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README.md
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@ -8,10 +8,10 @@ RandomX is a proof-of-work (PoW) algorithm that is optimized for general-purpose
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.
RandomX can operate in two modes:
RandomX can operate in two main modes with different memory requirements:
* **Fast mode** - requires 2080 MiB of shared memory.
* **Light mode** - requires only 256 MiB of shared memory, but runs significantly slower and uses more power per hash.
* **Light mode** - requires only 256 MiB of shared memory, but runs significantly slower
## Documentation
@ -46,14 +46,20 @@ RandomX was primarily designed as a PoW algorithm for [Monero](https://www.getmo
* 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`.
* The input `H` is the standard hashing blob.
If you wish to use RandomX as a PoW algorithm for your cryptocurrency, we strongly recommend not using the [default parameters](src/configuration.h) and change at least the following:
* Size of the Dataset (`RANDOMX_DATASET_BASE_SIZE` and `RANDOMX_DATASET_EXTRA_SIZE`).
* Scratchpad size (`RANDOMX_SCRATCHPAD_L3`, `RANDOMX_SCRATCHPAD_L2` and `RANDOMX_SCRATCHPAD_L1`).
* Instruction frequencies (parameters starting with `RANDOMX_FREQ_`).
### Performance
Preliminary performance of selected CPUs using the optimal number of threads (T) and large pages (if possible), in hashes per second (H/s):
|CPU|RAM|OS|AES|Fast mode|Light mode|
|---|---|--|---|---------|--------------|
AMD Ryzen 7 1700|16 GB DDR4|Ubuntu 16.04|hardware|4080 H/s (8T)|620 H/s (16T)|
AMD Ryzen 7 1700|16 GB DDR4|Ubuntu 16.04|hardware|4090 H/s (8T)|620 H/s (16T)|
Intel Core i7-8550U|16 GB DDR4|Windows 10|hardware|1700 H/s (4T)|350 H/s (8T)|
Intel Core i3-3220|2 GB DDR3|Ubuntu 16.04|software|-|120 H/s (4T)|
Intel Core i3-3220|2 GB DDR3|Ubuntu 16.04|software|-|145 H/s (4T)|
Raspberry Pi 3|1 GB DDR2|Ubuntu 16.04|software|-|2.0 H/s (4T) †|
† Using the interpreter mode. Compiled mode is expected to increase performance by a factor of 10.
@ -90,8 +96,13 @@ The reference implementation has been validated on the following platforms:
RandomX uses some source code from the following 3rd party repositories:
* Argon2d, Blake2b hashing functions: https://github.com/P-H-C/phc-winner-argon2
The author of RandomX declares no competing financial interest in RandomX adoption, other than being a holder or Monero. The development of RandomX was funded from the author's own pocket with only the help listed above.
## Donations
XMR (tevador):
If you'd like to use RandomX, please consider donating to help cover the development cost of the algorithm.
Author's XMR address:
```
845xHUh5GvfHwc2R8DVJCE7BT2sd4YEcmjG8GNSdmeNsP5DTEjXd1CNgxTcjHjiFuthRHAoVEJjM7GyKzQKLJtbd56xbh7V
```