Compare commits

..

20 Commits
v8.12 ... v8.21

Author SHA1 Message Date
Mikhail Chusavitin
18e24a9aa5 Estimate fan duty from observed RPM maxima 2026-04-16 10:10:18 +03:00
Mikhail Chusavitin
e306250da7 Disable fp64/fp4 in mixed gpu burn 2026-04-16 10:00:03 +03:00
Mikhail Chusavitin
c5b2081ac9 Disable unstable fp4/fp64 benchmark phases 2026-04-16 09:58:02 +03:00
434528083e Power bench: compare GPU-reported TDP vs IPMI server power delta
- NvidiaPowerBenchResult gains ServerPower *BenchmarkServerPower
- RunNvidiaPowerBench samples IPMI idle before Phase 1 and loaded via
  background goroutine throughout Phase 2 ramp
- renderPowerBenchReport: new "Server vs GPU Power Comparison" table
  with ratio annotation (✓ match / ⚠ minor / ✗ over-report)
- renderPowerBenchSummary: server_idle_w, server_loaded_w, server_delta_w,
  server_reporting_ratio keys

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 07:21:02 +03:00
30aa30cd67 LiveCD: set Baby Bee wallpaper centered on black background
400×400px PNG centered via feh --bg-center --image-bg '#000000'.
Fallback solid fill also changed to black.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:57:23 +03:00
4f76e1de21 Dashboard: per-device status chips with hover tooltips
Replace single aggregated badge per hardware category with individual
colored chips (O/W/F/?) for each ComponentStatusRecord. Added helper
functions: matchedRecords, firstNonEmpty. CSS classes: chip-ok/warn/fail/unknown.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:54:13 +03:00
3732e64a4a Add slowdown temperature exceedance detector to benchmark
detectSlowdownTempExceedance scans steady-state metric rows per GPU and
emits a [WARNING] note + PARTIAL status if any sample >= SlowdownTempC.
Uses per-GPU threshold from nvidia-smi -q, fallback 80°C.

Distinct from p95-based TempHeadroomC check: catches even a single spike
above the slowdown threshold that would be smoothed out in aggregates.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:46:45 +03:00
0d925299ff Use per-GPU temperature limits from nvidia-smi -q for headroom calculation
Parse "GPU Shutdown Temp" and "GPU Slowdown Temp" from nvidia-smi -q verbose
output in enrichGPUInfoWithMaxClocks. Store as ShutdownTempC/SlowdownTempC
on benchmarkGPUInfo and BenchmarkGPUResult. Fallback: 90°C shutdown / 80°C
slowdown when not available.

TempHeadroomC = ShutdownTempC - P95TempC (per-GPU, not hardcoded 100°C).
Warning threshold: p95 >= SlowdownTempC. Critical: headroom < 10°C.
Report table shows both limits alongside headroom and p95 temp.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:45:15 +03:00
a8d5e019a5 Translate report to English; add power anomaly detector
All report strings are now English only.

Add detectPowerAnomaly: scans steady-state metric rows per GPU with a
5-sample rolling baseline; flags a sudden drop ≥30% while GPU usage >50%
as [HARD STOP] — indicates bad cable contact or VRM fault.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:42:00 +03:00
72ec086568 Restructure benchmark report as balanced scorecard (5 perspectives)
Split throttle into separate signals: ThermalThrottlePct, PowerCapThrottlePct,
SyncBoostThrottlePct. Add TempHeadroomC (100 - p95_temp) as independent
thermal headroom metric; warning < 20°C (>80°C), critical < 10°C (>90°C).

Hard stop findings: thermal throttle with fans < 95%, ECC uncorrected errors,
p95 temp > 90°C. Throttle findings now include per-type percentages and
diagnostic context.

Replace flat scorecard table with BSC 5-perspective layout:
1. Compatibility (hard stops: thermal+fan, ECC)
2. Thermal headroom (p95 temp, delta to 100°C, throttle %)
3. Power delivery (power cap throttle, power CV, fan duty)
4. Performance (Compute TOPS, Synthetic, Mixed, TOPS/SM/GHz)
5. Anomalies (ECC corrected, sync boost, power/thermal variance)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 06:40:06 +03:00
7a0b0934df Separate compute score from server quality score
CompositeScore = raw ComputeScore (TOPS). Throttling GPUs score lower
automatically — no quality multiplier distorting the compute signal.

Add ServerQualityScore (0-100): server infrastructure quality independent
of GPU model. Formula: 0.40×Stability + 0.30×PowerSustain + 0.30×Thermal.
Use to compare servers with the same GPU or flag bad server conditions.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 00:45:55 +03:00
d8ca0dca2c Redesign scoring metrics: variance-based sustain scores, throttle stability
PowerSustainScore: power draw variance (CV) during load, not deviation from TDP.
ThermalSustainScore: temperature variance (CV) during load.
StabilityScore: fraction of time spent in thermal+power-cap throttling.
Remove NCCL bonus from quality_factor.

quality = 0.35 + 0.35×Stability + 0.15×PowerSustain + 0.15×ThermalSustain, cap 1.00.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 00:39:59 +03:00
d90250f80a Fix DCGM cleanup and shorten memory validate 2026-04-16 00:39:37 +03:00
8d6eaef5de Update perf benchmark report methodology to reflect new design
Remove references to pre-benchmark power calibration and dcgmi
targeted_power. Document platform_power_score ramp-up methodology,
PowerSustainScore fallback to steady-state power, and full-budget
single-precision phases.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 00:31:58 +03:00
732bf4cbab Redesign power and performance benchmarks with new methodology
Power/Thermal Fit: cumulative fixed-limit ramp where each GPU's stable TDP
is found under real multi-GPU thermal load (all prior GPUs running at their
fixed limits). PlatformMaxTDPW = sum of stable limits across all GPUs.
Remove PlatformPowerScore from power test.

