A Novel Optimization for GPU Mining Using Overclocking and Undervolting
Abstract
:1. Introduction
1.1. Our Contributions
1.2. Organization of Paper
2. Background
2.1. Cryptocurrency Mining
2.2. Different Methods of Mining Cryptocurrencies
2.3. Mining Pools
2.4. Hardware Required for Mining
3. Proposed Methodology
- Overclocking is a technique of enhancing the GPU’s omission retention-and center timer rates to speeds higher than those specified by the manufacturer. You can increase the power limit when overclocking, but bear in mind that this will increase power utilization. The overclocking technique is best for lower-end GPUs, such as the Nvidia GTX series of GPUs, as they have less memory and lower clock speeds compared to the newer generation of cards, such as the RTX series cards. Effectiveness is crucial in mining because it affects global profitability.
- Undervolting the GPU is another important technique which helps the GPU to consume less power than the default power consumption of the GPU. When a GPU is undervolted, its energy consumption falls by around 30%, and its blower rate will be reduced by 33%. As a result, the GPU may run at a lower temperature and consume less power.
- In regard to mining optimization, Monero will use a new algorithm known as RandomX. We will go through how to overclock your GPU, which will help you optimize your earnings when mining with the RandomX algorithm. By performing a GPU overclocking, you could damage the GPU, so to overcome this issue we have also implemented undervolting techniques, which help the GPU to run with less power. RandomX mining is dependent on GPU clock speeds and VRAM. Our tests with different GPU clock speeds on RTX 3060 Non-LHR GPU yielded different results. Tests were performed on a system with 8 RTX 3060 GPUs. The results might change depending on which GPUs use clock speeds and VRAM better in their mining rig.
- Hardware Optimization is the core part our paper. When it comes to undervolting the GPUs, we have utilized the MSI AFTERBURNER software to undervolt and overclock the GPUs. When it comes to undervolting we have reduced the power consumption by 20% from its default power consumption, which is 225 watts. We have managed to reduce it to 180 watts for each GPU and we have used 8 RTX 3060 GPUs which are Nvidia’s custom-made GPU design called “Founders Edition”. With overclocking, we have increased the clock speeds of the GPUs with the afterburner itself. Its base clock speed was 1320 MHz and we have boosted the clock speeds to 1777 MHz, which uses the GPU to its maximum. Even though overclocking will increase the temperatures significantly, the undervolting technique will ensure the temperatures will not increase, as the power consumption will be reduced. With the help of these GPU Mining techniques, we have managed to increase the mining efficiency from 100% to 147%, which really adds to to the overall mining. All the experimental results are listed down below in Section 4 Experimentation Results.
- XRP Stak
- Miner Gate
- Monero Spelunker
- CC Miner
4. Experimentation Results
5. Discussion
6. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | CPU | GPU |
---|---|---|
Speed | 2256 bit | 3200 bit |
Energy Efficiency | Not energy efficient | Energy efficient |
Maintenance Level | Difficult to maintain | Easy to maintain |
