Cypher_Stack/RandomWOW
2
0
Fork 0
mirror of https://git.wownero.com/wownero/RandomWOW.git synced 2024-12-22 07:48:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

Updated GPU mining section in readme

Browse Source
This commit is contained in:
tevador 2019-05-08 10:38:52 +02:00 committed by GitHub
parent bdc593fd5c
commit ddb3aea562
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 7 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
README.md
View File

@ -52,7 +52,7 @@ If you wish to use RandomX as a PoW algorithm for your cryptocurrency, we strong
* Scratchpad size (`RANDOMX_SCRATCHPAD_L3`, `RANDOMX_SCRATCHPAD_L2` and `RANDOMX_SCRATCHPAD_L1`). * Scratchpad size (`RANDOMX_SCRATCHPAD_L3`, `RANDOMX_SCRATCHPAD_L2` and `RANDOMX_SCRATCHPAD_L1`).
* Instruction frequencies (parameters starting with `RANDOMX_FREQ_`). * Instruction frequencies (parameters starting with `RANDOMX_FREQ_`).
### Performance ### CPU mining performance
Preliminary performance of selected CPUs using the optimal number of threads (T) and large pages (if possible), in hashes per second (H/s): 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| |CPU|RAM|OS|AES|Fast mode|Light mode|
@ -64,16 +64,14 @@ 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. † Using the interpreter mode. Compiled mode is expected to increase performance by a factor of 10.
### GPU mining performance
SChernykh has developed a CUDA miner for NVIDIA GPUs. [Benchmarks are listed here](https://github.com/SChernykh/RandomX_CUDA).
Note that GPUs are at a disadvantage when running RandomX since the algorithm was designed to be efficient on CPUs.
# FAQ # FAQ
### Can RandomX run on a GPU?
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.
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.
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)).
### Does RandomX facilitate botnets/malware mining or web mining? ### Does RandomX facilitate botnets/malware mining or web mining?
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. 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.