mirror of
https://github.com/VolmitSoftware/Iris.git
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Brian Neumann-Fopiano
105 lines
4.3 KiB
Markdown
105 lines
4.3 KiB
Markdown
# Iris SIMD Kernel Benchmark
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Standalone, portable microbenchmark that measures whether Iris's SIMD (Java
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Vector API) kernels actually beat their scalar equivalents **on the CPU it runs
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on**. The Volmit dev/test machines are Apple Silicon, where the wide-SIMD path
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(4+ double lanes) does not exist. Copy the built artifact to a Windows/x86 box
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(AVX2 = 4 lanes, AVX-512 = 8 lanes) and run it to get real numbers for that CPU.
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The six kernel classes are copied verbatim (logic byte-for-byte) from
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`Iris/core/.../util/simd/`. This tool is intentionally a duplicate so it builds
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and runs on its own with no Gradle, no VolmLib, and no Iris on the classpath.
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## Requirements
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- **JDK 25** (the tool is compiled with `--release 25`).
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- The JDK must ship the `jdk.incubator.vector` incubator module (Temurin,
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Oracle, and all standard OpenJDK builds do).
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## How to run
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Windows:
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```
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run.bat
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```
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macOS / Linux:
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```
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./run.sh
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```
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The run scripts compile the sources and create the ignored `simd-bench.jar` locally when it is
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missing. Use `build.bat` or `./build.sh` to rebuild it explicitly.
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Or invoke directly (the `--add-modules` flag is required because the Vector API
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is still an incubator module):
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```
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java --add-modules jdk.incubator.vector -jar simd-bench.jar
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```
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### Mode flag
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By default it runs `both` (scalar and vector head-to-head in one run). You can
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also do a two-run A/B:
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```
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run.bat --mode scalar
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run.bat --mode vector
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run.bat --mode both (default)
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```
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`--mode=scalar` syntax works too. The correctness cross-check only runs in
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`both` mode (it needs both implementations).
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## How to read the output
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- **Header** prints the JVM, `os.arch`, CPU count, and the preferred vector
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width. `DoubleVector pref: N lanes` is the SIMD width for `double` on this CPU
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(2 on 128-bit NEON, 4 on AVX2, 8 on AVX-512).
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- **noise SIMD gate (aligned && doubleLanes>=4)** mirrors the real profitability
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gate in Iris's `VectorNoiseKernels2D`. It reports ENABLED on 4+ lane CPUs and
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DISABLED on 2-lane NEON. **This tool ignores the gate and force-measures the
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raw vector kernel anyway**, so you see the real number even where Iris would
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gate SIMD off.
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- **Correctness cross-check** runs each kernel once with both impls on identical
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input and confirms they agree (roundToInt/noise are bit-exact; `sum` differs
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only by floating-point reduction order, checked with a relative tolerance).
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If anything says MISMATCH, do not trust the timing numbers below it.
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- **speedup = scalar ns/op / vector ns/op.** `> 1.0` means SIMD is faster on
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this CPU; `< 1.0` means SIMD is slower. Verdict column: SIMD FASTER / SIMD
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SLOWER / NEUTRAL (within ~5%).
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- `ns/op` for the array kernels is one full-array invocation (256 or 1024
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doubles). For noise it is one 256-element `simplexFractalFBM` call.
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- The trailing **Checksum** lines exist only to keep the JIT from deleting the
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measured work. Ignore their values.
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## Harness notes (why the numbers are trustworthy)
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- Each op is warmed up 50,000 times before timing so the JIT has compiled the
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hot path.
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- Every kernel output is folded into a running checksum (printed at the end) so
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no measured call is dead-code-eliminated.
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- One input element is perturbed per iteration (a cheap store keyed off the loop
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counter) so the JIT cannot hoist a "constant" result out of the timing loop.
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This perturbation is identical for scalar and vector, so the comparison stays
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fair; it adds a tiny fixed cost to both sides that very slightly compresses the
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ratio on the cheapest kernels.
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- Each (kernel, impl) is timed over 10 rounds; the **minimum** ns/op is reported
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(least-noisy statistic for a microbenchmark).
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- Scalar and vector see the same seeded input for a given kernel.
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## Caveats
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- This is an **isolated-kernel vacuum microbench**, not full worldgen. It says
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whether the raw kernel is faster on this CPU, not what end-to-end generation
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throughput will be (cache behavior, allocation, and surrounding code differ in
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the real engine).
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- **Effort 1 array kernels** (`roundToInt` / `sum` / `max`) run unconditionally
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in real Iris. **Effort 2 noise** (`simplexFractalFBM`) is currently unwired in
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Iris and gated to 4+ double lanes; this tool force-measures it regardless.
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- Vector-API auto-vectorization and cost depend heavily on the JDK version and
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CPU. Run on the actual target hardware; do not extrapolate across machines.
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