Pairwise Alignment Benchmarks

This repository contains a few things:

pa-wrapper: a wrapper library around pairwise aligners;
pa-bin: a unified command line tool to call these aligners;
pa-bench: a tool to benchmark aligners against each other;
evals/astarpa: experiments and analysis for A*PA.
evals/astarpa2: experiments and analysis for A*PA2.

`pa-wrapper`: Wrapper API

pa-wrapper contains a unified API to a number of aligners:

A*PA, A*PA2 Block Aligner, Edlib, Ksw2, Parasail, Triple Accel, [Bi]Wfa

Create an AlignerParams object and call build_aligner() on it to obtain an instance of an aligner, on which .align() can be called repeatedly.

Adding an aligner

To add an aligner, update `pa-wrapper/Cargo.toml` and `pa-wrapper/src/lib.rs`, and add a new file `pa-wrapper/src/wrappers/.rs`. Remember to crash the program for unsupported parameter configurations!

`pa-bin`: Unified binary

Use cargo run --bin pa-bin -- <arguments> input/file/or/dir.{txt,seq,fa} to run any of the supported aligners on some input.

Succinct help of pa-bin (see --help for more):

CLI tool that wraps other aligners and runs them on the given input

Usage: pa-bin [OPTIONS] <--aligner <ALIGNER>|--params <PARAMS>|--params-file <PATH>|--print-params <ALIGNER>> [INPUT] [OUTPUT]

Arguments:
  [INPUT]   (Directory of) .seq, .txt, or Fasta files with sequence pairs to align
  [OUTPUT]  Write a .csv of `{cost},{cigar}` lines. Defaults to input file with .csv extension

Options:
      --cost-only  Return only cost (no traceback)
      --silent     Do not print anything to stderr
  -h, --help       Print help (see more with '--help')

Aligner:
      --aligner <ALIGNER>       The aligner to use with default parameters [possible values: astar-nw, astar-pa,
                                block-aligner, edlib, ksw2, triple-accel, wfa]
      --params <PARAMS>         Yaml/json string of aligner parameters
      --params-file <PATH>      File with aligner parameters
      --print-params <ALIGNER>  Print default parameters for the given aligner [possible values: astar-nw, astar-pa,
                                block-aligner, edlib, ksw2, triple-accel, wfa]
      --json                    The parameters are json instead of yaml

Cost model:
      --sub <COST>     Substitution cost, (> 0) [default: 1]
      --open <COST>    Gap open cost (>= 0) [default: 0]
      --extend <COST>  Gap extend cost (> 0) [default: 1]

The aligner to run can be specified with --aligner <ALIGNER> for default arguments, or --params[-file] to read a (yaml or json) string of parameters (from a file). Use --print-params <ALIGNER> to get default parameters that can be modified.

`pa-bench`: Benchmarking

For benchmarking, see input format, usage, and quick start below.

Benchmarking is done using jobs. Each job consists on an input dataset (a .seq file), a cost model, and a tool with parameters. The pa-bench binary calls itself (recursively) for each job to measure time and memory usage. An experiment consists of a yaml input configuration file is used to specify the list of jobs to run.

Results are incrementally accumulated in a json results file.

Quick start

The easiest way to get started is probably to first clone (and fork) the repository. Then, you can copy either:

The evals/astarpa directory with all experiments (*.yaml) and analysis/plots (evals.ipynb) used in the A*PA paper.
The evals/astarpa-next directory that specifically tests new versions of A*PA on some datasets of ultra long ONT reads of human data. This contains the code to plot boxplots+swarmplots of the distribution of runtimes on a dataset.
Or you can modify/add experiments to evals/experiments/ and use evals/evals.ipynb.

If you think your experiments, analysis, and/or plots are generally useful and interesting, feel free to make a PR to add them here.

