Double precision (FP64) is precious and pricey. Some codes demand it. Many don’t. This guide helps you decide quickly, with simple tests and honest trade‑offs for GPU computing.
TL:DR
- If your solver requires FP64 or loses accuracy in mixed precision, use FP64‑strong hardware (A100/H100 class or CPUs).
- If your code supports mixed/single precision and passes the validation checks below, consumer/workstation GPUs are often your best value. Compute offers on-demand 4090s and 5090s, which are often better than A100s.
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The quick decision tree
- What does the code expect by default?
Default FP64 throughout → likely FP64. Default mixed/single on GPU → likely fine on RTX‑class. - Can you run a short validation?
Compare against a CPU FP64 baseline on a small case. - Do key metrics stay within your tolerance?
If yes, mixed/single is acceptable for that workload. - Any solver stage explicitly needs FP64?
If yes, consider hybrid runs (FP64 only where required) or pick FP64‑strong hardware.
What to test (and how)
Choose one small, representative case. Keep inputs identical across runs.
- MD (e.g., GROMACS)
Check energy drift, RMSD/RMSF, temperature/pressure stability over a short window. Mixed precision is standard in GPU builds; validate anyway. - CFD/FEM (Fluent/Mechanical/Abaqus/COMSOL)
Compare residual histories and probe values (lift/drag, displacement, stress) for a handful of iterations/time steps. - Geospatial (cuSpatial)
Verify containment counts/joins on a known subset match CPU results bit‑for‑bit; precision rarely blocks here if CRS is clean. - ABM (FLAME GPU)
Compare aggregate stats over fixed random seeds; stochastic variance should dominate, not precision. - SciML/PINNs
Compare loss curves and validation errors; mixed/FP32 often fine if you avoid underflow. - DFT/ab‑initio (CP2K/QE/VASP)
Typically requires real FP64. If you try mixed/single, expect deviations beyond tolerance.
Pass criteria (examples—set your own bands)
- MD: energy drift within your lab’s accepted bound; RMSD difference < 1–2% for the window.
- CFD/FEM: residual curves overlay; key scalar metrics within < 1%.
- Geospatial: exact match for PIP/joins on the test slice.
- SciML: validation error difference negligible vs run‑to‑run variance.
Precision by code family (rule‑of‑thumb matrix)
| Domain / examples | Typical precision path | FP64 need | GPU fit on consumer RTX |
|---|---|---|---|
| Molecular dynamics (GROMACS/AMBER/NAMD/LAMMPS) | Mixed precision GPU kernels | Low | Great (standard) |
| Docking / VS (AutoDock‑GPU, Vina‑compatible) | FP32/mixed | Low | Great |
| Geospatial (RAPIDS cuSpatial) | FP32/FP64, both common | Low | Great |
| ABM (FLAME GPU) | FP32 | Low | Great |
| CFD (Fluent) | Single/mixed on GPU | Medium | Often good (validate physics) |
| FEM/Structural (Abaqus/Standard, some Mechanical) | Mixed/single accelerates parts | Medium | Often good |
| Multiphysics (COMSOL 6.3 dG time‑explicit) | Single on GPU | Medium | Often good (specific studies) |
| DFT / ab‑initio (CP2K, QE, VASP) | FP64 throughout | High | Often poor (use FP64‑strong GPUs or CPUs) |
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Use this to pick your starting point, then validate.
Hardware implications on GPU computing
- Consumer/workstation GPUs (e.g., RTX 4090/5090)
Excellent FP32/mixed precision, limited FP64 throughput. Great for MD, docking, geospatial, ABM, many CFD/FEM/COMSOL cases. - Data‑center GPUs (A100/H100 class)
Strong FP64 and big VRAM. Use when your solver needs real FP64 or very large models. - CPUs
Always‑available FP64 and huge memory capacity; best for FP64‑only codes and big sparse solves that don’t map to your GPU path.
Pick the smallest tier that meets accuracy and wins on cost per result.
Red flags that mean “don’t drop FP64” yet
- Results diverge on mixed/single vs FP64 baseline beyond your tolerance.
- Long‑time integration drifts or blows up unless FP64 is used.
- Solvers warn: “double precision required”, “FP64 only”, or a GPU path is missing.
- Condition numbers are high and preconditioners are sensitive to rounding.
Small tricks that make mixed precision safer
- Shorter time steps (MD/CFD) within your stability rules.
- Tighter tolerances on inner solves to offset rounding.
- Iterative refinement if your linear algebra stack supports it.
- Deterministic seeds for comparison runs; document RNG.
How to report precision in Methods (copy‑paste)
hardware:
accelerator: "RTX 4090 (24 GB) | A100 80 GB | CPU only"
driver: "<NVIDIA driver>"
cuda: "<CUDA version>"
software:
solver: "<name version> (GPU: mixed | single | CPU: double)"
container: "<image>@sha256:<digest>"
validation:
baseline: "CPU FP64"
metrics:
- name: "<RMSD | residual | PIP count>"
tolerance: "<e.g., 1%>"
result_gpu: "<value>"
result_fp64: "<value>"
run:
cmd: "<exact command line>"
outputs:
wall_seconds: "<…>"
cost_per_result: "<define per domain>"
notes: "Any deviations, seeds, solver flags"
Related reading
Scientific modeling on cloud GPUs — what works, what doesn’t
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