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perf(engine_multiscale): performance enhancments due to improved hashing, locality, and data structure optimization

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This particular commit speeds up QSE solving for systems where reverse reactions and engine caching is disabled by about 24%
This commit is contained in:
Emily Boudreaux
2025-10-22 15:08:49 -04:00
parent b20d3467fa
commit 0581f69c48
6 changed files with 41 additions and 47 deletions
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9
src/lib/engine/engine_graph.cpp
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@@ -1312,11 +1312,9 @@ namespace gridfire {
if ( p != 0) { return false; }
const double T9 = tx[0];
// This is an interesting problem because the reverse rate should only ever be computed for strong reactions
// Which do not depend on rho or Y. However, the signature requires them...
// For now, we just pass dummy values for rho and Y
// We can pass a dummy comp and rho because reverse rates should only be calculated for strong reactions whose
// rates of progression do not depend on composition or density.
const double reverseRate = m_engine.calculateReverseRate(m_reaction, T9, 0.0, {});
// std::cout << m_reaction.peName() << " reverseRate: " << reverseRate << " at T9: " << T9 << "\n";
ty[0] = reverseRate; // Store the reverse rate in the output vector
if (vx.size() > 0) {
@@ -1335,9 +1333,6 @@ namespace gridfire {
const double T9 = tx[0];
const double reverseRate = ty[0];
// This is an interesting problem because the reverse rate should only ever be computed for strong reactions
// Which do not depend on rho or Y. However, the signature requires them...
// For now, we just pass dummy values for rho and Y
const double derivative = m_engine.calculateReverseRateTwoBodyDerivative(m_reaction, T9, 0, {}, reverseRate);
px[0] = py[0] * derivative; // Return the derivative of the reverse rate with respect to T9