Performance Benchmark: remove pre-benchmark power calibration entirely.
After N single-card runs, execute k=2..N parallel ramp-up steps and compute
PlatformPowerScore = mean compute scalability vs best single-card TOPS.
PowerSustainScore falls back to Steady.AvgPowerW when calibration absent.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-16 00:30:50 +03:00
fa6d905a10 Tune bee-gpu-burn single-precision benchmark phases 2026-04-16 00:05:47 +03:00
Mikhail Chusavitin
5c1862ce4c Use lb clean --all to clear bootstrap cache on every build
Prevents stale debootstrap cache from bypassing --debootstrap-options
changes (e.g. --include=ca-certificates added in v8.15).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-15 17:37:08 +03:00
Mikhail Chusavitin
b65ef2ea1d Fix: use --debootstrap-options to include ca-certificates in bootstrap
--bootstrap-packages is not a valid lb config option (20230502).
Use --debootstrap-options "--include=ca-certificates" instead to ensure
ca-certificates is present when lb chroot_archives runs apt-get update
against the NVIDIA CUDA HTTPS source.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-15 17:26:01 +03:00
Mikhail Chusavitin
533d703c97 Bootstrap ca-certificates so NVIDIA CUDA HTTPS source is trusted
debootstrap creates a minimal chroot without ca-certificates, causing
apt-get update to fail TLS verification for the NVIDIA CUDA apt source:
  "No system certificates available. Try installing ca-certificates."
Add ca-certificates to --bootstrap-packages so it is present before
lb chroot_archives configures the NVIDIA HTTPS source and runs apt-get update.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-15 17:24:20 +03:00
Mikhail Chusavitin
04eb4b5a6d Revert "Pre-download DCGM/fabricmanager debs on host to bypass chroot apt"
This reverts commit 4110dbf8a6.
2026-04-15 17:19:53 +03:00
18 changed files with 1557 additions and 565 deletions