Benchmarking features

Main settings

Time limit: Use --time-limit 1h to limit each run to 1 hour using ulimit.
Memory: Use --mem-limit GiB to limit each run to 1GiB of total memory using ulimit.
Nice: Use --nice=-20 to increase the priority of each runner job. This requires root. (See the end of this file.)
Parallel running: Use -j 10 to run 10 jobs in parallel. Each job is pinned to a different core.
Pinning: By default, each job is fixed to run on a single core. This doesn't work on all OSes and can crash/Panic the runner. Use --no-pin to avoid this.
Incremental running: By default, jobs results already present in the target json file are reused. With --rerun-failed, failed jobs are retried, and with --rerun-all, all jobs are rerun. --clean completely removes the cache.

Debugging failing jobs

To see which jobs are being run, use --verbose.
To see the error output of runners, use --stderr. This should be the first thing to do to figure out why jobs are failing.

Output

Output is written to a json file, and also written to a cache that can be reused across experiments.

Runtime of processing input pairs, excluding startup and file io time.
Maximum memory usage (max rss), excluding the memory usage of the input data.
Start and end time of job, for logging purposes.
CPU frequency at start and end of job, as a sanity check.

Other

Skipping: When a job fails, all larger jobs (larger n or e) are automatically skipped.
Interrupting: You can interrupt a run at any time with ctrl-C. This will stop ongoing jobs and write results so far to disk.
Cigar checking: When traceback is enabled, all Cigar strings are checked to see whether they are valid and have the right cost.
Cost checking: The cost returned by exact aligners is cross-validated. For inexact aligners, the fraction of correct results is computed.

Input format

The input is specified as a yaml file containing:

datasets: file paths or settings to generate datasets;
traces: whether each tool computes a path or only the edit distance;
costs: the cost models to run all aligners on;
algos: the algorithms (aligners with parameters) to use.

A job is created for the each combination of the 4 lists.

Examples can be found in evals/experiments/. Here is one:

datasets:
  # Hardcoded data
  - !Data
    - - CGCTGGCTGCTGCCACTAACTCCGTATAGTCTCACCAAGT
      - CGCTGGCTCGCCTGCCACGTAACTCCGTATAGTCTCACCAACTGTCAGTT
    - - AACCAGGGTACACCGACTAATCCACGCACAAGTTGGGGTC
      - ACAGGTACACCACTATCACGACAAGTTGGGTC
  # Path to a single .seq file, relative to `evals/data`
  - !Path path/to/sequences.seq
  # Recursively finds all non-hidden .seq files in a directory, relative to `evals/data`
  - !Path path/to/directory
  # Download and extract a zip file containing .seq files to `evals/data/download/{dir}`
  - !Download
    url: https://github.com/pairwise-alignment/pa-bench/releases/download/datasets/ont-500k.zip
    dir: ont-500k
  # Generated data in `evals/data/generated/`
  - !Generated # Seed for the RNG.
    seed: 31415
    # The approximate total length of the input sequences.
    total_size: 100000
    # The error models to use. See pa-generate crate for more info:
    # https://github.com/pairwise-alignment/pa-generate
    error_models:
      # Uniform, NoisyInsert, NoisyDelete, NoisyMove, NoisyDuplicate, SymmetricRepeat
      - Uniform
    error_rates: [0.01, 0.05, 0.1, 0.1]
    lengths: [100, 1000, 10000, 100000]
# Run both with and without traces
traces: [false, true]
costs:
  # unit costs
  - { sub: 1, open: 0, extend: 1 }
  # affine costs
  - { sub: 1, open: 1, extend: 1 }
algos:
  - !BlockAligner
    size: !Size [32, 8192]
  - !ParasailStriped
  - !Edlib
  - !TripleAccel
  - !Wfa
    memory_model: !MemoryUltraLow
    heuristic: !None
  - !Ksw2
    method: !GlobalSuzukiSse
    band_doubling: false
  - !AstarPa

Usage

Clone this repo and make sure you have Rust installed.
Run cargo run --release -- [--release] evals/experiments/test.yaml from the root.
In case of errors, add --verbose to see which jobs are being run, and/or --stderr to see the output of failing (Result: Err(Panic)) jobs. For non-linus OSes, you may need to add --no-bin to disable pinning to specific cores.

