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# Copyright (c) 2023 European Synchrotron Radiation Facility
# License: MIT https://opensource.org/license/mit/
from __future__ import annotations
import json
import os
import sys
import tempfile
import time
from typing import Optional, NamedTuple
import h5py
import numpy
# Set affinity and env. var. before importing hdf5plugin
if len(sys.argv) >= 2:
NCPU = int(sys.argv[1])
print(f"Set affinity: Using {NCPU} CPU(s)")
os.sched_setaffinity(0, list(range(0, NCPU, 4)))
os.environ["OPENMP_NUM_THREADS"] = str(NCPU)
os.environ["BLOSC_NTHREADS"] = str(NCPU)
else:
NCPU = len(os.sched_getaffinity(0))
print(f"Default affinity: Using {NCPU} CPU(s)")
# Directory where to run read/write benchmarks
DIRECTORY = "/dev/shm"
# Function to get data to use for tests
def get_data():
with h5py.File("/dev/shm/kevlar.h5") as h5file:
return h5file["/entry/data/data"][::10] # Take 100 frames
import hdf5plugin
class Result(NamedTuple):
"""Store benchmark result"""
raw_nbytes: int
compressed_nbytes: int
write_duration: float
read_duration: float
chunks: tuple[int]
compression_rate = property(
lambda self: self.raw_nbytes / self.compressed_nbytes)
write_speed = property(
lambda self: (self.raw_nbytes / 1024**2) / self.write_duration,
doc="Unit: MB/sec")
read_speed = property(
lambda self: (self.raw_nbytes / 1024**2) / self.read_duration,
doc="Unit: MB/sec")
def benchmark(
data: numpy.ndarray,
directory: Optional[str] = None,
**kwargs
) -> Result:
"""Run benchmark for given conditions
:param data: Dataset to use
:param directory: Directory where to write HDF5 file for benchmark
:param kwargs: Arguments passed to h5py.Group.create_dataset
"""
if directory is None:
tmpdir = tempfile.TemporaryDirectory()
dirname = tmpdir.name
else:
dirname = directory
filename = os.path.join(dirname, 'hdf5plugin_benchmark.h5')
if os.path.exists(filename):
os.remove(filename)
# Compression
with h5py.File(filename, "w") as h5file:
dataset = h5file.create_dataset(
"data", shape=data.shape, dtype=data.dtype, **kwargs)
start_write_time = time.perf_counter()
dataset[:] = data
dataset.flush()
write_duration = time.perf_counter() - start_write_time
# Decompression
with h5py.File(filename, "r") as h5file:
dataset = h5file["data"]
start_time = time.perf_counter()
read_data = dataset[()]
read_duration = time.perf_counter() - start_time
storage_size = dataset.id.get_storage_size()
chunks = dataset.chunks
os.remove(filename)
return Result(data.nbytes, storage_size, write_duration, read_duration, chunks=chunks)
DEFAULT_FILTERS = { # Filters available with h5py/libhdf5
"Raw": None,
"GZip": "gzip",
"LZF": "lzf",
}
LOSSLESS_FILTERS = {
"BZip2": hdf5plugin.BZip2(),
"LZ4": hdf5plugin.LZ4(),
"ZStd": hdf5plugin.Zstd(),
}
BITSHUFFLE_FILTERS = {
"Bitshuffle-lz4": hdf5plugin.Bitshuffle(cname='lz4'),
"Bitshuffle-zstd": hdf5plugin.Bitshuffle(cname='zstd'),
}
BLOSC_FILTERS = {}
for cname in ('lz4', 'blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd'):
for shuffle_name, shuffle in [('NoShuffle', hdf5plugin.Blosc.NOSHUFFLE),
('Shuffle', hdf5plugin.Blosc.SHUFFLE),
('BitShuffle', hdf5plugin.Blosc.BITSHUFFLE)]:
for clevel in [5]: #(1, 3, 5, 9):
BLOSC_FILTERS[f"Blosc-{cname}-{shuffle_name}-{clevel}"] = hdf5plugin.Blosc(
cname=cname, clevel=clevel, shuffle=shuffle)
BLOSC2_FILTERS = {}
for cname in ('lz4', 'blosclz', 'lz4', 'lz4hc', 'zlib', 'zstd'):
for filters_name, filters in [('NoFilter', hdf5plugin.Blosc2.NOFILTER),
('Shuffle', hdf5plugin.Blosc2.SHUFFLE),
('BitShuffle', hdf5plugin.Blosc2.BITSHUFFLE)]:
for clevel in [5]: # (1, 3, 5, 9):
BLOSC2_FILTERS[f"Blosc2-{cname}-{filters_name}-{clevel}"] = hdf5plugin.Blosc2(
cname=cname, clevel=clevel, filters=filters)
FILTERS = {
**DEFAULT_FILTERS,
**LOSSLESS_FILTERS,
**BITSHUFFLE_FILTERS,
**BLOSC_FILTERS,
**BLOSC2_FILTERS,
}
print("Read benchmark data...")
data = get_data()
print("Run benchmarks:")
chunks = (1,) + data.shape[1:]
results = {}
for name, compression in FILTERS.items():
print(f"- {name}")
results[name] = benchmark(
data,
directory=DIRECTORY,
chunks=chunks,
compression=compression,
)._asdict()
print("Write results")
with open("benchmark.json", "w") as f:
json.dump(
dict(
config=dict(
ncpu=NCPU,
affinity=list(os.sched_getaffinity(0)),
openmp_num_threads=os.environ.get("OPENMP_NUM_THREADS", "unset"),
blosc_nthreads=os.environ.get("BLOSC_NTHREADS", "unset"),
hdf5plugin=hdf5plugin.get_config().build_config._asdict(),
chunks=chunks,
),
results=results,
),
f,
indent=2,
default=lambda obj: type(obj).__name__,
)
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