File diff suppressed because it is too large Load Diff

View File

@@ -61,6 +61,9 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
if result.ScalabilityScore > 0 {
fmt.Fprintf(&b, "**Scalability score:** %.1f%% \n", result.ScalabilityScore)
}
if result.PlatformPowerScore > 0 {
fmt.Fprintf(&b, "**Platform power score:** %.1f%% \n", result.PlatformPowerScore)
}
fmt.Fprintf(&b, "**Overall status:** %s \n", result.OverallStatus)
b.WriteString("\n")
@@ -81,41 +84,92 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
b.WriteString("\n")
}
// ── Methodology ───────────────────────────────────────────────────────────
b.WriteString("## Methodology\n\n")
fmt.Fprintf(&b, "- Profile `%s` uses standardized baseline -> warmup -> steady-state -> interconnect phases.\n", result.BenchmarkProfile)
b.WriteString("- Single-GPU compute score comes from `bee-gpu-burn` on the cuBLASLt path when available.\n")
b.WriteString("- Thermal and power limits are inferred from NVIDIA clock-event counters plus sustained telemetry.\n")
b.WriteString("- `result.json` is the canonical machine-readable source for the run.\n\n")
b.WriteString("**Compute score** is derived from two phases:\n\n")
b.WriteString("- **Synthetic** — each precision type (int8, fp8, fp16, fp32, fp64, fp4) runs alone for a dedicated window. ")
b.WriteString("Measures peak throughput with the full GPU dedicated to one kernel type. ")
b.WriteString("Each result is normalised to fp32-equivalent TOPS using precision weights: ")
b.WriteString("fp64 ×2.0 · fp32 ×1.0 · fp16 ×0.5 · int8 ×0.25 · fp8 ×0.25 · fp4 ×0.125.\n")
b.WriteString("- **Mixed** — all precision types run simultaneously (combined phase). ")
b.WriteString("Reflects real inference workloads where fp8 matrix ops, fp16 attention and fp32 accumulation compete for bandwidth and SM scheduler slots.\n\n")
b.WriteString("**Formula:** `Compute = Synthetic × (1 + MixedEfficiency × 0.3)`\n\n")
b.WriteString("where `MixedEfficiency = Mixed / Synthetic`. A GPU that sustains 90 % throughput under mixed load ")
b.WriteString("receives a +27 % bonus over its synthetic score; one that drops to 60 % receives +18 %.\n\n")
b.WriteString("**Composite score** = `Compute × quality_factor` where quality factors in power sustain, thermal sustain, stability, and interconnect.\n\n")
// ── Balanced Scorecard ────────────────────────────────────────────────────
b.WriteString("## Balanced Scorecard\n\n")
// ── Scorecard table ───────────────────────────────────────────────────────
b.WriteString("## Scorecard\n\n")
b.WriteString("| GPU | Status | Composite | Compute | Synthetic | Mixed | Mixed Eff. | TOPS/SM/GHz | Power Sustain | Thermal Sustain | Stability | Interconnect |\n")
b.WriteString("|-----|--------|-----------|---------|-----------|-------|------------|-------------|---------------|-----------------|-----------|-------------|\n")
// Perspective 1: Compatibility — hard stops
b.WriteString("### 1. Compatibility\n\n")
b.WriteString("| GPU | Thermal throttle | Fan duty at throttle | ECC uncorr | Status |\n")
b.WriteString("|-----|------------------|----------------------|------------|--------|\n")
for _, gpu := range result.GPUs {
name := strings.TrimSpace(gpu.Name)
if name == "" {
name = "Unknown GPU"
thermalThrottle := "-"
if gpu.Scores.ThermalThrottlePct > 0 {
thermalThrottle = fmt.Sprintf("%.1f%%", gpu.Scores.ThermalThrottlePct)
}
interconnect := "-"
if gpu.Scores.InterconnectScore > 0 {
interconnect = fmt.Sprintf("%.1f", gpu.Scores.InterconnectScore)
fanAtThrottle := "-"
if result.Cooling != nil && result.Cooling.FanDutyCycleAvailable && gpu.Scores.ThermalThrottlePct > 0 {
fanAtThrottle = fmt.Sprintf("%.0f%%", result.Cooling.P95FanDutyCyclePct)
}
topsPerSM := "-"
if gpu.Scores.TOPSPerSMPerGHz > 0 {
topsPerSM = fmt.Sprintf("%.3f", gpu.Scores.TOPSPerSMPerGHz)
ecc := "-"
if gpu.ECC.Uncorrected > 0 {
ecc = fmt.Sprintf("⛔ %d", gpu.ECC.Uncorrected)
}
compatStatus := "✓ OK"
if gpu.ECC.Uncorrected > 0 || (gpu.Scores.ThermalThrottlePct > 0 && result.Cooling != nil && result.Cooling.FanDutyCycleAvailable && result.Cooling.P95FanDutyCyclePct < 95) {
compatStatus = "⛔ HARD STOP"
}
fmt.Fprintf(&b, "| GPU %d | %s | %s | %s | %s |\n",
gpu.Index, thermalThrottle, fanAtThrottle, ecc, compatStatus)
}
b.WriteString("\n")
// Perspective 2: Thermal headroom
b.WriteString("### 2. Thermal Headroom\n\n")
b.WriteString("| GPU | p95 temp | Slowdown limit | Shutdown limit | Headroom | Thermal throttle | Status |\n")
b.WriteString("|-----|----------|----------------|----------------|----------|------------------|--------|\n")
for _, gpu := range result.GPUs {
shutdownTemp := gpu.ShutdownTempC
if shutdownTemp <= 0 {
shutdownTemp = 90
}
slowdownTemp := gpu.SlowdownTempC
if slowdownTemp <= 0 {
slowdownTemp = 80
}
headroom := gpu.Scores.TempHeadroomC
thermalStatus := "✓ OK"
switch {
case headroom < 10:
thermalStatus = "⛔ CRITICAL"
case gpu.Steady.P95TempC >= slowdownTemp:
thermalStatus = "⚠ WARNING"
}
throttlePct := "-"
if gpu.Scores.ThermalThrottlePct > 0 {
throttlePct = fmt.Sprintf("%.1f%%", gpu.Scores.ThermalThrottlePct)
}
fmt.Fprintf(&b, "| GPU %d | %.1f°C | %.0f°C | %.0f°C | %.1f°C | %s | %s |\n",
gpu.Index, gpu.Steady.P95TempC, slowdownTemp, shutdownTemp, headroom, throttlePct, thermalStatus)
}
b.WriteString("\n")
// Perspective 3: Power delivery
b.WriteString("### 3. Power Delivery\n\n")
b.WriteString("| GPU | Power cap throttle | Power stability | Fan duty (p95) | Status |\n")
b.WriteString("|-----|-------------------|-----------------|----------------|--------|\n")
for _, gpu := range result.GPUs {
powerCap := "-"
if gpu.Scores.PowerCapThrottlePct > 0 {
powerCap = fmt.Sprintf("%.1f%%", gpu.Scores.PowerCapThrottlePct)
}
fanDuty := "-"
if result.Cooling != nil && result.Cooling.FanDutyCycleAvailable {
fanDuty = fmt.Sprintf("%.0f%%", result.Cooling.P95FanDutyCyclePct)
}
powerStatus := "✓ OK"
if gpu.Scores.PowerCapThrottlePct > 5 {
