Raise default threads for BAM parsing 8->32, BLAS 8->16

It was originally capped at 8 under the belief that reading 8 BAM files in
parallel would saturate the disk, so there would be no benefit of going higher.
However, my laptop can read at 4 GB/s, and decompress BAM files perhaps 40 times
slower, so it's CPU bottlenecked even with 32 threads.

This change is significant, because users have reported slow BAM file parsing.
However, it will potentially quadruple the memory usage of the BAM parsing step.
Will be benchmarked before merging.

The BLAS change is simply because I think 8 CPUs is too conservative.

vamb/__main__.py

@@ -22,13 +22,13 @@
 import pandas as pd
 
 _ncpu = os.cpu_count()
-DEFAULT_THREADS = 8 if _ncpu is None else min(_ncpu, 8)
+DEFAULT_BLAS_THREADS = 16 if _ncpu is None else min(_ncpu, 16)
 
 # These MUST be set before importing numpy
 # I know this is a shitty hack, see https://github.com/numpy/numpy/issues/11826
-os.environ["MKL_NUM_THREADS"] = str(DEFAULT_THREADS)
-os.environ["NUMEXPR_NUM_THREADS"] = str(DEFAULT_THREADS)
-os.environ["OMP_NUM_THREADS"] = str(DEFAULT_THREADS)
+os.environ["MKL_NUM_THREADS"] = str(DEFAULT_BLAS_THREADS)
+os.environ["NUMEXPR_NUM_THREADS"] = str(DEFAULT_BLAS_THREADS)
+os.environ["OMP_NUM_THREADS"] = str(DEFAULT_BLAS_THREADS)
 
 # Append vamb to sys.path to allow vamb import even if vamb was not installed
 # using pip
@@ -771,9 +771,11 @@ def cluster_and_write_files(
             print(
                 str(i + 1),
                 None if cluster.radius is None else round(cluster.radius, 3),
-                None
-                if cluster.observed_pvr is None
-                else round(cluster.observed_pvr, 2),
+                (
+                    None
+                    if cluster.observed_pvr is None
+                    else round(cluster.observed_pvr, 2)
+                ),
                 cluster.kind_str,
                 sum(sequence_lens[i] for i in cluster.members),
                 len(cluster.members),
@@ -1686,9 +1688,11 @@ def add_input_output_arguments(subparser):
         dest="nthreads",
         metavar="",
         type=int,
-        default=DEFAULT_THREADS,
+        default=vamb.parsebam.DEFAULT_BAM_THREADS,
         help=(
-            "number of threads to use " "[min(" + str(DEFAULT_THREADS) + ", nbamfiles)]"
+            "number of threads to read BAM files [min("
+            + str(vamb.parsebam.DEFAULT_BAM_THREADS)
+            + ", nbamfiles)]"
         ),
     )
     inputos.add_argument(

vamb/aamb_encode.py

@@ -1,6 +1,5 @@
 """Adversarial autoencoders (AAE) for metagenomics binning, this files contains the implementation of the AAE"""
 
-
 import numpy as np
 from math import log, isfinite
 import time

vamb/parsebam.py

@@ -14,12 +14,13 @@
 from typing import Optional, TypeVar, Union, IO, Sequence, Iterable
 from pathlib import Path
 import shutil
-
-_ncpu = _os.cpu_count()
-DEFAULT_THREADS = 8 if _ncpu is None else _ncpu
+import os
 
 A = TypeVar("A", bound="Abundance")
 
+_ncpu = os.cpu_count()
+DEFAULT_BAM_THREADS = 32 if _ncpu is None else min(_ncpu, 32)
+
 
 class Abundance:
     "Object representing contig abundance. Contains a matrix and refhash."
@@ -115,10 +116,10 @@ def from_files(
 
         chunksize = min(nthreads, len(paths))
 
-        # We cap it to 16 threads, max. This will prevent pycoverm from consuming a huge amount
+        # We cap it to DEFAULT_BAM_THREADS threads, max. This will prevent pycoverm from consuming a huge amount
         # of memory if given a crapload of threads, and most programs will probably be IO bound
-        # when reading 16 files at a time.
-        chunksize = min(chunksize, 16)
+        # when reading DEFAULT_BAM_THREADS files at a time.
+        chunksize = min(chunksize, DEFAULT_BAM_THREADS)
 
         # If it can be done in memory, do so
         if chunksize >= len(paths):
@@ -134,7 +135,7 @@ def from_files(
         else:
             if cache_directory is None:
                 raise ValueError(
-                    "If min(16, nthreads) < len(paths), cache_directory must not be None"
+                    "If min(DEFAULT_BAM_THREADS, nthreads) < len(paths), cache_directory must not be None"
                 )
             return cls.chunkwise_loading(
                 paths,

vamb/semisupervised_encode.py

@@ -1,6 +1,5 @@
 """Semisupervised multimodal VAEs for metagenomics binning, this files contains the implementation of the VAEVAE for MMSEQ predictions"""
 
-
 __cmd_doc__ = """Encode depths and TNF using a VAE to latent representation"""
 
 import numpy as _np

vamb/taxvamb_encode.py

@@ -1,6 +1,5 @@
 """Hierarchical loss for the labels suggested in https://arxiv.org/abs/2210.10929"""
 
-
 __cmd_doc__ = """Hierarchical loss for the labels"""
 
 

workflow_avamb/src/rip_bins.py

@@ -183,9 +183,9 @@ def remove_meaningless_edges_from_pairs(
                     contig_length,
                 )
                 print("Cluster ripped because of a meaningless edge ", cluster_updated)
-                clusters_changed_but_not_intersecting_contigs[
-                    cluster_updated
-                ] = cluster_contigs[cluster_updated]
+                clusters_changed_but_not_intersecting_contigs[cluster_updated] = (
+                    cluster_contigs[cluster_updated]
+                )
 
     components: list[set[str]] = list()
     for component in nx.connected_components(graph_clusters):
@@ -295,9 +295,9 @@ def make_all_components_pair(
                     contig_length,
                 )
                 print("Cluster ripped because of a pairing component ", cluster_updated)
-                clusters_changed_but_not_intersecting_contigs[
-                    cluster_updated
-                ] = cluster_contigs[cluster_updated]
+                clusters_changed_but_not_intersecting_contigs[cluster_updated] = (
+                    cluster_contigs[cluster_updated]
+                )
                 component_len = max(
                     [
                         len(nx.node_connected_component(graph_clusters, node_i))