bench: tensorstore CPU vs damacy GPU read+decode comparison#155
Merged
Conversation
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #155 +/- ##
=======================================
Coverage 57.72% 57.73%
=======================================
Files 64 64
Lines 10055 10055
Branches 1750 1750
=======================================
+ Hits 5804 5805 +1
+ Misses 3501 3500 -1
Partials 750 750
Flags with carried forward coverage won't be shown. Click here to find out more. 🚀 New features to boost your workflow:
|
nclack
added a commit
that referenced
this pull request
Jun 13, 2026
Small follow-up rolling up the remaining working-tree changes left out of #155. Two independent changes, one commit each: - **bench: per-stage in/out throughput + load** — `bench/report.py`'s stage table now reports `GB/s_out` and `load%` (stage `ms_total` / wall) alongside the existing `GB/s_in`, so each pipeline stage shows input and output throughput and its share of the wall — making it obvious which stage bounds a run. - **chore: pixi workspace config** — `[tool.pixi.*]` workspace/environments in `pyproject.toml`, `.pixi/*` ignored in `.gitignore`, and `pixi.lock` merge attributes in `.gitattributes`. --------- Co-authored-by: Nathan Clack <nclack@biohub.org>
github-actions Bot
added a commit
that referenced
this pull request
Jun 13, 2026
Small follow-up rolling up the remaining working-tree changes left out of #155. Two independent changes, one commit each: - **bench: per-stage in/out throughput + load** — `bench/report.py`'s stage table now reports `GB/s_out` and `load%` (stage `ms_total` / wall) alongside the existing `GB/s_in`, so each pipeline stage shows input and output throughput and its share of the wall — making it obvious which stage bounds a run. - **chore: pixi workspace config** — `[tool.pixi.*]` workspace/environments in `pyproject.toml`, `.pixi/*` ignored in `.gitignore`, and `pixi.lock` merge attributes in `.gitattributes`. --------- Co-authored-by: Nathan Clack <nclack@biohub.org> 96d6be8
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Adds
bench/tensorstore_bench.py, a scenario-driven CPU read+decode benchmarkusing tensorstore, so the GPU (damacy) vs CPU (tensorstore) tradeoff for zarr v3
read+decode can be measured on identical data, chunking, and patch sampling.
ScenarioJSON schema viabench/scenario.py(same asrun.py), handling both the synthetic path (uris is None, arrays enumeratedfrom
uri_fmt/array_path/n_zarrs) and explicituris.with the same filtering as the bench so array counts line up.
bench/main.c's xorshift64* RNG and draw order (array index, thenper-axis start), so with a shared seed the sampled patches match damacy's
bit-for-bit and both read the same bytes.
--threadsconcurrency sweep (default 1,2,4,8,16,32) via tensorstore contextlimits + a bounded in-flight read window, reporting samples/s and GB/s per
thread count and the best point — the CPU thread pool is the real comparison
point against a single GPU decode stream.
--drop-cachemirrorsrun.py's page-cache drop for cold-read measurement;--compare-with <damacy results.json>prints a head-to-head line.tensorstore fuses read+decode, so only total throughput is reported (no per-stage
split). Smoke-tested on a synthetic scenario and a uris scenario on a login node;
the full cold sweep runs on a compute node.
Closes #153.