Papers › FastPM: a new scheme for fast simulations of dark matter and halos
FastPM: a new scheme for fast simulations of dark matter and halos
Yu Feng, Man-Yat Chu, Uros Seljak, Patrick McDonald
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We introduce FastPM, a highly-scalable approximated particle mesh N-body solver, which implements the particle mesh (PM) scheme enforcing correct linear displacement (1LPT) evolution via modified kick and drift factors. Employing a 2-dimensional domain decomposing scheme, FastPM scales extremely well with a very large number of CPUs. In contrast to COmoving-LAgrangian (COLA) approach, we do not require to split the force or track separately the 2LPT solution, reducing the code complexity and memory requirements. We compare FastPM with different number of steps (Nₛ) and force resolution factor (B) against 3 benchmarks: halo mass function from Friends of Friends halo finder, halo and dark matter power spectrum, and cross correlation coefficient (or stochasticity), relative to a high resolution TreePM simulation. We show that the modified time stepping scheme reduces the halo stochasticity when compared to COLA with the same number of steps and force resolution. While increasing Nₛ and B improves the transfer function and cross correlation coefficient, for many applications FastPM achieves sufficient accuracy at low Nₛ and B. For example, Nₛ=10 and B=2 simulation provides a substantial saving (a factor of 10) of computing time relative to Nₛ=40, B=3 simulation, yet the halo benchmarks are very similar at z=0. We find that for abundance matched halos the stochasticity remains low even for Nₛ=5. FastPM compares well against less expensive schemes, being only 7 (4) times more expensive than 2LPT initial condition generator for Nₛ=10 (Nₛ=5). Some of the applications where FastPM can be useful are generating a large number of mocks, producing non-linear statistics where one varies a large number of nuisance or cosmological parameters, or serving as part of an initial conditions solver.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections