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Updated readme
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README.md
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README.md
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@ -4,88 +4,94 @@ RandomX is a proof-of-work (PoW) algorithm that is optimized for general-purpose
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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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## Overview
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RandomX behaves like a keyed hashing function: it accepts a key `K` and arbitrary input `H` and produces a 256-bit result `R`. Under the hood, RandomX utilizes a virtual machine that executes programs in a special instruction set that consists of a mix of integer math, floating point math and branches. These programs can be translated into the CPU's native machine code on the fly. Example of a RandomX program translated into x86-64 assembly is [program.asm](doc/program.asm). A portable interpreter mode is also provided.
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RandomX can operate in two modes:
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* **Fast mode** - requires 2080 MiB of shared memory.
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* **Light mode** - requires only 256 MiB of shared memory, but runs significantly slower and uses more power per hash.
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## Documentation
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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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RandomX is written in C++11 and builds a static library with a C API provided by header file [randomx.h](src/randomx.h). Minimal API usage example is provided in [api-example1.c](src/tests/api-example1.c). The reference code includes a `benchmark` executable for testing.
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Precompiled test binaries are available on the [Releases page](https://github.com/tevador/RandomX/releases).
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### Ubuntu/Debian
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## Usage
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Build dependencies: `make` and `gcc` (minimum version 4.8, but version 7+ is recommended).
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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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Build using the provided makefile.
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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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### Windows
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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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Build dependencies: Visual Studio 2017.
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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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A solution file is provided.
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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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### Precompiled binaries
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### Light mode
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Precompiled `benchmark` binaries are available on the [Releases page](https://github.com/tevador/RandomX/releases).
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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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## Proof of work
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RandomX was primarily designed as a PoW algorithm for [Monero](https://www.getmonero.org/). The recommended usage is following:
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* The key `K` is selected to be the hash of a block in the blockchain - this block is called the 'key block'. For optimal mining and verification performance, the key should change every 2048 blocks (~2.8 days) and there should be a delay of 64 blocks (~2 hours) between the key block and the change of the key `K`. This can be achieved by changing the key when `blockHeight % 2048 == 64` and selecting key block such that `keyBlockHeight % 2048 == 0`.
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* The input `H` is the standard hashing blob.
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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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Preliminary performance of selected CPUs using the optimal number of threads (T) and large pages (if possible), in hashes per second (H/s):
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|CPU|RAM|OS|AES|RandomX (mining)|RandomX (light)|
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|CPU|RAM|OS|AES|Fast mode|Light mode|
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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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AMD Ryzen 7 1700|16 GB DDR4|Ubuntu 16.04|hardware|4080 H/s (8T)|620 H/s (16T)|
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Intel Core i7-8550U|16 GB DDR4|Windows 10|hardware|1700 H/s (4T)|350 H/s (8T)|
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Intel Core i3-3220|2 GB DDR3|Ubuntu 16.04|software|-|120 H/s (4T)|
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Raspberry Pi 3|1 GB DDR2|Ubuntu 16.04|software|-|2.0 H/s (4T) †|
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† Using the interpreter mode. Compiled mode is expected to increase performance by a factor of 10.
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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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RandomX was designed to be efficient on CPUs. Designing an algorithm compatible with both CPUs and GPUs brings 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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Efficient mining requires more than 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 requirement 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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### Since RandomX uses floating point math, does 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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The reference implementation has been validated on the following platforms:
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* x86+SSE2 (32-bit, little-endian)
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* x86-64 (64-bit, little-endian)
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* ARMv7+NEON (32-bit, little-endian)
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* ARMv8 (64-bit, little-endian)
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* PPC64 (64-bit, big-endian)
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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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* [SChernykh](https://github.com/SChernykh) - contributed significantly to the design of RandomX
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* [hyc](https://github.com/hyc) - original idea of using random code execution for PoW
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* [nioroso-x3](https://github.com/nioroso-x3) - provided access to PowerPC for testing purposes
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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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XMR (tevador):
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```
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845xHUh5GvfHwc2R8DVJCE7BT2sd4YEcmjG8GNSdmeNsP5DTEjXd1CNgxTcjHjiFuthRHAoVEJjM7GyKzQKLJtbd56xbh7V
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```
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10
doc/specs.md
10
doc/specs.md
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@ -622,20 +622,20 @@ Whenever a register is selected as the operand of a CBRANCH instruction, its `co
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The CBRANCH instruction performs the following steps:
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1. A constant `b` is calculated as `mod.cond + RANDOMX_JUMP_OFFSET`.
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1. A constant `conditionImmediate` is constructed as sign-extended `imm32` with bit `b` set to 1 and bit `b-1` set to 0 (if `b > 0`).
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1. `conditionImmediate` is added to `creg`.
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1. A constant `cimm` is constructed as sign-extended `imm32` with bit `b` set to 1 and bit `b-1` set to 0 (if `b > 0`).
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1. `cimm` is added to `creg`.
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1. If bits `b` to `b + RANDOMX_JUMP_BITS - 1` of `creg` are zero, execution jumps to instruction `creg.lastUsed + 1` (the instruction following the instruction where `creg` was last modified).
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Bits in immediate and register values are numbered from 0 to 63 with 0 being the least significant bit. For example, for `b = 10` and `RANDOMX_JUMP_BITS = 8`, the bits are arranged like this:
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```
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conditionImmediate = SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSMMMMMMMMMMMMMMMMMMMMM10MMMMMMMMM
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creg = ..............................................XXXXXXXX..........
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cimm = SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSMMMMMMMMMMMMMMMMMMMMM10MMMMMMMMM
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creg = ..............................................XXXXXXXX..........
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```
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`S` is a copied sign bit from `imm32`. `M` denotes bits of `imm32`. The 9th bit is set to 0 and the 10th bit is set to 1. This value would be added to `creg`.
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The second line uses `X` to mark bits of `creg` that would be checked by the condition. If all these bits are 0 after adding `conditionImmediate`, the jump is executed.
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The second line uses `X` to mark bits of `creg` that would be checked by the condition. If all these bits are 0 after adding `cimm`, the jump is executed.
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The construction of the CBRANCH instruction ensures that no inifinite loops are possible in the program.
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