scope of this thesis. Besides, with the rate of change consumer computational hardware and software environments have historically changed it seems to be more useful to address the topic on a more purely algorithmic level; at the very least, it is more efficient as less effort needs to be expended future-proofing this thesis against future changes to its context than might have otherwise been necessary.
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Klesczewski, Joe, "Combinatorial and Stochastic Approach to Parallelization of the Kangaroo Method of Solving the Discrete Logarithm Problem" (2021). Thesis. Rochester Institute of Technology. Accessed from
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