powerStatus = "⚠ POWER LIMITED"
}
fmt.Fprintf(&b, "| GPU %d | %s | %.1f | %s | %s |\n",
gpu.Index, powerCap, gpu.Scores.PowerSustainScore, fanDuty, powerStatus)
}
b.WriteString("\n")
// Perspective 4: Performance
b.WriteString("### 4. Performance\n\n")
b.WriteString("| GPU | Compute TOPS | Synthetic | Mixed | Mixed Eff. | TOPS/SM/GHz |\n")
b.WriteString("|-----|--------------|-----------|-------|------------|-------------|\n")
for _, gpu := range result.GPUs {
synthetic := "-"
if gpu.Scores.SyntheticScore > 0 {
synthetic = fmt.Sprintf("%.2f", gpu.Scores.SyntheticScore)
@@ -128,20 +182,41 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
if gpu.Scores.MixedEfficiency > 0 {
mixedEff = fmt.Sprintf("%.1f%%", gpu.Scores.MixedEfficiency*100)
}
fmt.Fprintf(&b, "| GPU %d %s | %s | **%.2f** | %.2f | %s | %s | %s | %s | %.1f | %.1f | %.1f | %s |\n",
gpu.Index, name,
gpu.Status,
gpu.Scores.CompositeScore,
gpu.Scores.ComputeScore,
synthetic,
mixed,
mixedEff,
topsPerSM,
gpu.Scores.PowerSustainScore,
gpu.Scores.ThermalSustainScore,
gpu.Scores.StabilityScore,
interconnect,
)
topsPerSM := "-"
if gpu.Scores.TOPSPerSMPerGHz > 0 {
topsPerSM = fmt.Sprintf("%.3f", gpu.Scores.TOPSPerSMPerGHz)
}
fmt.Fprintf(&b, "| GPU %d | **%.2f** | %s | %s | %s | %s |\n",
gpu.Index, gpu.Scores.CompositeScore, synthetic, mixed, mixedEff, topsPerSM)
}
if len(result.PerformanceRampSteps) > 0 {
fmt.Fprintf(&b, "\n**Platform power score (scalability):** %.1f%%\n", result.PlatformPowerScore)
}
b.WriteString("\n")
// Perspective 5: Anomaly flags
b.WriteString("### 5. Anomalies\n\n")
b.WriteString("| GPU | ECC corrected | Sync boost throttle | Power instability | Thermal instability |\n")
b.WriteString("|-----|---------------|---------------------|-------------------|---------------------|\n")
for _, gpu := range result.GPUs {
eccCorr := "-"
if gpu.ECC.Corrected > 0 {
eccCorr = fmt.Sprintf("⚠ %d", gpu.ECC.Corrected)
}
syncBoost := "-"
if gpu.Scores.SyncBoostThrottlePct > 0 {
syncBoost = fmt.Sprintf("%.1f%%", gpu.Scores.SyncBoostThrottlePct)
}
powerVar := "OK"
if gpu.Scores.PowerSustainScore < 70 {
powerVar = "⚠ unstable"
}
thermalVar := "OK"
if gpu.Scores.ThermalSustainScore < 70 {
thermalVar = "⚠ unstable"
}
fmt.Fprintf(&b, "| GPU %d | %s | %s | %s | %s |\n",
gpu.Index, eccCorr, syncBoost, powerVar, thermalVar)
}
b.WriteString("\n")
@@ -171,13 +246,13 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
fmt.Fprintf(&b, "- **Power limit:** %.0f W (default %.0f W)\n", gpu.PowerLimitW, gpu.DefaultPowerLimitW)
}
if gpu.PowerLimitDerated {
fmt.Fprintf(&b, "- **Power limit derating:** active after %d targeted_power attempt(s)\n", gpu.PowerCalibrationTries)
fmt.Fprintf(&b, "- **Power limit derating:** active (reduced limit %.0f W)\n", gpu.PowerLimitW)
}
if gpu.CalibratedPeakPowerW > 0 {
if gpu.CalibratedPeakTempC > 0 {
fmt.Fprintf(&b, "- **Power calibration (`dcgmi targeted_power`):** %.0f W p95 at %.1f °C p95\n", gpu.CalibratedPeakPowerW, gpu.CalibratedPeakTempC)
fmt.Fprintf(&b, "- **Calibrated peak power:** %.0f W p95 at %.1f °C p95\n", gpu.CalibratedPeakPowerW, gpu.CalibratedPeakTempC)
} else {
fmt.Fprintf(&b, "- **Power calibration (`dcgmi targeted_power`):** %.0f W p95\n", gpu.CalibratedPeakPowerW)
fmt.Fprintf(&b, "- **Calibrated peak power:** %.0f W p95\n", gpu.CalibratedPeakPowerW)
}
}
if gpu.LockedGraphicsClockMHz > 0 {
@@ -186,14 +261,18 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
b.WriteString("\n")
// Steady-state telemetry
fmt.Fprintf(&b, "**Steady-state telemetry** (%ds):\n\n", int(gpu.Steady.DurationSec))
b.WriteString("| | Avg | P95 |\n|---|---|---|\n")
fmt.Fprintf(&b, "| Power | %.1f W | %.1f W |\n", gpu.Steady.AvgPowerW, gpu.Steady.P95PowerW)
fmt.Fprintf(&b, "| Temperature | %.1f °C | %.1f °C |\n", gpu.Steady.AvgTempC, gpu.Steady.P95TempC)
fmt.Fprintf(&b, "| GPU clock | %.0f MHz | %.0f MHz |\n", gpu.Steady.AvgGraphicsClockMHz, gpu.Steady.P95GraphicsClockMHz)
fmt.Fprintf(&b, "| Memory clock | %.0f MHz | %.0f MHz |\n", gpu.Steady.AvgMemoryClockMHz, gpu.Steady.P95MemoryClockMHz)
fmt.Fprintf(&b, "| GPU utilisation | %.1f %% | — |\n", gpu.Steady.AvgUsagePct)
b.WriteString("\n")
if benchmarkTelemetryAvailable(gpu.Steady) {
fmt.Fprintf(&b, "**Steady-state telemetry** (%ds):\n\n", int(gpu.Steady.DurationSec))
b.WriteString("| | Avg | P95 |\n|---|---|---|\n")
fmt.Fprintf(&b, "| Power | %.1f W | %.1f W |\n", gpu.Steady.AvgPowerW, gpu.Steady.P95PowerW)
fmt.Fprintf(&b, "| Temperature | %.1f °C | %.1f °C |\n", gpu.Steady.AvgTempC, gpu.Steady.P95TempC)
fmt.Fprintf(&b, "| GPU clock | %.0f MHz | %.0f MHz |\n", gpu.Steady.AvgGraphicsClockMHz, gpu.Steady.P95GraphicsClockMHz)
fmt.Fprintf(&b, "| Memory clock | %.0f MHz | %.0f MHz |\n", gpu.Steady.AvgMemoryClockMHz, gpu.Steady.P95MemoryClockMHz)
fmt.Fprintf(&b, "| GPU utilisation | %.1f %% | — |\n", gpu.Steady.AvgUsagePct)
b.WriteString("\n")
} else {
b.WriteString("**Steady-state telemetry:** unavailable\n\n")
}
// Per-precision stability phases.
if len(gpu.PrecisionSteady) > 0 {
@@ -329,6 +408,19 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult) string {
}
}
// ── Platform Scalability ──────────────────────────────────────────────────
if len(result.PerformanceRampSteps) > 0 {
b.WriteString("## Platform Scalability (Performance Ramp)\n\n")
fmt.Fprintf(&b, "**Platform power score:** %.1f%% \n\n", result.PlatformPowerScore)
b.WriteString("| k GPUs | GPU Indices | Total Synthetic TOPS | Scalability |\n")
b.WriteString("|--------|-------------|----------------------|-------------|\n")
for _, step := range result.PerformanceRampSteps {
fmt.Fprintf(&b, "| %d | %s | %.2f | %.1f%% |\n",
step.StepIndex, joinIndexList(step.GPUIndices), step.TotalSyntheticTOPS, step.ScalabilityPct)
}
b.WriteString("\n")
}
// ── Raw files ─────────────────────────────────────────────────────────────
b.WriteString("## Raw Files\n\n")
b.WriteString("- `result.json`\n- `report.md`\n- `summary.txt`\n- `verbose.log`\n")

