![]() If that were true, I’d advise you not to stop at 13. It’s amazing how prevalent this line of thinking is when it comes to streams. If i use 13 streams will the hashing power be 13x faster ? If you want to learn what streams are for, google “gtc cuda streams” and take the first hit. Piecemeal Q+A “Socratic” method might seem “efficient” but is actually quite inefficient for learning a body of knowledge like this, IMO. I know you’d like to learn it in 5 minutes, but in my experience it takes longer than that. Please avail yourself of an organized introduction to CUDA. A kernel with enough blocks will fill all the SMs on a GPU, and this is generally desirable. If I run a single kernel, there is only one SM working ? If you don’t believe me, print out those quantities. Sha256_kernel > (g_out, g_hash_out, g_found, d_in, input_size, difficulty, nonce) #define NUMBLOCKS (SHA_PER_ITERATIONS + BLOCK_SIZE - 1) / BLOCK_SIZE I looked at this: #define BLOCK_SIZE 1024 I don’t understand why you are talking about 4 blocks because I run 10 blocks of 1024 threads. Here’s the cpu version : GitHub - moffa13/SHA256Speed Here’s the github : GitHub - moffa13/SHA256CUDA Please tell me what should be changed in order to optimize this :) Is it the right way, calling multiple times the kernel ? If not, I rerun the kernel with an updated nonce offset. After the kernel call, I run cudaDeviceSynchronize and I check if the global variable is set to 1. If one thread finds the right nonce, a global variable is set to 1 then the other threads return. One thread does one hash as it can’t be parallelised. What I’m doing is that I run a for loop which starts a kernel and processes hashes. I’m pretty sure I can optimize this but I don’t know how. I created the same program but for cpu and I can do over 6 millions hashes in a second. You enter a string then it finds an associated nonce prepended to the input string matching a sha256 with some number of zeros at the beggining.įor example if I want a difficulty of 6 and I put “moffa13” as input string, the program returnsĦ253010moffa13 which has this associated hash :Ġ000002dece0c0f5791305f53bfd5116966ea97a9604984cbb50891f243e5641 (6 zeros before) ![]() Mining calculation is carried out online - errors are possible at the time of updating data via API from information providers.I’m new to cuda development, I tried to do a program which illustrates the bitcoin mining difficulty. Devices can differ not only in hashrate, but also in consumption both up and down. Use the device navigation on the yield page to switch the yield table. The calculation of profitability for ASICs differs from the calculation for video cards (GPUs) by the ability to use several mining algorithms at once.Īctive models are those where the calculator saves the mining payback history based on the Crazy Mining price. Graphs of historical profitability and payback are based on the calculation of free electricity. The final calculation depends on the cost of electricity. The main currency of the calculator is Bitcoin(BTC) as the most stable in profitability, except for the cases of mining altcoins that are not traded against the alt/btc pair. The data is collected daily and updated in real time due to changes in the exchange rate of stablecoins. If you have hashrate data specifically for your graphics card, in this case you can enter this data into the calculator and it will automatically calculate the. When using a calculator, you should pay attention to the following factors:
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