Benchmark#

The included bench.mjs script evaluates loading performance for all supported formats. It loads the file 11 times and reports the time for the last 10 (ignoring the first cold-start / disk cache run).

Running the Benchmark#

node bench.mjs dpsv.trx

Results#

The graph below shows load time for the left inferior fronto-occipital fasciculus (IFOF) from the HCP1065 Population-Averaged Tractography Atlas (Yeh, 2022) — 21,209 streamlines and 4,915,166 vertices.

Formats were generated with tff_convert_tractogram.py. Benchmark run on a MacBook with an M2 Pro CPU, loading from local SSD.

M2 Performance

Format Summary#

Format	Extension	Notes
tt	`.tt`	DSI Studio — very compact; 1/32 voxel precision (slightly lossy)
tt.gz	`.tt.gz`	Gzip-compressed DSI Studio
vtk	`.vtk`	VTK legacy (DiPy OFFSETS, `--offsets_dtype uint32`)
tck	`.tck`	MRtrix format
trk	`.trk`	TrackVis format
trk.gz	`.trk.gz`	Gzip-compressed TrackVis
trk.zst	`.trk.zst`	Zstandard-compressed TrackVis
trx	`.trx`	Uncompressed TRX
z.trx	`.trx`	Zip-compressed TRX
16.trx	`.trx`	TRX with float16 positions
16z.trx	`.trx`	Zip-compressed TRX with float16 positions

Note: Native zstd decompression is very fast, but the JavaScript decompressor (fzstd) is relatively slower than native implementations.