View File

@@ -49,8 +49,8 @@ func TestBuildBenchmarkSteadyPlanStandard(t *testing.T) {
benchmarkPrecisionPhases,
func(label string) string { return label },
)
if len(labels) != 7 || len(phases) != 7 {
t.Fatalf("labels=%d phases=%d want 7", len(labels), len(phases))
if len(labels) != 5 || len(phases) != 5 {
t.Fatalf("labels=%d phases=%d want 5", len(labels), len(phases))
}
if basePhaseSec != 60 {
t.Fatalf("basePhaseSec=%d want 60", basePhaseSec)
@@ -61,7 +61,7 @@ func TestBuildBenchmarkSteadyPlanStandard(t *testing.T) {
if phases[len(phases)-1].PlanLabel != "mixed" || phases[len(phases)-1].DurationSec != 300 {
t.Fatalf("mixed phase=%+v want duration 300", phases[len(phases)-1])
}
if benchmarkPlanDurationsCSV(phases) != "60,60,60,60,60,60,300" {
if benchmarkPlanDurationsCSV(phases) != "60,60,60,60,300" {
t.Fatalf("durations=%q", benchmarkPlanDurationsCSV(phases))
}
}
@@ -80,7 +80,7 @@ func TestBuildBenchmarkSteadyPlanStability(t *testing.T) {
if mixedPhaseSec != 3600 {
t.Fatalf("mixedPhaseSec=%d want 3600", mixedPhaseSec)
}
if benchmarkPlanDurationsCSV(phases) != "300,300,300,300,300,300,3600" {
if benchmarkPlanDurationsCSV(phases) != "300,300,300,300,3600" {
t.Fatalf("durations=%q", benchmarkPlanDurationsCSV(phases))
}
}
@@ -99,7 +99,7 @@ func TestBuildBenchmarkSteadyPlanOvernight(t *testing.T) {
if mixedPhaseSec != 14400 {
t.Fatalf("mixedPhaseSec=%d want 14400", mixedPhaseSec)
}
if benchmarkPlanDurationsCSV(phases) != "3600,3600,3600,3600,3600,3600,14400" {
if benchmarkPlanDurationsCSV(phases) != "3600,3600,3600,3600,14400" {
t.Fatalf("durations=%q", benchmarkPlanDurationsCSV(phases))
}
}
@@ -133,10 +133,10 @@ func TestSplitBenchmarkRowsByPlannedPhaseUsesPhaseDurations(t *testing.T) {
func TestBenchmarkSupportedPrecisionsSkipsFP4BeforeBlackwell(t *testing.T) {
t.Parallel()
if got := benchmarkSupportedPrecisions("9.0"); strings.Join(got, ",") != "int8,fp8,fp16,fp32,fp64" {
if got := benchmarkSupportedPrecisions("9.0"); strings.Join(got, ",") != "int8,fp8,fp16,fp32" {
t.Fatalf("supported=%v", got)
}
if got := benchmarkSupportedPrecisions("10.0"); strings.Join(got, ",") != "int8,fp8,fp16,fp32,fp64,fp4" {
if got := benchmarkSupportedPrecisions("10.0"); strings.Join(got, ",") != "int8,fp8,fp16,fp32" {
t.Fatalf("supported=%v", got)
}
}
@@ -314,6 +314,30 @@ func TestRenderBenchmarkReportListsUnifiedArtifacts(t *testing.T) {
}
}
func TestScoreBenchmarkGPUIgnoresDisabledPrecisions(t *testing.T) {
t.Parallel()
score := scoreBenchmarkGPUResult(BenchmarkGPUResult{
PrecisionSteady: []BenchmarkPrecisionSteadyPhase{
{Precision: "fp16", WeightedTeraOpsPerSec: 100},
{Precision: "fp64", WeightedTeraOpsPerSec: 999},
{Precision: "fp4", WeightedTeraOpsPerSec: 999},
},
PrecisionResults: []BenchmarkPrecisionResult{
{Category: "fp32_tf32", Supported: true, WeightedTeraOpsPerSec: 50},
{Category: "fp64", Supported: true, WeightedTeraOpsPerSec: 999},
{Category: "fp4", Supported: true, WeightedTeraOpsPerSec: 999},
},
})
if score.SyntheticScore != 100 {
t.Fatalf("SyntheticScore=%f want 100", score.SyntheticScore)
}
if score.MixedScore != 50 {
t.Fatalf("MixedScore=%f want 50", score.MixedScore)
}
}
func TestEnrichGPUInfoWithMaxClocks(t *testing.T) {
t.Parallel()

