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127 lines
5.6 KiB
Markdown
127 lines
5.6 KiB
Markdown
# RandomX
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RandomX is an experimental proof of work (PoW) algorithm that uses random code execution.
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### Key features
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* Memory-hard (requires >4 GiB of memory)
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* CPU-friendly (especially for x86 and ARM architectures)
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* arguably ASIC-resistant
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* inefficient on GPUs
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* unusable for web-mining
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## Virtual machine
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RandomX is intended to be run efficiently on a general-purpose CPU. The virtual machine (VM) which runs RandomX code attempts to simulate a generic CPU using the following set of components:
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Full description: [vm.md](doc/vm.md).
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## Dataset
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RandomX uses a 4 GiB read-only dataset. The dataset is constructed using a combination of the [Argon2d](https://en.wikipedia.org/wiki/Argon2) hashing function, [AES](https://en.wikipedia.org/wiki/Advanced_Encryption_Standard) encryption/decryption and a random permutation. The dataset is regenerated every ~34 hours.
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Full description: [dataset.md](doc/dataset.md).
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## Instruction set
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RandomX uses a simple low-level language (instruction set), which was designed so that any random bitstring forms a valid program. Each RandomX instruction has a length of 128 bits.
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Full description: [isa.md](doc/isa.md).
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## Implementation
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Proof-of-concept implementation is written in C++.
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```
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> bin/randomx --help
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Usage: bin/randomx [OPTIONS]
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Supported options:
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--help shows this message
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--compiled use x86-64 JIT-compiled VM (default: interpreted VM)
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--lightClient use 'light-client' mode (default: full dataset mode)
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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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--nonces N run N nonces (default: 1000)
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--genAsm generate x86 asm code for nonce N
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```
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Two RandomX virtual machines are implemented:
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### Interpreted VM
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The interpreted VM is the reference implementation, which aims for maximum portability.
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The VM has been tested for correctness on the following platforms:
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* Linux: x86-64, ARMv7 (32-bit), ARMv8 (64-bit)
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* Windows: x86, x86-64
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* MacOS: x86-64
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The interpreted VM supports two modes: "full dataset" mode, which requires more than 4 GiB of virtual memory, and a "light-client" mode, which requires about 64 MiB of memory, but runs significantly slower because dataset blocks are created on the fly rather than simply fetched from memory.
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Software AES implementation is available for CPUs which don't support [AES-NI](https://en.wikipedia.org/wiki/AES_instruction_set).
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The following table lists the performance for Intel Core i5-3230M (Ivy Bridge) CPU using a single core on Windows 64-bit, compiled with Visual Studio 2017:
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|mode|required memory|AES|initialization time [s]|performance [programs/s]|
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|------|----|-----|-------------------------|------------------|
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|light client|64 MiB|software|1.0|9.2|
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|light client|64 MiB|AES-NI|1.0|16|
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|full dataset|4 GiB|software|54|40|
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|full dataset|4 GiB|AES-NI|26|40|
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### JIT-compiled VM
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A JIT compiler is available for x86-64 CPUs. This implementation shows the approximate performance that can be achieved using optimized mining software. The JIT compiler generates generic x86-64 code without any architecture-specific optimizations. Only "full dataset" mode is supported.
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For optimal performance, an x86-64 CPU needs:
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* 32 KiB of L1 instruction cache per thread
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* 16 KiB of L1 data cache per thread
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* 240 KiB of L2 cache (exclusive) per thread
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The following table lists the performance of AMD Ryzen 7 1700 (clock fixed at 3350 MHz, 1.05 Vcore, dual channel DDR4 2400 MHz) on Linux 64-bit (compiled with GCC 5.4.0).
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Power consumption was measured for the whole system using a wall socket wattmeter (±1W). Table lists difference over idle power consumption. [Prime95](https://en.wikipedia.org/wiki/Prime95#Use_for_stress_testing) (small/in-place FFT) and [Cryptonight V2](https://github.com/monero-project/monero/pull/4218) power consumption are listed for comparison.
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||threads|initialization time [s]|performance [programs/s]|power [W]
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|-|------|----|-----|-------------------------|
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|RandomX (interpreted)|1|27|52|16|
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|RandomX (interpreted)|8|4.0|390|63|
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|RandomX (interpreted)|16|3.5|620|74|
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|RandomX (compiled)|1|27|407|17|
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|RandomX (compiled)|2|14|810|26|
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|RandomX (compiled)|4|7.3|1620|42|
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|RandomX (compiled)|6|5.1|2410|56|
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|RandomX (compiled)|8|4.0|3200|71|
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|RandomX (compiled)|12|4.0|3670|82|
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|RandomX (compiled)|16|3.5|4110|92|
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|Cryptonight v2|8|-|-|47|
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|Prime95|8|-|-|77|
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|Prime95|16|-|-|81|
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## Proof of work
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RandomX VM can be used for PoW using the following steps:
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1. Initialize the VM using a 256-bit hash of any data.
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2. Execute the RandomX program.
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3. Calculate `blake2b(RegisterFile || t1ha2(Scratchpad))`*
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\* [blake2b](https://en.wikipedia.org/wiki/BLAKE_%28hash_function%29#BLAKE2) is a cryptographic hash function, [t1ha2](https://github.com/leo-yuriev/t1ha) is a fast hashing function.
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The above steps can be chained multiple times to prevent mining strategies that search for programs with particular properties (for example, without division).
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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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* PCG32 random number generator: https://github.com/imneme/pcg-c-basic
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* Software AES implementation https://github.com/fireice-uk/xmr-stak
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* t1ha2 hashing function: https://github.com/leo-yuriev/t1ha
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## Donations
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XMR:
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```
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4B9nWtGhZfAWsTxWujPDGoWfVpJvADxkxJJTmMQp3zk98n8PdLkEKXA5g7FEUjB8JPPHdP959WDWMem3FPDTK2JUU1UbVHo
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```
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