Update CPU count configuration for parallel operations

Changes have been made to improve the control of parallel computations in the application. The number of CPUs used by the mdio_to_segy and to_zarr methods can now be controlled by altering environmental variables MDIO__EXPORT__CPU_COUNT and MDIO__IMPORT__CPU_COUNT respectively. This allows users to optimize the program's performance based on their specific hardware setup.

src/mdio/converters/mdio.py

@@ -3,10 +3,12 @@
 
 from __future__ import annotations
 
+import os
 from os import path
 from tempfile import TemporaryDirectory
 
 import numpy as np
+from psutil import cpu_count
 from tqdm.dask import TqdmCallback
 
 from mdio import MDIOReader
@@ -24,6 +26,10 @@
     distributed = None
 
 
+default_cpus = cpu_count(logical=True)
+NUM_CPUS = int(os.getenv("MDIO__EXPORT__CPU_COUNT", default_cpus))
+
+
 def mdio_to_segy(  # noqa: C901
     mdio_path_or_buffer: str,
     output_segy_path: str,
@@ -176,7 +182,12 @@ def mdio_to_segy(  # noqa: C901
                 out_byteorder=out_byteorder,
                 file_root=tmp_dir.name,
                 axis=tuple(range(1, samples.ndim)),
-            ).compute()
+            )
+
+            if client is not None:
+                flat_files = flat_files.compute()
+            else:
+                flat_files = flat_files.compute(num_workers=NUM_CPUS)
 
         # If whole blocks are missing, remove them from the list.
         missing_mask = flat_files == "missing"

src/mdio/segy/blocked_io.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import multiprocessing as mp
+import os
 from concurrent.futures import ProcessPoolExecutor
 from itertools import repeat
 
@@ -35,8 +36,8 @@
     ZFPY = None
     zfpy = None
 
-# Globals
-NUM_CORES = cpu_count(logical=False)
+default_cpus = cpu_count(logical=True)
+NUM_CPUS = int(os.getenv("MDIO__IMPORT__CPU_COUNT", default_cpus))
 
 
 def to_zarr(
@@ -136,7 +137,7 @@ def to_zarr(
     # For Unix async writes with s3fs/fsspec & multiprocessing,
     # use 'spawn' instead of default 'fork' to avoid deadlocks
     # on cloud stores. Slower but necessary. Default on Windows.
-    num_workers = min(num_chunks, NUM_CORES)
+    num_workers = min(num_chunks, NUM_CPUS)
     context = mp.get_context("spawn")
     executor = ProcessPoolExecutor(max_workers=num_workers, mp_context=context)