Скачал данный майнер
https://bitcointalk.org/index.php?topic=167229.0 Пытаюсь запустить. Вообще не понимаю как. Помогите плиз
у меня получается на данный момент так
http://s017.radikal.ru/i428/1304/cb/6e19c538b9cbt.jpgЭто по настройке. Но я вот что нифига не могу понять что как его настроить
CudaMiner release April 13th 2013 - alpha release
-------------------------------------------------
this is a CUDA accelerated mining application for litecoin only.
The most computationally heavy parts of the scrypt algorithm (the
Salsa 20/8 iterations) are run on the GPU.
You should see a notable speed-up compared to OpenCL based miners.
Some numbers from my testing:
GTX 260: 44 kHash/sec (OpenCL: 20)
GTX 460: 95 kHash/sec
GTX 660Ti: 156 kHash/sec (OpenCL: 60-70)
Your nVidia cards will now suck a little less for mining! This tool
will automatically use all nVidia GPUs found in your system, but the
used device count can be limited to a lower number using the "-t"
option, or even selected individually with the "-d" option
This code is based on the pooler cpuminer 2.2.3 release and inherits
its command line interface and options.
Additional command line options are:
--no-autotune disables the built-in autotuning feature for
maximizing CUDA kernel efficiency and uses some
heuristical guesswork, which might not be optimal.
--devices [-d] gives a list of CUDA device IDs to operate on.
Device IDs start counting from 0!
--launch-config [-l] specify the kernel launch configuration per device.
This replaces autotune or heuristic selection.
--interactive [-i] list of flags (0 or 1) to enable interactive
desktop performance on individual cards. Use this
to remove lag at the cost of some hashing performance.
--texture-cache [-C] list of flags (0 or 1) to enable use of the
texture cache for reading from the scrypt scratchpad.
This is very experimental and may hurt performance.
--single-memory [-m] list of flags (0 or 1) to make the devices
allocate their scrypt scratchpad in a single,
consecutive memory block. On Windows Vista, 7/8
this may lead to a smaller memory size being used.
>>> Example command line options, advanced use <<<
cudaminer.exe -d 0,1,2 -i 1,0,0 -l auto,S27x3,28x4 -C 0,0,1
-o
http://ltc.kattare.com:9332 -O myworker.1:mypass
I tell cudaminer to use devices 0,1 and 2. Because I have the monitor
attached to device 0, I set that device to run in interactive mode and
to be fully responsive for desktop use while mining.
Device 1 will use kernel launch configuration S27x3, device 2 uses 28x4,
but device 0 performs autotune for interactive mode because I explicitly
set it to auto.
I turn on the use of the texture cache for Device 1, and off for the
other devices.
The given -o/-O settings mine on Burnside's pool, on which I happen to have
an account.
>>> Additional Notes <<<
The HMAC SHA-256 parts of scrypt are still executed on the CPU, and so
any BitCoin mining will NOT be GPU accelerated. This tool is for LTC.
This does not support the Stratum protocol. To do stratum mining
you have to run a local proxy.
This code should be fine on nVidia GPUs ranging from compute
capability 1.1 up to compute capability 3.5. The Geforce Titan has
received experimental and untested support.
To see what autotuning does, enable the debug option (-D) switch.
You will get a table of kHash/s for a variety of launch configurations.
You may only want to do this when running on a single GPU, otherwise
the autotuning output of multiple cards will mix.
>>> RELEASE HISTORY <<<
- the April 14th release lowers the CPU use to a point where it is
almost negligible.
- the April 13th release turns the broken texture cache feature OFF by
default, as now also seems detrimental to performance. So what remains of
yesterday's update is just the interactive mode and the restored
Geforce Titan support.
I also added a validation of GPU results by the CPU.
- the April 12th update boosts Kepler performance by 15-20% by enabling
the texture cache on these devices to do its scrypt scratchpad lookups.
You can also override the use of the texture cache from command line.
I also add an interactive mode for cards that drive monitors, so you
can be almost lag-free when using the desktop. It costs some performance
though. In interactive mode autotuning, smaller kernel launch configs
are selected. Try not to override this with huge launch configs, or the
effect of interactive mode would be negated.
Put Titan support back to its original state. I suspect that a CUDA
compiler bug made the kernel crash when I applied the same optimizations
that work so nicely on Compute 1.0 trough 3.0 devices.
- the April 10th update speeds up the CUDA kernels SIGNIFICANTLY by using
larger memory transactions (yay!!!)
- the April 9th update fixes an autotune problem and adds Linux autotools
support.
- the April 8th release adds CUDA kernel optimizations that may get up to
20% more kHash out of newer cards (Fermi generation and later...).
It also adds UNTESTED Geforce Titan support.
I also use Microsoft's parallel patterns library to split up the CPU
HMAC SHA256 workload over several CPU cores. This was a limiting factor
for some GPUs before.
- the April 6th release adds an auto-tuning feature that determines the
best kernel launch configuration per GPU. It takes up to a few minutes
while the GPU's memory and host CPU may be pegged a bit. You can disable
this tuning with the --no-autotune switch
- April 4th initial release.
>>> About CUDA Kernels <<<
CUDA kernels do the computation. Which one we select and in which
configuration it is run greatly affects performance. CUDA kernel
launch configurations are given as a character string, e.g. S27x3
prefix blocks x warps
Currently there is just one prefix, which is "S". Later releases may
see the introduction of more kernel variants with using other letters.
Examples:
e.g. S27x3 is a launch configuration that works well on GTX 260
28x4 is a launch configuration that works on Geforce GTX 460
290x2 is a launch configuration that works on Geforce GTX 660Ti
You should wait through autotune to see what kernel is found best for
your current hardware configuration.
The choice between Non-Titan and Titan CUDA kernels is automatically
made based on your device's compute capability. Titans cost around
a thousand dollars, so you probably don't have one.
Prefix | Non-Titan | Titan
-------------------------------------------------------
<none> | low shared memory | default kernel
| optimized kernel | with funnel shifter
| |
S | special kernel | spinlock kernel
| for older GPUs | with funnel shifter
>>> TODO <<<
Usability Improvements:
- fix Linux (and Windows?) 64bit compilation
- cleaner shutdown on CTRL-C
- add reasonable error checking for CUDA API calls
- add Stratum support
- add failover support
Further Optimization:
- consider use of some inline assembly in CUDA
- investigate benefits of a LOOKUP_GAP implementation
- feature parity on the Titan kernels (optimization, texture cache)
***************************************************************
If you find this tool useful and like to support its continued
development, then consider a donation in LTC.
The donation address is LKS1WDKGED647msBQfLBHV3Ls8sveGncnm
***************************************************************
Source code is included to satisfy GNU GPL V2 requirements.
With kind regards,
Christian Buchner (
Christian.Buchner@gmail.com )