First, this will generate/download required input data files in evals/data. Results are written to evals/results/test.json and a cache of all (outdated) jobs for the current experiment is stored in evals/results/test.cache.json or at the provided --cache.

Succinct help of pa-bench (see --help for more):

Usage: pa-bench bench [OPTIONS] [EXPERIMENTS]...

Arguments:
  [EXPERIMENTS]...  Path to an experiment yaml file

Options:
  -o, --output <OUTPUT>  Path to the output json file. By default mirrors the `experiments` dir in `results`
      --cache <CACHE>    Shared cache of JobResults. Default: <experiment>.cache.json
      --no-cache         Completely disable using a cache
  -j <NUM_JOBS>          Number of parallel jobs to use [default: 5]
      --rerun-all        Ignore job cache, i.e. rerun jobs already present in the results file
      --rerun-failed     Rerun failed jobs that are otherwise reused
      --release          Shorthand for '-j1 --nice=-20'
  -h, --help             Print help (see more with '--help')

Limits:
  -t, --time-limit <TIME_LIMIT>  Time limit. Defaults to value in experiment yaml or 1m
  -m, --mem-limit <MEM_LIMIT>    Memory limit. Defaults to value in experiment yaml or 1GiB
      --nice <NICE>              Process niceness. '--nice=-20' for highest priority
      --no-pin                   Disable pinning, which may not work on OSX

Output:
  -v, --verbose  Print jobs started and finished
      --stderr   Show stderr of runner process

Notes on benchmarking

Niceness. Changing niceness to -20 (the highest priority) requires running pa-bench as root. Alternatively, you could add the following line to /etc/security/limits.conf to allow your user to use lower niceness values:

<username> - nice -20

Pinning. Pinning jobs to cores probably only works on linux. On other systems, benchmarking will crash and will report Result: Err(Panic). Use --no-pin to avoid this.

CPU Settings. Make sure to

fix the cpu frequency using cpupower frequency-set -d 2.6GHz -u 2.6GHz -g powersave (powersave can give more consistent results than performance),
disable hyperthreading,
disable turbo-boost,
disable power saving,
the laptop is fully charged and connected to power.

Datasets

Datasets are available in the datasets release.

Code layout

From low-level to higher, the following crates are relevant:

pa-types: Basic pairwise alignment types such as Seq, Pos, Cost and Cigar.
pa-generate: A utility to generate sequence pairs with various kinds or error types.
pa-wrapper contains an AlignerTrait and implements this uniform interface for all aligners. Each aligner is behind a feature flag. Parasailors is disabled by default do reduce the otherwise large build time.
pa-bin is a thin binary/CLI around pa-wrappers.
pa-bench-types contains the definition of a Experiment, Dataset, Job, JobResult, and the AlgorithmParams enum that selects the algorithm to run and its parameters. This causes pa-bench-types to have dependencies on crates that contain aligner-specific parameter types.
pa-bench contains a binary that collects all jobs in an experiment and calls itself once per job.

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README.md

README.md

Pairwise Alignment Benchmarks

`pa-wrapper`: Wrapper API

`pa-bin`: Unified binary

`pa-bench`: Benchmarking

Quick start

Benchmarking features

Input format

Usage

Notes on benchmarking

Datasets

Code layout

Files

README.md

Latest commit

History

README.md

File metadata and controls

Pairwise Alignment Benchmarks

pa-wrapper: Wrapper API

pa-bin: Unified binary

pa-bench: Benchmarking

Quick start

Benchmarking features

Input format

Usage

Notes on benchmarking

Datasets

Code layout

`pa-wrapper`: Wrapper API

`pa-bin`: Unified binary

`pa-bench`: Benchmarking