hotSpring — Computational Plasma Physics, Lattice QCD, Spectral Theory
Dense plasmas, nuclear structure, lattice QCD — 697+ tests on consumer GPUs for $0.044. Paper parity on a $600 card.
Domain
Dense plasmas, nuclear structure, molecular dynamics, lattice QCD, spectral theory, neuromorphic computing.
Repository: syntheticChemistry/hotSpring
The Science Story
hotSpring is the primary GPU science driver — the spring that proves barraCuda can do first-principles computational physics on consumer hardware. It is the most mature spring because physics has the least room to hide: 0.000% energy drift or the simulation is wrong.
Headline Results
- Sarkas Yukawa MD at paper parity (N=10,000, 80k steps) on a $600 RTX 4070 for $0.044 in electricity
- Full AME2020 nuclear dataset (2,042 nuclei — 39× the published paper) on a single consumer GPU
- Lattice QCD β-scans (32⁴, 12 temperatures) resolving the deconfinement transition on a $500 RTX 3090 for $0.58
- DF64 delivers 3.24 TFLOPS of double precision on FP32 cores
- Phase 0 discovered and fixed 5 silent bugs in the upstream Sarkas code
Validation Phases
| Phase | Key Result |
|---|---|
| A–E (MD) | Python → Rust → GPU → f64 → paper parity. 0.000% energy drift. $0.044 electricity |
| F (Nuclear EOS) | 2,042 nuclei AME2020 on consumer GPU. 478× speedup, 44.8× energy reduction |
| Lattice QCD | SU(3) HMC + dynamical fermions. 32⁴ β-scan, deconfinement at β=5.69 |
| Spectral | Anderson localization (1D/2D/3D), Hofstadter butterfly, Lanczos eigensolver |
| NPU | 10 SDK assumptions overturned. ESN streaming at 2.8μs/step |
Researchers Reproduced
| Researcher | Department | Domain |
|---|---|---|
| Michael Murillo | CMSE, MSU | Dense plasmas, WDM, molecular dynamics |
| Alexei Bazavov | CMSE + Physics, MSU | Lattice QCD, thermodynamics |
| Ilya Kachkovskiy | Math, MSU | Spectral theory, Anderson localization |
| Rika Anderson | Biology, Carleton | Pangenomics (cross-spring) |
What the Constraint Revealed
Eliminating CUDA forced Vulkan, which exposed SHADER_F64 on consumer GPUs. Eliminating vendor compilers forced coralReef, which now compiles 93/93 cross-spring WGSL shaders to native GPU binaries. A $300 Akida NPU runs ESN inference at 2.8μs/step — 1,000× faster than GPU for streaming workloads, 9,017× less energy for transport predictions.
Cross-Spring Connections
- → airSpring: f64 GPU dispatch batching pattern
- → wetSpring: FusedMapReduceF64 pattern for bulk statistics; Anderson localization shared primitives
- → ToadStool: 195 acceptance checks, 6 bugs found
- → neuralSpring: isomorphic GEMM serves plasma and nuclear
- → groundSpring: spectral primitives + QCD inverse problems
baseCamp Papers
Papers 07, 10, 15, 25 — see baseCamp Science for full list.