MultiOutput

Price several named risk measures from one recorded tape and one compiled kernel: the tape carries one rec.output(name, ...) per measure, and a single replay runs the forward sweep once and one reverse sweep per measure, returning every value, its Monte-Carlo standard error, and its full input gradient — the N × M Jacobian — all from the same Philox paths.

try (MultiOutput mo = MultiOutput.of(rec -> {
        SDouble s0 = rec.input("S0", 100), k = rec.input("K", 100),
                vol = rec.input("sigma", 0.2), r = rec.input("r", 0.03);
        SDouble sT = s0.mul(r.sub(vol.mul(vol).mul(0.5)).add(vol.mul(rec.randn())).exp());
        return Map.of(
            "call",    sT.sub(k).max(0.0).mul(r.neg().exp()),
            "digital", com.nablatensor.ops.Smooth.gt(rec, sT, k, 1.0).mul(r.neg().exp()));
      }).on("cpu-jit").build()) {

  MultiOutput.Result res = mo.run(1_000_000, 42L);
  res.value("digital");                 // digital price
  res.gradient("call").get("sigma");    // call vega
}