View File

@@ -31,6 +31,7 @@ type BenchmarkCoolingSummary struct {
Available bool `json:"available"`
AvgFanRPM float64 `json:"avg_fan_rpm,omitempty"`
FanDutyCycleAvailable bool `json:"fan_duty_cycle_available,omitempty"`
FanDutyCycleEstimated bool `json:"fan_duty_cycle_estimated,omitempty"`
AvgFanDutyCyclePct float64 `json:"avg_fan_duty_cycle_pct,omitempty"`
P95FanDutyCyclePct float64 `json:"p95_fan_duty_cycle_pct,omitempty"`
Notes []string `json:"notes,omitempty"`
@@ -55,27 +56,32 @@ type NvidiaBenchmarkOptions struct {
}
type NvidiaBenchmarkResult struct {
BenchmarkVersion string `json:"benchmark_version"`
GeneratedAt time.Time `json:"generated_at"`
Hostname string `json:"hostname,omitempty"`
ServerModel string `json:"server_model,omitempty"`
BenchmarkProfile string `json:"benchmark_profile"`
ParallelGPUs bool `json:"parallel_gpus,omitempty"`
RampStep int `json:"ramp_step,omitempty"`
RampTotal int `json:"ramp_total,omitempty"`
RampRunID string `json:"ramp_run_id,omitempty"`
ScalabilityScore float64 `json:"scalability_score,omitempty"`
OverallStatus string `json:"overall_status"`
SelectedGPUIndices []int `json:"selected_gpu_indices"`
Findings []string `json:"findings,omitempty"`
Warnings []string `json:"warnings,omitempty"`
Normalization BenchmarkNormalization `json:"normalization"`
HostConfig *BenchmarkHostConfig `json:"host_config,omitempty"`
CPULoad *BenchmarkCPULoad `json:"cpu_load,omitempty"`
Cooling *BenchmarkCoolingSummary `json:"cooling,omitempty"`
GPUs []BenchmarkGPUResult `json:"gpus"`
Interconnect *BenchmarkInterconnectResult `json:"interconnect,omitempty"`
ServerPower *BenchmarkServerPower `json:"server_power,omitempty"`
BenchmarkVersion string `json:"benchmark_version"`
GeneratedAt time.Time `json:"generated_at"`
Hostname string `json:"hostname,omitempty"`
ServerModel string `json:"server_model,omitempty"`
BenchmarkProfile string `json:"benchmark_profile"`
ParallelGPUs bool `json:"parallel_gpus,omitempty"`
RampStep int `json:"ramp_step,omitempty"`
RampTotal int `json:"ramp_total,omitempty"`
RampRunID string `json:"ramp_run_id,omitempty"`
ScalabilityScore float64 `json:"scalability_score,omitempty"`
// PlatformPowerScore is the mean compute scalability across ramp steps 2..N.
// 100% = each added GPU contributes exactly its single-card throughput.
// < 100% = throughput loss due to thermal throttle, power limits, or contention.
PlatformPowerScore float64 `json:"platform_power_score,omitempty"`
PerformanceRampSteps []NvidiaPerformanceRampStep `json:"performance_ramp_steps,omitempty"`
OverallStatus string `json:"overall_status"`
SelectedGPUIndices []int `json:"selected_gpu_indices"`
Findings []string `json:"findings,omitempty"`
Warnings []string `json:"warnings,omitempty"`
Normalization BenchmarkNormalization `json:"normalization"`
HostConfig *BenchmarkHostConfig `json:"host_config,omitempty"`
CPULoad *BenchmarkCPULoad `json:"cpu_load,omitempty"`
Cooling *BenchmarkCoolingSummary `json:"cooling,omitempty"`
GPUs []BenchmarkGPUResult `json:"gpus"`
Interconnect *BenchmarkInterconnectResult `json:"interconnect,omitempty"`
ServerPower *BenchmarkServerPower `json:"server_power,omitempty"`
}
type BenchmarkNormalization struct {
@@ -107,6 +113,12 @@ type BenchmarkGPUResult struct {
PowerLimitDerated bool `json:"power_limit_derated,omitempty"`
MultiprocessorCount int `json:"multiprocessor_count,omitempty"`
DefaultPowerLimitW float64 `json:"default_power_limit_w,omitempty"`
// ShutdownTempC is the hardware thermal shutdown threshold for this GPU,
// sourced from nvidia-smi -q ("GPU Shutdown Temp"). Fallback: 90°C.
ShutdownTempC float64 `json:"shutdown_temp_c,omitempty"`
// SlowdownTempC is the software throttle onset threshold ("GPU Slowdown Temp").
// Fallback: 80°C.
SlowdownTempC float64 `json:"slowdown_temp_c,omitempty"`
// CalibratedPeakPowerW is the p95 power measured during a short
// dcgmi targeted_power calibration run before the main benchmark.
// Used as the reference denominator for PowerSustainScore instead of
@@ -206,9 +218,30 @@ type BenchmarkScorecard struct {
MixedEfficiency float64 `json:"mixed_efficiency,omitempty"`
PowerSustainScore float64 `json:"power_sustain_score"`
ThermalSustainScore float64 `json:"thermal_sustain_score"`
StabilityScore float64 `json:"stability_score"`
InterconnectScore float64 `json:"interconnect_score"`
CompositeScore float64 `json:"composite_score"`
// StabilityScore: fraction of steady-state time the GPU spent throttling
// (thermal + power cap combined). 0% throttle = 100; 100% throttle = 0.
StabilityScore float64 `json:"stability_score"`
// Throttle breakdown — percentage of steady-state time in each throttle type.
// Used for diagnosis: tells WHY the GPU throttled, not just whether it did.
ThermalThrottlePct float64 `json:"thermal_throttle_pct"` // HW+SW thermal slowdown
PowerCapThrottlePct float64 `json:"power_cap_throttle_pct"` // SW power cap
SyncBoostThrottlePct float64 `json:"sync_boost_throttle_pct,omitempty"`
// Temperature headroom: distance to the 100°C destruction threshold.
