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updated readme

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Vasimuddin
2021-11-29 00:52:08 -08:00
committed by Heng Li
parent 3b2eca139a
commit 66c90fdb83
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README.md
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@@ -1,6 +1,6 @@
## mm2-fast
### Introduction
mm2-fast is an accelerated implementation of minimap2 on modern CPUs. mm2-fast accelerates all the three major modules of minimap2: (a) seeding, (b) chaining, and (c) pairwise alignment, achieving up to 1.9x speedup and 1.4x speed using AVX512 and AVX2 respectively over minimap2.
mm2-fast is an accelerated implementation of minimap2 on modern CPUs. mm2-fast accelerates all the three major modules of minimap2: (a) seeding, (b) chaining, and (c) pairwise alignment, achieving up to 1.8x speedup using AVX512 over minimap2.
mm2-fast is a drop-in replacement of minimap2, providing the same functionality with the exact same output.
In the current version, all the modules are optimized using **AVX-512** and **AVX2** vectorization. Detailed benchmark results are available in our [preprint](https://doi.org/10.1101/2021.07.21.453294).
@@ -40,10 +40,10 @@ The output generated by minimap2 and mm2-fast should match.
```sh
diff minimap2_output mm2-fast_output > diff_result
```
The file diff\_result should show a clean-diff with the difference of 0 lines.
The file ```diff_result``` should empty, meaning a difference of 0 lines.
### Advanced options
The default compilation using make applies two optimizations: vectorized chaining and alignment. The learned-indexes based seeding is disabled by default as it requires availability of [Rust](https://en.wikipedia.org/wiki/Rust_(programming_language)). This is because the learned hash-table uses an external training library that runs on Rust. Rust is trivial to install, see https://rustup.rs/ and add its path to .bashrc file. Rust installation only takes a few seconds. Following are the steps to enable learned hash table optimization in mm2-fast:
The default compilation using make applies two optimizations: vectorized chaining and sequence alignment. The learned-indexes based seeding is disabled by default as it requires availability of [Rust](https://en.wikipedia.org/wiki/Rust_(programming_language)). This is because the learned hash-table uses an external training library that runs on Rust. Rust is trivial to install, see https://rustup.rs/ and add its path to .bashrc file. Rust installation only takes a few seconds. Following are the steps to enable learned hash table optimization in mm2-fast:
```sh
# Start by building learned hash table index for optimized seeding module
./build_rmi.sh test/MT-human.fa map-ont ##Takes two arguments: 1. path-to-reference-seq-file 2. preset.
@@ -59,7 +59,7 @@ make clean && make no_opt=1
```
### Performance
We have observed up to 1.9x speedup across datasets (please refer to the paper for more details). For example, for the randomly sampled 100K reads from ["HG002\_GM24385\_1\_2\_3\_Guppy\_3.6.0\_prom.fastq.gz"](https://precision.fda.gov/challenges/10/view), minimap2 takes 92 seconds, while mm2-fast takes 54 seconds to map against the human genome on a 28 cores Intel® Xeon® Platinum 8280 CPUs. Our sampled datasets with 100K reads are available [here](https://drive.google.com/drive/folders/1131j7ejHdT7QZnjxLcTLi5qqwYcfFbuv).
We have observed up to 1.8x speedup across datasets (please refer to the paper for more details). For example, for the randomly sampled 100K reads from ["HG002\_GM24385\_1\_2\_3\_Guppy\_3.6.0\_prom.fastq.gz"](https://precision.fda.gov/challenges/10/view), minimap2 takes 92 seconds, while mm2-fast takes 54 seconds to map against the human genome on a 28 cores Intel® Xeon® Platinum 8280 CPUs. Our sampled datasets with 100K reads are available [here](https://drive.google.com/drive/folders/1131j7ejHdT7QZnjxLcTLi5qqwYcfFbuv).
### Future Plans