Case Study - Accelerating Cryptocurrency Mining

Problem Our client was an experimental spin-off of a technology investment company. Their vision was to analyse and engage with the most disruptive ideas in the crypto-currency domain. Our team was tasked with carrying out an independent analysis of a particular cryptocurrency’s mining algorithm and to come up with suggestions on potential avenues for accelerating the process.

Solution Unlike most problems where TechnoLynx engages in GPU acceleration projects, this time, our analysis has uncovered that this problem is already not only well researched but also that the available GPU implementations have rather high performance, in fact, very close to saturating DRAM bandwidth limits.

As the most likely avenues of further improvements, our team suggested looking at heuristic alternatives and also delivered a proof of concept to explore the viability of the idea. Later, in agreement with the client, we turned our attention to performance bottlenecks that are not inherent to the mining algorithm, but rather to certain GPUs, and we carried out an analysis of said hindrance as well as proposed attack vectors of mitigation.

For this project, we mostly relied on C++, CUDA and CMake when dealing with the GPUs, but we used scikit-learn and PyTorch to model heuristic approaches.

Result The client followed developments and recommendations coming from us as well as other consultants exploring this domain and they ultimately concluded that this angle may not be the most profitable investment in the end. Whilst being very happy with the working relationship and the deliverables, they decided to terminate the project on mutually good terms. ​ May this case study stand here as a testament that sometimes it is more valuable to learn about the paths one would not want to pursue rather than persevering and making a costly mistake.