[Python] How to parallelize ffmpeg setPTS filter when using GPU?

We have a long python code which chunks the video into multiple parts and changes the speed using setPTS filter.

import ffmpeg
ffmpeg.input(segment_path).filter("setpts", f"{1/speed_factor}*PTS").output(
adjusted_segment_path,vcodec="h264_nvenc", acodec="aac",preset="fast", crf=23, g=30, keyint_min=30, sc_threshold=0,r=30,vsync='cfr',threads=1
).global_args("-hwaccel", "cuda").run(quiet=True, overwrite_output=True,capture_stdout=True, capture_stderr=True)


Now, because this happens multiple times before concatenation, we thought, instead of sequential processing using a ThreadPool, it may help reduce the time. So we did that:

import ffmpeg
import concurrent.futures

def process_video(segment_path, adjusted_segment_path, speed_factor):
ffmpeg.input(segment_path).filter("setpts", f"{1/speed_factor}*PTS").output(
adjusted_segment_path,
vcodec="h264_nvenc",
acodec="aac",
preset="fast",
crf=23,
g=30,
keyint_min=30,
sc_threshold=0,
r=30,
vsync='cfr',
threads=1
).global_args("-hwaccel", "cuda").run(
quiet=True, overwrite_output=True, capture_stdout=True, capture_stderr=True
)


segment_paths = ["input1.mp4", "input2.mp4", "input3.mp4"] # List of input video segments
output_paths = ["output1.mp4", "output2.mp4", "output3.mp4"] # Corresponding output paths
speed_factor = 1.5

# Using ThreadPoolExecutor for concurrent processing
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
futures = [
executor.submit(process_video, seg, out, speed_factor)
for seg, out in zip(segment_paths, output_paths)
]

# Wait for all tasks to complete
for future in concurrent.futures.as_completed(futures):
try:
future.result() # This will raise any exceptions encountered in the thread
except Exception as e:
print(f"Error processing video: {e}")


But the time required did not reduce. Previously, it was 50 seconds for a long video input, now too, it remains the same.

Is there any other way I can improve the code?

I also noticed the GPU utilization is low and the code is still executed sequentially (I can see when I run nvtop, which processes are running)

I am using an L4 GPU with CUDA Version: 12.4, nvcc CUDA Toolkit is also at 12.4

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