nvrl/ember-tune-rs
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implemented multiple fans

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This commit is contained in:
Nils Pukropp
2026-02-26 14:06:49 +01:00
parent 7e2bef58d2
commit cab39a6478
10 changed files with 101 additions and 44 deletions
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39
src/engine/mod.rs
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@@ -69,8 +69,8 @@ impl OptimizerEngine {
.unwrap_or(0.0)
}
/// Finds the "Silicon Knee" - the point where performance per watt plateaus
/// and thermal density spikes.
/// Finds the "Silicon Knee" - the point where performance per watt (efficiency)
/// starts to diminish significantly and thermal density spikes.
pub fn find_silicon_knee(&self, profile: &ThermalProfile) -> f32 {
if profile.points.len() < 3 {
return profile.points.last().map(|p| p.power_w).unwrap_or(15.0);
@@ -82,27 +82,42 @@ impl OptimizerEngine {
let mut best_pl = points[0].power_w;
let mut max_score = f32::MIN;
// Use a sliding window (3 points) to calculate gradients more robustly
for i in 1..points.len() - 1 {
let prev = &points[i - 1];
let curr = &points[i];
let next = &points[i + 1];
// 1. Performance Gradient (dMHz/dW)
let dmhz_dw_prev = (curr.freq_mhz - prev.freq_mhz) / (curr.power_w - prev.power_w).max(0.1);
let dmhz_dw_next = (next.freq_mhz - curr.freq_mhz) / (next.power_w - curr.power_w).max(0.1);
let freq_diminish = dmhz_dw_prev - dmhz_dw_next;
// 1. Efficiency Metric (Throughput per Watt)
// If throughput is 0 (unsupported), fallback to Frequency per Watt
let efficiency_curr = if curr.throughput > 0.0 {
curr.throughput as f32 / curr.power_w.max(0.1)
} else {
curr.freq_mhz / curr.power_w.max(0.1)
};
let efficiency_next = if next.throughput > 0.0 {
next.throughput as f32 / next.power_w.max(0.1)
} else {
next.freq_mhz / next.power_w.max(0.1)
};
// 2. Thermal Gradient (d2T/dW2)
// Diminishing returns: how much efficiency drops per additional watt
let efficiency_drop = (efficiency_curr - efficiency_next) / (next.power_w - curr.power_w).max(0.1);
// 2. Thermal Acceleration (d2T/dW2)
let dt_dw_prev = (curr.temp_c - prev.temp_c) / (curr.power_w - prev.power_w).max(0.1);
let dt_dw_next = (next.temp_c - curr.temp_c) / (next.power_w - curr.power_w).max(0.1);
let temp_accel = (dt_dw_next - dt_dw_prev) / (next.power_w - prev.power_w).max(0.1);
// 3. Wall Detection
let is_throttling = next.freq_mhz < curr.freq_mhz;
let penalty = if is_throttling { 2000.0 } else { 0.0 };
// 3. Wall Detection (Any drop in absolute frequency/throughput is a hard wall)
let is_throttling = next.freq_mhz < curr.freq_mhz || (next.throughput > 0.0 && next.throughput < curr.throughput);
let penalty = if is_throttling { 5000.0 } else { 0.0 };
// Heuristic scoring: Weight thermal acceleration and diminishing frequency gains
let score = (freq_diminish * 2.0) + (temp_accel * 10.0) - penalty;
// Heuristic scoring:
// - Higher score is "Better" (The Knee is the peak of this curve)
// - We want high efficiency (low drop) and low thermal acceleration.
let score = (efficiency_curr * 10.0) - (efficiency_drop * 50.0) - (temp_accel * 20.0) - penalty;
if score > max_score {
max_score = score;