I tried t60x4 get around 370. and T60x4 gives me 570.
So T59x4 is the best so far.
There seems to be a lot of confusion over the different kernels since the release so I just want to repost directly from the README:
Prefix Alias Compute Req. Registers use for
F L 1.0 64 scrypt & low N-factor scrypt-jane
K Y 3.0 63 scrypt & low N-factor scrypt-jane
T Z 3.5 80 scrypt & low N-factor scrypt-jane
f X 1.0 32 high N-factor scrypt-jane
k 3.0 32 high N-factor scrypt-jane
t 3.5 32 high N-factor scrypt-jane
the old "Legacy" kernel has been replaced with the F kernel, which will also be faster on Compute 1.0 legacy devices in many cases.
Therefore the F kernel has been compiled to require only Compute 1.0 capability.
T, K, F, t, k, and f are different kernels with different uses. Please keep this in mind when you are making suggestions to others on what kernel to use.
For those who were using interim releases (special thanks to those who provided their self compiled binaries!)
- If you were using T, K, or X kernels for scrypt-jane , you will want to use t, k, or f respectively.
- If you were using Z, Y for scrypt in the interim releases, you will want to use T, K not t, k.
SMX counts(source
http://en.wikipedia.org/wiki/CUDA):
Compute ver. 1.0 1.1 1.2 1.3 2.0 2.1 3.0 3.5
Cores per SMX 8 8 8 8 32 48 192 192
To find the compute level of your card please review this site:
http://www.techpowerup.com/gpudb/For scrypt using the K Kernel generally the best basic configuration is going to be K(SMX)x32, you can find the SMX number by finding the number of Cuda cores on your card and dividing by 192. For example a GTX 680 has 1536 Cuda cores this means the SMX for your card is 8 giving you a basic config of:
For scrypt using the T Kernel generally the best basic configuration is going to be T(SMX)x24, you can find the SMX number by finding the number of Cuda cores on your card and dividing by 192. For example a GTX Titan has 2688 Cuda cores this means the SMX for your card is 14 giving you a basic config of:
Special note here, some oddities have been noted for some makes and models of cards in this kernel and the T(SMX)x24 is not always the best, Once you have establish a baseline hash rate at T(SMX)x24 try to reduce the "warps" section (ex. T(SMX)x20 or T(SMX)x16) and see if there is improvement.
For scrypt using the F Kernel generally the best basic configuration is going to be F(SMX/2)x32, you can find the SMX number by finding the number of Cuda cores on your card and dividing by the appropriate core count for your Compute capability. For example a GTX 580 has 512 Cuda cores and is compute 2.0 this means the SMX for your card is 16 giving you a basic config of:
Note: If anyone has a different observation for the Fermi cards please let me know! I have only compiled this part from observation I have no direct experience with this GPU Class and Cudaminer.
This only helps with basic Kernel selection and configuration, it is up to the individual to identify what additional -H, -C, -m, and -L parameters work best on your configuration.
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