// TempHeadroomC = 100 - P95TempC. < 20°C = warning; < 10°C = critical.
// Independent of throttle — a GPU at 86°C without throttle is still in the red zone.
TempHeadroomC float64 `json:"temp_headroom_c"`
InterconnectScore float64 `json:"interconnect_score"`
// ServerQualityScore (0100) reflects server infrastructure quality independent
// of GPU model. Combines throttle time, power variance, and temp variance.
// Use this to compare servers with the same GPU, or to flag a bad server
// that throttles an otherwise fast GPU.
ServerQualityScore float64 `json:"server_quality_score"`
// CompositeScore is the raw compute score (TOPS, fp32-equivalent).
// A throttling GPU will score lower here automatically — no quality multiplier.
CompositeScore float64 `json:"composite_score"`
// TOPSPerSMPerGHz is compute efficiency independent of clock speed and SM count.
TOPSPerSMPerGHz float64 `json:"tops_per_sm_per_ghz,omitempty"`
}
@@ -265,16 +298,31 @@ type NvidiaPowerBenchResult struct {
RecommendedSlotOrder []int `json:"recommended_slot_order,omitempty"`
RampSteps []NvidiaPowerBenchStep `json:"ramp_steps,omitempty"`
OverallStatus string `json:"overall_status"`
Findings []string `json:"findings,omitempty"`
GPUs []NvidiaPowerBenchGPU `json:"gpus"`
// PlatformMaxTDPW is the sum of per-GPU stable power limits found during the
// cumulative thermal ramp. Represents the actual sustained power budget of
// this server under full GPU load. Use for rack power planning.
PlatformMaxTDPW float64 `json:"platform_max_tdp_w"`
// ServerPower captures IPMI server power delta (idle→loaded) measured in
// parallel with the thermal ramp. Use to compare GPU-reported TDP against
// actual wall-power draw as seen by the server's power supply.
ServerPower *BenchmarkServerPower `json:"server_power,omitempty"`
Findings []string `json:"findings,omitempty"`
GPUs []NvidiaPowerBenchGPU `json:"gpus"`
}
type NvidiaPowerBenchGPU struct {
Index int `json:"index"`
Name string `json:"name,omitempty"`
BusID string `json:"bus_id,omitempty"`
DefaultPowerLimitW float64 `json:"default_power_limit_w,omitempty"`
AppliedPowerLimitW float64 `json:"applied_power_limit_w,omitempty"`
Index int `json:"index"`
Name string `json:"name,omitempty"`
BusID string `json:"bus_id,omitempty"`
DefaultPowerLimitW float64 `json:"default_power_limit_w,omitempty"`
// AppliedPowerLimitW is the stable limit found during single-card calibration.
AppliedPowerLimitW float64 `json:"applied_power_limit_w,omitempty"`
// StablePowerLimitW is the final fixed limit for this GPU after the
// cumulative thermal ramp. This is the limit at which the GPU operated
// stably with all other GPUs running simultaneously at their own limits.
// May be lower than AppliedPowerLimitW if multi-GPU thermal load required
// additional derating.
StablePowerLimitW float64 `json:"stable_power_limit_w,omitempty"`
MaxObservedPowerW float64 `json:"max_observed_power_w,omitempty"`
MaxObservedTempC float64 `json:"max_observed_temp_c,omitempty"`
CalibrationAttempts int `json:"calibration_attempts,omitempty"`
@@ -286,13 +334,31 @@ type NvidiaPowerBenchGPU struct {
}
type NvidiaPowerBenchStep struct {
StepIndex int `json:"step_index"`
GPUIndices []int `json:"gpu_indices"`
TotalObservedPowerW float64 `json:"total_observed_power_w,omitempty"`
AvgObservedPowerW float64 `json:"avg_observed_power_w,omitempty"`
MinPowerRealizationPct float64 `json:"min_power_realization_pct,omitempty"`
AvgPowerRealizationPct float64 `json:"avg_power_realization_pct,omitempty"`
DeratedGPUCount int `json:"derated_gpu_count,omitempty"`
Status string `json:"status"`
Notes []string `json:"notes,omitempty"`
StepIndex int `json:"step_index"`
GPUIndices []int `json:"gpu_indices"`
// NewGPUIndex is the GPU whose stable limit was searched in this step.
NewGPUIndex int `json:"new_gpu_index"`
// NewGPUStableLimitW is the stable power limit found for the new GPU.
NewGPUStableLimitW float64 `json:"new_gpu_stable_limit_w,omitempty"`
TotalObservedPowerW float64 `json:"total_observed_power_w,omitempty"`
AvgObservedPowerW float64 `json:"avg_observed_power_w,omitempty"`
Derated bool `json:"derated,omitempty"`
Status string `json:"status"`
Notes []string `json:"notes,omitempty"`
}
// NvidiaPerformanceRampStep holds per-step performance data for the
// scalability ramp-up phase of the performance benchmark.
type NvidiaPerformanceRampStep struct {
StepIndex int `json:"step_index"`
GPUIndices []int `json:"gpu_indices"`
// TotalSyntheticTOPS is the sum of per-GPU SyntheticScore (fp32-equivalent
// TOPS from dedicated single-precision phases) across all GPUs in this step.
TotalSyntheticTOPS float64 `json:"total_synthetic_tops"`
TotalMixedTOPS float64 `json:"total_mixed_tops,omitempty"`
// ScalabilityPct = TotalSyntheticTOPS / (k × best_single_gpu_tops) × 100.
// 100% = perfect linear scaling. < 100% = thermal/power/interconnect loss.
ScalabilityPct float64 `json:"scalability_pct"`
Status string `json:"status"`
Notes []string `json:"notes,omitempty"`
}

