This is a new implementation of a chia plotter which is designed as a processing pipeline, similar to how GPUs work, only the "cores" are normal software CPU threads.
As a result this plotter is able to fully max out any storage device's bandwidth, simply by increasing the number of "cores", ie. threads.
Check discord for support: https://discord.gg/rj46Dc5c
For <poolkey> and <farmerkey> see output of `chia keys show`.
<tmpdir> needs about 220 GiB space, it will handle about 25% of all writes. (Examples: './', '/mnt/tmp/')
<tmpdir2> needs about 110 GiB space and ideally is a RAM drive, it will handle about 75% of all writes.
Combined (tmpdir + tmpdir2) peak disk usage is less than 256 GiB.
Usage:
chia_plot [OPTION...]
-n, --count arg Number of plots to create (default = 1, -1 = infinite)
-r, --threads arg Number of threads (default = 4)
-u, --buckets arg Number of buckets (default = 256)
-t, --tmpdir arg Temporary directory, needs ~220 GiB (default = $PWD)
-2, --tmpdir2 arg Temporary directory 2, needs ~110 GiB [RAM] (default = <tmpdir>)
-d, --finaldir arg Final directory (default = <tmpdir>)
-p, --poolkey arg Pool Public Key (48 bytes)
-f, --farmerkey arg Farmer Public Key (48 bytes)
--help Print help
Make sure to crank up <threads> if you have plenty of cores, the default is 4.
Depending on the phase more threads will be launched, the setting is just a multiplier.
RAM usage depends on <threads> and <buckets>.
With the new default of 256 buckets it's about 0.5 GB per thread at most.
sudo mount -t tmpfs -o size=110G tmpfs /mnt/ram/
XCH: xch1w5c2vv5ak08pczeph7tp5xmkl5762pdf3pyjkg9z4ks4ed55j3psgay0zh
I developed this on my own time, even though I already filled all my HDDs (~50 TiB) with the official (slow) plotter.
On a dual Xeon(R) E5-2650v2@2.60GHz R720 with 256GB RAM and a 3x800GB SATA SSD RAID0, using a 110G tmpfs for <tmpdir2>:
Number of Threads: 16
Number of Buckets: 2^8 (256)
Working Directory: /mnt/tmp3/chia/tmp/
Working Directory 2: /mnt/tmp3/chia/tmp/ram/
[P1] Table 1 took 17.2488 sec
[P1] Table 2 took 145.011 sec, found 4294911201 matches
[P1] Table 3 took 170.86 sec, found 4294940789 matches
[P1] Table 4 took 203.713 sec, found 4294874801 matches
[P1] Table 5 took 201.346 sec, found 4294830453 matches
[P1] Table 6 took 195.928 sec, found 4294681297 matches
[P1] Table 7 took 158.053 sec, found 4294486972 matches
Phase 1 took 1092.2 sec
[P2] max_table_size = 4294967296
[P2] Table 7 scan took 15.5542 sec
[P2] Table 7 rewrite took 37.7806 sec, dropped 0 entries (0 %)
[P2] Table 6 scan took 46.7014 sec
[P2] Table 6 rewrite took 65.7315 sec, dropped 581295425 entries (13.5352 %)
[P2] Table 5 scan took 45.4663 sec
[P2] Table 5 rewrite took 61.9683 sec, dropped 761999997 entries (17.7423 %)
[P2] Table 4 scan took 44.8217 sec
[P2] Table 4 rewrite took 61.36 sec, dropped 828847725 entries (19.2985 %)
[P2] Table 3 scan took 44.9121 sec
[P2] Table 3 rewrite took 61.5872 sec, dropped 855110820 entries (19.9097 %)
[P2] Table 2 scan took 43.641 sec
[P2] Table 2 rewrite took 59.6939 sec, dropped 865543167 entries (20.1528 %)
Phase 2 took 620.488 sec
Wrote plot header with 268 bytes
[P3-1] Table 2 took 73.1018 sec, wrote 3429368034 right entries
[P3-2] Table 2 took 42.3999 sec, wrote 3429368034 left entries, 3429368034 final
[P3-1] Table 3 took 68.9318 sec, wrote 3439829969 right entries
[P3-2] Table 3 took 43.8179 sec, wrote 3439829969 left entries, 3439829969 final
[P3-1] Table 4 took 71.3236 sec, wrote 3466027076 right entries
[P3-2] Table 4 took 46.2887 sec, wrote 3466027076 left entries, 3466027076 final
[P3-1] Table 5 took 70.6369 sec, wrote 3532830456 right entries
[P3-2] Table 5 took 45.5857 sec, wrote 3532830456 left entries, 3532830456 final
[P3-1] Table 6 took 75.8534 sec, wrote 3713385872 right entries
[P3-2] Table 6 took 48.8266 sec, wrote 3713385872 left entries, 3713385872 final
[P3-1] Table 7 took 83.2586 sec, wrote 4294486972 right entries
[P3-2] Table 7 took 56.3803 sec, wrote 4294486972 left entries, 4294486972 final
Phase 3 took 733.323 sec, wrote 21875928379 entries to final plot
[P4] Starting to write C1 and C3 tables
[P4] Finished writing C1 and C3 tables
[P4] Writing C2 table
[P4] Finished writing C2 table
Phase 4 took 84.6697 sec, final plot size is 108828428322 bytes
Total plot creation time was 2530.76 sec
To make sure the plots are valid you can use the ProofOfSpace tool from chiapos:
git clone https://github.com/Chia-Network/chiapos.git
cd chiapos && mkdir build && cd build && cmake .. && make -j8
./ProofOfSpace check -f plot-k32-???.plot [num_iterations]I do have some history with GPU mining, back in 2014 I was the first to open source a XPM GPU miner, which was about 40x more efficient than the CPU miner. See my other repos.
