the viewer shows us a single xy cross-section of the simulation at a moment in time.
it uses red-tipped arrows to show the x-y components of the B field at every point,
and the Z component of the E field is illustrated with color (bright green for positive polarization and dark blue for negative).
the light blue splotches depict the conductors (in the center, the wire coupling loops; on the edge, our energy-dissipating boundary).
what we see here is that both ferrites (the two large circles in the above image) have a clockwise polarized B field. this is in the middle of a transition, so the E fields look a bit chaotic. advance to t=46 ns: the "reset" pulse was applied at t=24ns and had 22ns to settle:
we can see the "reset" pulse has polarized both ferrites in the CCW orientation this time. the E field is less pronounced because we gave the system 22ns instead of 3ns to settle this time.
the graphical viewer is helpful for debugging geometries, but the CSV measurements are useful for viewing numeric system performance. peak inside "out/examples/sr-latch/meas.csv" to see a bunch of measurements over time. you can use a tool like Excel or [visidata](https://www.visidata.org/) to plot the interesting ones.
here's a plot of `M(mem2)` over time from the SR latch simulation. we're measuring the (average) M value along the major tangent to the torus corresponding to the ferrite on the right in the images above. the notable bumps correspond to these pulses: "set", "reset", "set", "reset", "set+reset applied simultaneously", "reset", "reset".
2. apply the FDTD update equations to "step" the E field, and then "step" the H field. these equations take the external stimulus from step 1 into account.
3. evaluate all the measurement functions over the current state; write these to disk.
4. serialize the current state to disk so that we can resume from this point later if we choose.
within each step above, the logic is multi-threaded and the rendeveous points lie at the step boundaries.
it turns out that the Courant rules force us to evaluate FDTD updates (step 2) on a _far_ smaller time scale than the other steps are sensitive to. so to tune for performance, we apply some optimizations here universally:
- stimuli (step 1) are evaluated only once every N frames (tunable). we still *apply* them on each frame individually. the waveform resembles that of a Sample & Hold circuit.
- measurement functions (step 3) are triggered only once every M frames.
- the state is serialized (step 4) only once every Z frames.
as a result, step 2 is actually able to apply the FDTD update functions not just once but up to `min(N, M, Z)` times.
although steps 1 and 3 vary heavily based on the user configuration of the simulation, step 2 can be defined pretty narrowly in code (no user-callbacks/dynamic function calls/etc). this lets us offload the processing of step 2 to a dedicated GPU. by tuning N/M/Z, step 2 becomes the dominant cost in our simulations an GPU offloading can trivially boost performance by more than an order of magnitude on even a mid-range consumer GPU.
this library takes effort to separate the following from the core/math-heavy "simulation" logic:
- Stimuli
- Measurements
- Render targets (video, CSV, etc)
- Materials (conductors, non-linear ferromagnets)
- Float implementation (for CPU simulations only)
the simulation only interacts with these things through a trait interface, such that they're each swappable.
common stimuli type live in [src/stim.rs](src/stim.rs).
common measurements live in [src/meas.rs](src/meas.rs).
common render targets live in [src/render.rs](src/render.rs). these change infrequently enough that [src/driver.rs](src/driver.rs) has some specialized helpers for each render backend.
common materials are spread throughout [src/mat](src/mat/mod.rs).
different float implementations live in [src/real](src/real.rs).
if you're getting NaNs, you can run the entire simulation on a checked `R64` type in order to pinpoint the moment those are introduced.
## Materials
of these, the materials have the most "gotchas".
each cell owns an associated material instance.
in the original CPU implementation of this library, each cell had a `E` and `H` component,
and any additional state was required to be held in the material. so a conductor material
might hold only some immutable `conductivity` parameter, while a ferromagnetic material
might hold similar immutable material parameters _and also a mutable `M` field_.
spirv/rust-gpu requires stronger separation of state, and so this `M` field had to be lifted
completely out of the material. as a result, the material API differs slightly between the CPU
and spirv backends. as you saw in the examples, that difference doesn't have to appear at the user
level, but you will see it if you're adding new materials.
### Spirv Materials
all the materials usable in the spirv backend live in `src/sim/spirv/spirv_backend/src/mat.rs`.
to add a new one, implement the `Material` trait in that file on some new type, which must also
be in that file.
next, add an analog type somewhere in the main library, like `src/mat/mh_ferromagnet.rs`. this will
be the user-facing material.
now implement the `IntoFfi` and `IntoLib` traits for this new material inside `src/sim/spirv/bindings.rs`
so that the spirv backend can translate between its GPU-side material and your CPU-side/user-facing material.
finally, because cpu-side `SpirvSim<M>` is parameterized over a material, but the underlying spirv library
is compiled separately, the spirv library needs specialized dispatch logic for each value of `M` you might want
to use. add this to `src/sim/spirv/spirv_backend/src/lib.rs` (it's about five lines: follow the example of `Iso3R1`).
adding a CPU material is "simpler". just implement the `Material` trait in `src/mat/mod.rs`.
either link that material into the `GenericMaterial` type in the same file (if you want to easily
mix materials within the same simulation), or if that material can handle every cell in your
simulation then instantiance a `SimState<M>` object which is directly parameterized over your material.
## What's in the Box
this library ships with the following materials:
- conductors (Isomorphic or Anisomorphic). supports CPU or GPU.
- linear magnets (defined by their relative permeability, mu\_r). supports CPU only.
- a handful of ferromagnet implementations:
-`MHPgram` specifies the `M(H)` function as a parallelogram. supports CPU or GPU.
-`MBPgram` specifies the `M(B)` function as a parallelogram. supports CPU or GPU.
-`MHCurve` specifies the `M(H)` function as an arbitrary polygon. requires a new type for each curve for memory reasons (see `Ferroxcube3R1`). supports CPU only.
measurements include ([src/meas.rs](src/meas.rs)):
- E, B or H field (mean vector over some region)
- energy, power (net over some region)
- current (mean vector over some region)
- mean current magnitude along a closed loop (toroidal loops only)
- mean magnetic polarization magnitude along a closed loop (toroidal loops only)
output targets include ([src/render.rs](src/render.rs)):
-`ColorTermRenderer`: renders 2d-slices in real-time to the terminal.
-`Y4MRenderer`: outputs 2d-slices to an uncompressed `y4m` video file.
-`SerializerRenderer`: dumps the full 3d simulation state to disk. parseable after the fact with [src/bin/viewer.rs](src/bin/viewer.rs).
-`CsvRenderer`: dumps the output of all measurements into a `csv` file.
historically there was also a plotly renderer, but that effort was redirected into developing the `src/bin/viewer.rs` tool better.
with my Radeon RX 5700XT, the sr\_latch example takes 125 minutes to complete 150ns of simulation time (3896500 simulation steps). that's on a grid of size 163x126x80 where the cell dimension is 20um.
in a FDTD simulation, as we shrink the cell size the time step has to shrink too (it's an inverse affine relationship). so the scale-invariant performance metric is "grid cell steps per second" (`(163*126*80)*3896500 / (125*60)`): we get 850M.
this is the "default" optimized version. you could introduce a new material to the simulation, and performance would remain constant. as you finalize your simulation, you can specialize it a bit and compile the GPU code to optimize for your specific material. this can squeeze another factor-of-2 gain: view <examples/buffer_proto5.rs> to see how that's done.
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do note that the individual dependencies of this software project include licenses of their own. for your convenience, i've annotated each dependency inside [Cargo.toml](Cargo.toml) with its respective license.
