scripts/make_fit: Compress dtbs in parallel

When there are 1500 device tree files it takes quite a while to compress
them. Do it in parallel.

Signed-off-by: Simon Glass <sjg@chromium.org>
Link: https://patch.msgid.link/20260106162738.2605574-7-sjg@chromium.org
Signed-off-by: Nathan Chancellor <nathan@kernel.org>

Author: sjg20

scripts/make_fit.py

@@ -37,6 +37,7 @@
 
 import argparse
 import collections
+import multiprocessing
 import os
 import subprocess
 import sys
@@ -225,25 +226,42 @@ def compress_data(inf, compress):
     return comp_data
 
 
-def output_dtb(fsw, seq, fname, arch, compress):
+def compress_dtb(fname, compress):
+    """Compress a single DTB file
+
+    Args:
+        fname (str): Filename containing the DTB
+        compress (str): Compression algorithm, e.g. 'gzip'
+
+    Returns:
+        tuple: (str: fname, bytes: compressed_data)
+    """
+    with open(fname, 'rb') as inf:
+        compressed = compress_data(inf, compress)
+    return fname, compressed
+
+
+def output_dtb(fsw, seq, fname, arch, compress, data=None):
     """Write out a single devicetree to the FIT
 
     Args:
         fsw (libfdt.FdtSw): Object to use for writing
         seq (int): Sequence number (1 for first)
         fname (str): Filename containing the DTB
-        arch: FIT architecture, e.g. 'arm64'
+        arch (str): FIT architecture, e.g. 'arm64'
         compress (str): Compressed algorithm, e.g. 'gzip'
+        data (bytes): Pre-compressed data (optional)
     """
     with fsw.add_node(f'fdt-{seq}'):
         fsw.property_string('description', os.path.basename(fname))
         fsw.property_string('type', 'flat_dt')
         fsw.property_string('arch', arch)
         fsw.property_string('compression', compress)
 
-        with open(fname, 'rb') as inf:
-            compressed = compress_data(inf, compress)
-        fsw.property('data', compressed)
+        if data is None:
+            with open(fname, 'rb') as inf:
+                data = compress_data(inf, compress)
+        fsw.property('data', data)
 
 
 def process_dtb(fname, args):
@@ -300,6 +318,11 @@ def _process_dtbs(args, fsw, entries, fdts):
     """
     seq = 0
     size = 0
+
+    # First figure out the unique DTB files that need compression
+    todo = []
+    file_info = []  # List of (fname, model, compat, files) tuples
+
     for fname in args.dtbs:
         # Ignore non-DTB (*.dtb) files
         if os.path.splitext(fname)[1] != '.dtb':
@@ -311,11 +334,32 @@ def _process_dtbs(args, fsw, entries, fdts):
             sys.stderr.write(f'Error processing {fname}:\n')
             raise e
 
+        file_info.append((fname, model, compat, files))
+        for fn in files:
+            if fn not in fdts and fn not in todo:
+                todo.append(fn)
+
+    # Compress all DTBs in parallel
+    cache = {}
+    if todo and args.compress != 'none':
+        if args.verbose:
+            print(f'Compressing {len(todo)} DTBs...')
+
+        with multiprocessing.Pool() as pool:
+            compress_args = [(fn, args.compress) for fn in todo]
+            # unpacks each tuple, calls compress_dtb(fn, compress) in parallel
+            results = pool.starmap(compress_dtb, compress_args)
+
+        cache = dict(results)
+
+    # Now write all DTBs to the FIT using pre-compressed data
+    for fname, model, compat, files in file_info:
         for fn in files:
             if fn not in fdts:
                 seq += 1
                 size += os.path.getsize(fn)
-                output_dtb(fsw, seq, fn, args.arch, args.compress)
+                output_dtb(fsw, seq, fn, args.arch, args.compress,
+                           cache.get(fn))
                 fdts[fn] = seq
 
         files_seq = [fdts[fn] for fn in files]