As such, it's only a matter of time until I'll add OpenCL support to speed up the plotter even more, keeping most of the load off the CPUs.
- cmake (>=3.14)
- libgmp3-dev
- libsodium-dev
Binaries built by stotiks can be found here: https://github.com/stotiks/chia-plotter/releases
sudo pamac install cmake gmp libgmp-static libsodium libsodium-static gcc10
# Checkout the source and install
git clone https://github.com/madMAx43v3r/chia-plotter.git
cd chia-plotter
# Use gcc10 during build
export CC=gcc-10
export CXX=g++-10
git submodule update --init
./make_devel.sh
./build/chia_plot --helpgit clone https://github.com/dendil/chia-plotter.git
cd chia-plotter
git submodule update --init
sudo yum install epel-release -y
sudo yum install cmake3 gmp-devel libsodium gmp-static libsodium-static -y
ln /usr/bin/cmake3 /usr/bin/cmake
# Install a package with repository for your system:
# On CentOS, install package centos-release-scl available in CentOS repository:
sudo yum install centos-release-scl -y
# Install the collection:
sudo yum install devtoolset-7 -y
# Start using software collections:
scl enable devtoolset-7 bash
./make_devel.sh
./build/chia_plot --helpsudo apt install -y libsodium-dev libgmp3-dev cmake g++ git
# Checkout the source and install
git clone https://github.com/madMAx43v3r/chia-plotter.git
cd chia-plotter
git submodule update --init
./make_devel.sh
./build/chia_plot --helpThe binaries will end up in build/, you can copy them elsewhere freely (on the same machine, or similar OS).
First you need to install a package manager called Brew and Xcode from the Apple App Store.
brew install libsodium gmp cmake git autoconf automake libtool
sudo ln -s /usr/local/include/gmp.h /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/usr/include/
sudo ln -s /usr/local/include/sodium.h /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/usr/include/
sudo ln -s /usr/local/include/sodium /Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk/usr/include/
git clone https://github.com/madMAx43v3r/chia-plotter.git
cd chia-plotter
git submodule update --init
./make_devel.sh
./build/chia_plot --helpIn some setups and scenarios, it could be useful to run your plotter inside a Docker container. This could be potentially useful while running chia-plotter in Windows.
To do so, install Docker in your computer and them run the following command:
docker run \
-v <path-to-your-tmp-dir>:/mnt/harvester \
-v <path-to-your-final-dir>:/mnt/farm \
odelucca/chia-plotter \
-t /mnt/harvester/ \
-d /mnt/farm/ \
-p <pool-key> \
-f <farm-key> \
-r <number-of-CPU-cores>💡 You can provide any of the plotter arguments after the image name (
odelucca/chia-plotter)
In a Linux benchmark, we were able to find that running in Docker has only 5% performance impact than running in native OS.
For Windows users, you should check if your Docker configuration has any RAM or CPU limits. Since Docke runs inside HyperV, that could potentially constrain your hardware usage. In any case, you can set the RAM limits with the -m flag (after the docker run command).
While running in Windows, you may need to proper configure your Docker to allow multi CPUs. You can do so by following this article
In a nutshell, you could also pass the --cpus flag to your docker run command in order to achieve the same result.
So, for example, the following command:
docker run \
-v <path-to-your-tmp-dir>:/mnt/harvester \
-v <path-to-your-final-dir>:/mnt/farm \
-m 8000 \
--cpus 8 \
odelucca/chia-plotter \
-t /mnt/harvester/ \
-d /mnt/farm/ \
-p <pool-key> \
-f <farm-key> \
-r 8Would run your plotter with 8 CPUs and 8GB of RAM.
- Doesn't compile with gcc-11, use a lower version.
- Needs at least cmake 3.14 (because of bls-signatures)