Difficult | Harder to mine | Effortless mining |
Memory | No memory | Has memory depending upon the GPU |
Installation | Not easy to install | Easy to install |
Support | No regular updates | Has regular software updates |
Optimization | Not easy to optimize | One-click optimization |
GPU Power Consumption (Watts) | MHz | Hash Rate (MH/S) | Fan Speed (RPM) |
---|---|---|---|
200 W | 1750 | 60 MH/S | 1250 RPM |
215 W | 1800 | 62.39 MH/S | 1350 RPM |
231 W | 1850 | 65.41 MH/S | 1400 RPM |
262 W | 1900 | 67.61 MH/S | 1460 RPM |
291 W | 1950 | 69.11 MH/S | 1570 RPM |
315 W | 2000 | 71.28 MH/S | 1650 RPM |
320 W | 2050 | 73.91 MH/S | 1670 RPM |
GPU Power Consumption | Profit Per Day | Electricity Savings |
---|---|---|
200 W | 321.85 Rs | 40.837 Rs |
215 W | 328.48 Rs | 40.837 Rs |
231 W | 336.12 Rs | 40.837 Rs |
262 W | 349.99 Rs | 40.837 Rs |
291 W | 362.35 Rs | 40.837 Rs |
315 W | 374.94 Rs | 40.837 Rs |
320 W | 380.21 Rs | 40.837 Rs |
GPU | Power Consumption | Clock Speeds | Hash Rates | CUDA Cores | VRam |
---|---|---|---|---|---|
RTX 3090 OC | 350 W | 1371 MHz | 120 MH/s | 10,496 | 24 GB GDDR6X |
RTX 3080 OC | 320 W | 1350 MHZ | 100 MH/S | 8704 | 10 GB GDDR6X |
RTX 3060 Ti | 200 W | 1400 MHz | 60 MH/S | 4864 | 8 GB GDDR6X |
AMD RX 5700 XT | 225 W | 1445 MHz | 1090.3 H/S | 2304 SP | 8 GB GDDR6 |
RTX 2070 | 225 W | 1596 MHz | 710.0 H/S | 2304 | 8 GB GDDR6 |
AMD RX 580 | 185 W | 1241 MHz | 470.0 H/S | 2048 SP | 8 GB GDDR5 |
GTX 1660 Super | 125 W | 1513 MHz | 505.0 H/S | 1408 | 6 GB GDDR6 |
Constraints | Our Work | Dev [3] | Alkaeed [7] | Fadeyi [24] | Han [32] |
---|---|---|---|---|---|
Cryptocurrency | Monero | Bitcoin | Ethereum | Ravencoin | Dogecoin |
Algorithm | RandomX | SHA-256 | Keccak-256 | KAWPOW | Scrypt |
AverageHash rate | 427.97 MH/s | 193 TH/s | 12,638 MH/s | 3.51 TH/s | 461 MH/s |
Mining Hardware | RTX GPU | ASIC | RTX GPUs | Nvidia GPUs | RTX GPU |
Power Consumption | 190 kWh | 214.93 TWh | 113 TWh | 194 kWh | 101 kWh |
Energy source | Renewable Energy | Renewable Energy | Renewable Energy | Renewable Energy | Renewable Energy |
Overclocking | ✓ | ✖ | ✖ | ✖ | ✖ |
Undervolting | ✓ | ✖ | ✖ | ✖ | ✖ |
Mining Efficiency | ✓ | ✖ | ✖ | ✖ | ✖ |
Hardware optimisation | ✓ | ✖ | ✖ | ✖ | ✖ |
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Shuaib, M.; Badotra, S.; Khalid, M.I.; Algarni, A.D.; Ullah, S.S.; Bourouis, S.; Iqbal, J.; Bharany, S.; Gundaboina, L. A Novel Optimization for GPU Mining Using Overclocking and Undervolting. Sustainability 2022, 14, 8708. https://doi.org/10.3390/su14148708
Shuaib M, Badotra S, Khalid MI, Algarni AD, Ullah SS, Bourouis S, Iqbal J, Bharany S, Gundaboina L. A Novel Optimization for GPU Mining Using Overclocking and Undervolting. Sustainability. 2022; 14(14):8708. https://doi.org/10.3390/su14148708
Chicago/Turabian StyleShuaib, Mohammed, Sumit Badotra, Muhammad Irfan Khalid, Abeer D. Algarni, Syed Sajid Ullah, Sami Bourouis, Jawaid Iqbal, Salil Bharany, and Lokesh Gundaboina. 2022. "A Novel Optimization for GPU Mining Using Overclocking and Undervolting" Sustainability 14, no. 14: 8708. https://doi.org/10.3390/su14148708
APA StyleShuaib, M., Badotra, S., Khalid, M. I., Algarni, A. D., Ullah, S. S., Bourouis, S., Iqbal, J., Bharany, S., & Gundaboina, L. (2022). A Novel Optimization for GPU Mining Using Overclocking and Undervolting. Sustainability, 14(14), 8708. https://doi.org/10.3390/su14148708