Miners use hashing to solve mathematical puzzles and validate transactions
In proof-of-work systems, mining relies on hashing: miners use computational power to solve mathematical puzzles, validate transactions, and maintain blockchain integrity.
CASE STUDY
Accelerating Cryptocurrency Mining (Under NDA)
TechnoLynx worked with a client exploring disruptive ideas in cryptocurrency to assess whether a specific proof-of-work mining algorithm could be accelerated to improve overall hash rate performance and mining efficiency.
How Cryptocurrency Mining Works
As mining difficulty increased, miners moved from CPUs to GPUs
As mining difficulty increased, CPUs became insufficient and miners moved to GPUs for their efficiency on these workloads.
Asics role in crypto mining
ASICs are even more specialised and can be faster and more efficient than GPUs, but they are more expensive and less versatile. There is ongoing debate about proof-of-work versus alternatives like proof-of-stake, which aim to reduce energy requirements.
The Challenge
The client wanted to evaluate whether meaningful improvements could be made to a cryptocurrency mining algorithm. Their goal was to identify potential avenues for faster, more efficient mining, ideally improving performance and profitability, within a proof-of-work system.
Hash-rate performance focus
Identify where the mining algorithm could be accelerated, with an emphasis on improving overall hash rate.
Hardware constraints
Mining is energy-intensive and depends on powerful compute hardware such as GPUs (and sometimes ASICs), which can impose hard performance limits.
Find real bottlenecks
Determine whether performance headroom exists in the algorithm itself, or whether the bottlenecks sit elsewhere (e.g., GPU limitations).
Cryptocurrency statistics (Unsplash).
Project Timeline
From mining-algorithm analysis to heuristic exploration and GPU bottleneck investigation
Algorithm & Infrastructure Analysis
Analysed the existing mining algorithm, focusing on operational efficiency and performance bottlenecks, including potential acceleration via GPUs or ASICs.
Found that GPU implementations were already highly optimised, with performance nearing hardware limits, particularly around DRAM bandwidth, leaving little room for further gains in the core approach.
Baseline Reality Check
Heuristic Proof of Concept
Explored heuristic methods, using “educated guesses” to bypass certain computations. Implemented a proof of concept; results were promising but not significant enough to justify deeper investment.
Shifted focus to performance bottlenecks tied to specific GPUs. Identified areas where certain GPUs underperformed due to hardware limitations and design inefficiencies, and suggested mitigation approaches.
GPU Bottleneck Investigation
Decision & Next Steps
Concluded that additional improvements would likely be incremental. The client decided not to pursue further development and redirected resources to more promising work, supported by a clear, independent analysis.
The Solution
TechnoLynx conducted a thorough analysis of the existing infrastructure and the mining algorithm, identified current performance bottlenecks, and assessed whether acceleration through better use of GPUs or potential ASIC implementation was possible. After finding existing GPU implementations were already highly optimised and near hardware limits (particularly DRAM bandwidth), we explored heuristic methods via a proof of concept, then shifted focus to bottlenecks associated with specific GPUs and suggested mitigation methods.
Mining Algorithm Analysis
Evaluated efficiency and bottlenecks in the mining algorithm and assessed whether acceleration via improved GPU usage or ASIC implementation could plausibly deliver meaningful gains.
Heuristics Exploration
Investigated heuristic methods that bypass parts of the computation. Built a proof of concept; while encouraging, gains were not large enough to justify continued investment.
GPU Bottlenecks & Mitigations
Identified bottlenecks affecting specific GPUs and proposed mitigation options, including approaches tied to hash-rate calculations and transaction fee management.
Technical Specifications
GPU Engineering
C++, CUDA, CMake
Modeling / Exploration
scikit-learn, PyTorch (for heuristic alternatives)
Existing GPU implementations were already highly optimised, nearing hardware limits, especially DRAM bandwidth
GPUs can be used for a wide range of applications; ASICs are purpose-built for mining and can be faster and more efficient than GPUs, but they are more expensive and less versatile
Heuristics and optimisations offered limited upside; further improvements were likely incremental rather than groundbreaking
The Outcome
The client decided not to pursue further development, based on the conclusion that the mining algorithm was already performing at a high level and the remaining optimisation headroom would not justify additional investment.
The project still delivered value by providing a thorough, independent analysis and clear recommendations, supporting an informed decision and a shift toward more promising ventures.
Key Achievements
Performed a deep analysis of the mining algorithm and its performance bottlenecks
Confirmed existing GPU implementations were already highly optimised, nearing hardware limits (notably DRAM bandwidth)
Built and evaluated a heuristic proof of concept to test alternative acceleration strategies
Identified GPU-specific bottlenecks and proposed mitigation methods where practical
Helped the client make an informed ROI decision, avoiding investment in low-upside optimisation work
Provided a clear understanding of limitations and potential improvement directions for future exploration
Need a Performance Reality Check?
We can analyse your GPU workloads, identify true bottlenecks, and validate whether optimisation work will deliver meaningful ROI.
