mirror of
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91 lines
4.6 KiB
Markdown
91 lines
4.6 KiB
Markdown
# RandomX
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RandomX is a proof-of-work (PoW) algorithm that is optimized for general-purpose CPUs. RandomX uses random code execution (hence the name) together with several memory-hard techniques to achieve the following goals:
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* Prevent the development of a single-chip [ASIC](https://en.wikipedia.org/wiki/Application-specific_integrated_circuit)
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* Minimize the efficiency advantage of specialized hardware compared to a general-purpose CPU
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## Specification
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Full specification available in [specs.md](doc/specs.md).
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## Design
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Design notes available in [design.md](doc/design.md).
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## Build
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Build using `make`. Requires a C++11 compliant compiler. There are no dependencies.
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Precompiled test binaries are available on the [Releases page](https://github.com/tevador/RandomX/releases).
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## Usage
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```
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Usage: randomx [OPTIONS]
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Supported options:
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--help shows this message
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--mine mining mode: 2 GiB, x86-64 JIT compiled VM
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--verify verification mode: 256 MiB
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--jit x86-64 JIT compiled verification mode (default: interpreter)
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--largePages use large pages
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--softAes use software AES (default: x86 AES-NI)
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--threads T use T threads (default: 1)
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--init Q initialize dataset with Q threads (default: 1)
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--nonces N run N nonces (default: 1000)
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--genAsm generate x86-64 asm code for nonce N
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--genNative generate RandomX code for nonce N
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```
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### Mining mode
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Mining mode requires >2 GiB of RAM and optimal performance should be obtained with at least 16 KiB of L1 cache, 256 KiB of L2 cache and 2 MiB of L3 cache per mining thread.
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The reference miner supports only x86 64-bit CPUs at the moment. [AES-NI](https://en.wikipedia.org/wiki/AES_instruction_set) support is not required, but using the `--softAes` option reduces mining performance by about 40%.
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It is recommended to use [large pages](https://en.wikipedia.org/wiki/Page_(computer_memory)#Multiple_page_sizes) with the `--largePages` option. Using the default page size can reduce performance by up to 50% due to [TLB thrashing](https://en.wikipedia.org/wiki/Thrashing_(computer_science)#TLB_thrashing).
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[NUMA](https://en.wikipedia.org/wiki/Non-uniform_memory_access) systems should run one instance of RandomX per NUMA node.
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### Light mode
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Verification is done in the 'light' mode, which requires only 256 MiB of memory, but runs much slower than the mining mode. Use the `--jit` option on x86-64 CPUs for maximum verification performance.
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### Performance
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Preliminary performance using the optimal number of threads and large pages (if possible):
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|CPU|RAM|OS|AES|RandomX (mining)|RandomX (light)|
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|---|---|--|---|---------|--------------|
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AMD Ryzen 7 1700|16 GB DDR4|Ubuntu 16.04|HW|4250 H/s (8T)|640 H/s (16T)|
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Intel Core i7-8550U|16 GB DDR4|Windows 10|HW|1660 H/s (4T)|128 H/s (4T)|
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Intel Core i3-3220|2 GB DDR3|Ubuntu 16.04|software|-|187 H/s (4T)|
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Raspberry Pi 3|1 GB DDR2|Ubuntu 16.04|software|-|12.3 H/s (4T)|
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# FAQ
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### Can RandomX run on a GPU?
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RandomX was designed to be efficient on CPUs. Designing an algorithm compatible with both CPUs and GPUs brings too many limitations and ultimately decreases ASIC resistance.
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GPUs are expected to be at a disadvantage when running RandomX, but the exact performance has not been determined yet due to lack of a working GPU implementation.
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A rough estimate for AMD Vega 56 GPU gave an upper limit of 1200 H/s, comparable to a quad core CPU (details in issue [#24](https://github.com/tevador/RandomX/issues/24)).
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### Does RandomX facilitate botnets/malware mining or web mining?
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Quite the opposite. Efficient mining requires 2 GiB of memory, which is difficult to hide in an infected computer and disqualifies many low-end machines such as IoT devices. Web mining is nearly impossible due to the large memory requirements and low performance in interpreted mode.
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### Since RandomX uses floating point calculations, how can it give reproducible results on different platforms?
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RandomX uses only operations that are guaranteed to give correctly rounded results by the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard: addition, subtraction, multiplication, division and square root. Special care is taken to avoid corner cases such as NaN values or denormals.
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## Acknowledgements
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The following people have contributed to the design of RandomX:
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* [SChernykh](https://github.com/SChernykh)
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* [hyc](https://github.com/hyc)
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RandomX uses some source code from the following 3rd party repositories:
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* Argon2d, Blake2b hashing functions: https://github.com/P-H-C/phc-winner-argon2
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## Donations
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XMR:
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```
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845xHUh5GvfHwc2R8DVJCE7BT2sd4YEcmjG8GNSdmeNsP5DTEjXd1CNgxTcjHjiFuthRHAoVEJjM7GyKzQKLJtbd56xbh7V
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```
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