Energy Efficient Multiprocessing Solo Mining Algorithms for Public Blockchain Systems
Bibliographic record
Abstract
Blockchain as a decentralized distributed ledger is revolutionizing the world with a secure design data storage mechanism. In the case of Bitcoin, mining involves a process of packing transactions in a block by calculating a random number termed as a nonce. The nonce calculation is done by special nodes called miners, and all the miners follow the Proof of Work (PoW) mining mechanism to perform the mining task. The transaction verification time in PoW-based blockchain systems, i.e., Bitcoin, is much slower than other digital transaction systems such as PayPal. It needs to be quicker if a system adapts PoW-based blockchain solutions, where there are thousands of transactions being computed at a time. Besides this, PoW mining also consumes a lot of energy to calculate the nonce of a block. Mining pools resulting into aggregated hashpower have been a popular solution to speed up the PoW mining, but they can be attacked by using different types of attacks. Parallel computing can be used to speed up the solo mining methods by utilizing the multiple processes of the contributing processors. In this research, we analyze various consensus mechanisms and see that the PoW-based blockchain systems have the limitations of low transaction confirmation time and high energy consumption. We also analyze various types of consensus layer attacks and their effects on miners and mining pools. To tackle these issues, we propose parallel PoW nonce calculation methods to accelerate the transaction verification process especially in solo mining. We have tested our techniques on different difficulty levels, and our proposed techniques yield better results than the traditional nonce computation mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".