"0" is singlethreaded help, "1" is multithreaded help, and "2" gives all the work to the GPU.In order to launch a CUDA kernel we need to specify the block dimension and the grid dimension from the host code. If you are not mining with both GPU and CPU, the values of "0" and "1" should give you some more kh/s. Tip 4: The "-H" flag determines how much your CPU will help your GPU. Compute 2.1 and below may experience better hashrates with "-C 1" rather than "-C 2". Tip 3: Cards with compute 3.x ignores the "-C" flag. Tip 2: Cards with compute 2.1 and below may experience better hashrates using the 32bit version of cudaminer. Tip 1: Cards with compute 1.2 may experience better hashrates with the "S" kernel prefix. bat file with this line in "cudaminer -D -benchmark". If you test it and it doesn't work, try "-l auto", or try running the benchmark tool on CUDA Miner to see what's the best you can get: Create a new. I don't have any legacy of fermi cards for testing. GTX Titan = "-l T14x32" = Titan card (Compute 3.5), 14 Next-Gen Streaming Multiprocessors, 32 warps per SMXĬudaminer -r 10 -R 30 -T 30 -H 1 -i 0 -m 1 -d 0 -l K5x32 -no-autotune -url stratum+tcp://:1234 -u Username.Worker -p Password GTX 660 = "-l K5x32" = Kepler card (Compute 3.0), 5 Next-Gen Streaming Multiprocessors, 32 warps per SMX GTX 570 = "-l F15x16" = Fermi card (Compute 2.0), 15 Streaming Multiprocessors, 16 warps per SM As long as you stay with multiples, it's fine.ĩ800 GTX = "-l 元2x8" = Legacy card (Compute 1.0), 32 Special Function Units, 8 warps per SFU (Quad-pumped process)įERMI USERS: Test your values reversed to see what gives you the best results. (Double-pumped process)Ĭompute 3.x cards are limited to warps per SMX unit. Third value: Warps per SM(or SMX) unit = "-l K5x**(32)**"Ĭompute 1.x cards are limited to warps per SFU unit.Ĭompute 2.x cards are limited to warps per SM unit. Using the table below, divide this by 192, which gives 5 SMX.Ĭompute 1.0 and 1.1: 2 SFUs per unit of 8 Stream Processors.Ĭompute 1.2 and 1.3: 1 SFU per unit of 8 Stream Processors.Ĭompute 2.0: 1 SM per unit of 32 Stream Processors.Ĭompute 2.1: 1 SM per unit of 48 Stream Processors.Ĭompute 3.0 and 3.5: 1 SMX per unit of 192 Stream Processors. Example: GTX 660 has the Core Config "960:80:24" with 960 Stream Processors. In the wiki they are displayed as the first number on the "Core Config" column.
If your card doesn't have the number of SMs specified, calculate it manually by doing the math with the number of SM per unit of Stream Processors. If there are multiple versions of your card, use GPU-Z or NVIDIA Inspector to see what is the name and revision of your GPU and compare to the ones on the wiki. Use this link to find how many SM(or SMX) units your card has. T - For compute 3.5 cards such as Titan, GTX 780 and GK208 based Was used for Kepler cards but was replaced by "K" You can either find it manually by searching your card's compute version and using the right one for your card's compute version in this link. You can easily find what your card achitecture is by running CUDA Miner in autotune mode, by removing the "-l" argument or using it's value as "-l auto" and see what was reported.
To begin with, you need to pass 3 values in this argument, the first being which kernel you'll use for your card, the second is the number of SM(or SMX) your card has, and the 3rd and last value is the number of warps per SM(or SMX) your card is limited to.īEFORE YOU READ: This guide is only valid for the newest version of cudaminer!() So, this guide is to help you understand what you should put in the "-l" argument on CUDA Miner! People often get confused about the kernel launch config on CUDA Miner and start putting random numbers in.