HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
ProjectPhysX
FluidX3D
C++2022–2025
ProjectPhysX / FluidX3D

FluidX3D

The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.

7Distinct people
13Mentions
0Explicit recommenders
5.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:02:05 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year13 dated mentions

Observed mentions: 2022-08-06 02:59:25 UTC to 2025-04-24 19:57:59 UTC.

0123452022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC211
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC523
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC422
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202

What Hacker News says

2 sources
Sorry my interest there is debugging and I'm not immediately coming across good benchmarks. PoCL [0] seems to have added a TBB backend [1] so I'd expect it to be reasonable (otherwise why bother) but I haven't tested it. It isn't really related to your question but I think the FluidX3D benchmarks [2] illustrate that OpenCL is at least viable across a wide variety of hardware. As far as targeting CPUs in a release build it's not a particular backend that's important to me. The issue is at the source code level. Having single source is nice but you're still stuck with these two very different approaches. It means that the code is still clearly segmented and thus retargeting any given task (at least nontrivial ones) involves rewriting it to at least some extent. Contrast that with a model like OpenMP where the difference between CPU and GPU
Read the rest
is marking the relevant segment for offload. Granted that you'll often need to change algorithms when switching to achieve reasonable performance but it's still a really nice quality of life feature not to have to juggle more paradigms and libraries. [0] https://github.com/pocl/pocl [1] https://portablecl.org/docs/html/drivers.html [2] https://github.com/ProjectPhysX/FluidX3D
In CubeCL: GPU Kernels in Rust for CUDA, ROCm, and WGPU

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.