Tesla v100 bitcoin mining

No power connections are needed enabling the Tesla T4 to fit in tight spaces inside the server. Tesla gpu for Bitcoin mining has been praised and criticized. Critics noted its use in illegal transactions, the large amount of electricity victimized by miners, price irresolution, and thefts from exchanges. Usually measured in I was expecting GeForce GTX that nvidia tesla k80 bitcoin. I had singed up with NVidia a while ago for a test drive, but when they called me and I explained it was for a mining kernel, I never heard back from them.

GPU Tesla V Fourth root of Rgb led controller not working Iplayer amazon fire tv. Tesla Card Specifications Tesla k80 mining benchmark. Small semiconductors provide better performance and reduced power consumption. Chipsets with a higher number of transistors, semiconductor components of electronic devices, offer more computational power. A small form factor allows more transistors to fit on a chip, therefore increasing its performance.

Tesla k80 mining benchmark. Moreover, the average energy consumption during this time can be determined as described in Sect. Hashrate, energy consumption and measuring period are stored in a data structure as in the offline phase. The result of the offline phase serves as a starting point for the online phase. The creation of a measuring point function evaluation takes significantly more time in the online phase compared to the offline phase. However, less function evaluations are needed as the starting point is usually already close to the optimum.

Moreover, mining incomes can be earned during the online phase, as real mining is running in the background.

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Similar to the frequency optimization phase, the monitoring phase is also executed by a separate CPU thread for each GPU. The monitoring thread executes an infinite loop and is started when there are optimal frequencies available for all currencies on the corresponding GPU. The monitoring phase is responsible for the periodic calculation of energy costs and mining revenues. The energy costs in Euro per second are computed as follows:.

To compute the mining revenue in Euro per second the following formula is used where hr is the abbreviation for hashrate [ 21 ]:. The average block time is given by the currency e. For this reason the block difficulty is continuously adjusted according to the current network hashrate. Subtracting energy costs from the mining revenue gives the profit:.

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If the currently mined currency is no longer the most profitable one after the recalculation of energy costs and mining revenues, the background mining of this currency is terminated and the mining of a new, more profitable currency is initiated. Subsequently, energy-efficient frequencies for the newly mined currency are determined again, trying to find even better frequencies. The search method is identical to the online frequency optimization method in Sect.

The starting point of the search is the previously used frequency for the particular currency. The previous optimization result is updated with the result of the new optimization.

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The device information of the hardware system to be used is read for all GPUs. The GPUs are subdivided into groups for the offline and the online frequency optimization. Identical GPUs will be allocated to the same group. Each GPU group must be able to optimize the frequencies for all available currencies. After the program is started, existing optimization results from previous measurements can be incorporated for the individual groups.

This will reduce the optimization effort needed. If the optimization result of a group contains values for all currencies, the complete frequency optimization phase is skipped for the particular group.

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The currencies with no optimization result available are divided onto the individual GPUs of the group for frequency optimization. The reason behind the arrangement of GPUs into groups is that the optimum frequencies for the individual currencies are considered to be identical for identical GPUs. Thus, the optimization for the individual currencies can be performed in a parallel fashion within each group, exchanging optimization results between the GPUs of the group. This results in an acceleration of the frequency optimization phase.

Subsequently, a thread is started for every GPU. These threads run for the complete length of the program, i. In the monitoring phase, the threads run in an infinite loop until the user stops the program. At program termination the optimization results that have been updated during the monitoring phase are saved as a JSON file. These results contain information about optimal frequencies, hashrates and energy consumption for each currency on each GPU.

In this section, we evaluate the framework introduced in Sect. For the evaluation, the framework is tested with different GPUs and different currencies as introduced in Sects. In order to determine the energy optimum, i. The energy optimum is usually located in mid-range core frequencies and mid-range to high-range VRAM frequencies. To detect the efficiency increase, the values at optimum frequencies are compared with those at default frequencies see Table 2. ETH: hashrate above , energy consumption middle and hashes per Joule below for all adjustable frequencies on the Titan X left column and the Titan V right column.

As can be seen in Fig. It is also noteworthy that XMR requires relatively low amounts of energy. The efficiency gain via the optimization of frequencies is summarized in Table 3. A possible reason for this is that the higher amount of computing units does not push a GPU to the limits of its capacity, as the low IPC values demonstrate. This also explains the low demand for energy. XMR: hashrate top , energy usage middle and hashes per Joule below at all adjustable frequencies on the Titan X left column and the Titan V right column.

Hence, the energy optimum is usually located at lower VRAM-clock rates and slightly higher core-clock rates see Fig. Moreover, due to the high IPC value see also Fig. Table 4 indicates the efficiency gain for ZEC when using optimal frequencies. ZEC: hashrate above , energy consumption middle and hashes per Joule below at all adjustable frequencies on the Titan X left column and the Titan V right column.

In this section, we will look at the different optimization algorithms see Sect. ZEC will be used as currency.

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Every algorithm Hill Climbing, Simulated Annealing and Nelder-Mead is executed three times with different starting frequencies. The different starting points are the maximum, the minimum and the middle frequency. Furthermore all algorithms are also evaluated with a minimum hashrate as constraint. The maximum number of iterations is set to six for all tests.

The following sections introduce the optimization process and evaluate the performance of the individual algorithms. The performance of the optimization is measured with the following two criteria:. The results of optimizing with Hill Climbing are summarized in Table 5. The first table row shows the optimum found by an exhaustive search see Table 4. The second table row lists the energy efficiency number of hashes per Joule obtained with the associated frequencies in brackets VRAM frequency, core frequency for the different starting points.

The third table row displays the required number of function evaluations to find the best energy efficiency, as well as the total number of function evaluations until termination in brackets. Figure 7 shows the associated optimization procedure of Hill Climbing by following the additional black lines.

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The focus of these figures is more the amount of iterations than the exact path of the Hill Climbing approximation itself. The algorithm process is always shown in combination with the function to be optimized at different starting points for both GPUs. On average a result value of 3. The optimum from Table 4 lies at 4. On the Quadro P, the result value of 3. These small deviations can be explained by measurement inaccuracies.

In general, it is easier to find the optimum at a 1D optimization like on the Quadro P On average it requires five function evaluations to find the best value on the Quadro P and 15 evaluations on the Titan X. Hill Climbing: frequency optimization procedure of ZEC with starting points of minimal above , medial middle and maximal below frequencies on the Titan X left column and the Quadro P right column.

Yet, it is not necessary to escape local optima with Simulated Annealing, since they do not exist. Table 6 shows the result and the required amount of function evaluations for the optimization. The table structure is identical to that of Table 5 explained in Sect.


The corresponding optimization procedures at different starting points are shown in Fig. Again, the exact paths shown as black lines are not as important as the amount of iteration of the Simulated Annealing needs to find a point near the optimum. This is equivalent to a deviation of 0. Aside from measurement inaccuracies, the maximal value is also found on the Quadro P The average number of function evaluations is identical to that of Hill Climbing, being 15 on the Titan X and five on the Quadro P This is in part due to the fact that Simulated Annealing uses the same pattern as Hill Climbing to explore new solution candidates.

Simulated Annealing: frequency optimization procedure of ZEC with starting points of minimal above , medial middle and maximal below frequencies on the Titan X left column and the Quadro P right column.

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The optimization with the Nelder-Mead procedure performs slightly worse than with Hill Climbing and Simulated Annealing. The values obtained and the required number of function evaluations are summarized in Table 7. The table structure is already described in Sect. Figure 9 shows the corresponding optimization procedures at different starting frequencies. The black lines in this figure represent the path of Nelder-Mead.