View File

@@ -27,6 +27,7 @@ type GPUMetricRow struct {
FanAvgRPM float64 `json:"fan_avg_rpm,omitempty"`
FanDutyCyclePct float64 `json:"fan_duty_cycle_pct,omitempty"`
FanDutyCycleAvailable bool `json:"fan_duty_cycle_available,omitempty"`
FanDutyCycleEstimated bool `json:"fan_duty_cycle_estimated,omitempty"`
}
// sampleGPUMetrics runs nvidia-smi once and returns current metrics for each GPU.
@@ -147,14 +148,18 @@ func sampleAMDGPUMetrics() ([]GPUMetricRow, error) {
// WriteGPUMetricsCSV writes collected rows as a CSV file.
func WriteGPUMetricsCSV(path string, rows []GPUMetricRow) error {
var b bytes.Buffer
b.WriteString("stage,elapsed_sec,gpu_index,temperature_c,usage_pct,mem_usage_pct,power_w,clock_mhz,mem_clock_mhz,fan_avg_rpm,fan_duty_cycle_pct,fan_duty_cycle_available\n")
b.WriteString("stage,elapsed_sec,gpu_index,temperature_c,usage_pct,mem_usage_pct,power_w,clock_mhz,mem_clock_mhz,fan_avg_rpm,fan_duty_cycle_pct,fan_duty_cycle_available,fan_duty_cycle_estimated\n")
for _, r := range rows {
dutyAvail := 0
if r.FanDutyCycleAvailable {
dutyAvail = 1
}
fmt.Fprintf(&b, "%s,%.1f,%d,%.1f,%.1f,%.1f,%.1f,%.0f,%.0f,%.0f,%.1f,%d\n",
strconv.Quote(strings.TrimSpace(r.Stage)), r.ElapsedSec, r.GPUIndex, r.TempC, r.UsagePct, r.MemUsagePct, r.PowerW, r.ClockMHz, r.MemClockMHz, r.FanAvgRPM, r.FanDutyCyclePct, dutyAvail)
dutyEstimated := 0
if r.FanDutyCycleEstimated {
dutyEstimated = 1
}
fmt.Fprintf(&b, "%s,%.1f,%d,%.1f,%.1f,%.1f,%.1f,%.0f,%.0f,%.0f,%.1f,%d,%d\n",
strconv.Quote(strings.TrimSpace(r.Stage)), r.ElapsedSec, r.GPUIndex, r.TempC, r.UsagePct, r.MemUsagePct, r.PowerW, r.ClockMHz, r.MemClockMHz, r.FanAvgRPM, r.FanDutyCyclePct, dutyAvail, dutyEstimated)
}
return os.WriteFile(path, b.Bytes(), 0644)
}

View File

@@ -426,6 +426,13 @@ func (s *System) RunNvidiaTargetedPowerPack(ctx context.Context, baseDir string,
if err != nil {
return "", err
}
// Kill any lingering nvvs/dcgmi processes from a previous interrupted run
// before starting — otherwise dcgmi diag fails with DCGM_ST_IN_USE (-34).
if killed := KillTestWorkers(); len(killed) > 0 && logFunc != nil {
for _, p := range killed {
logFunc(fmt.Sprintf("pre-flight: killed stale worker pid=%d name=%s", p.PID, p.Name))
}
}
return runAcceptancePackCtx(ctx, baseDir, "gpu-nvidia-targeted-power", withNvidiaPersistenceMode(
satJob{name: "01-nvidia-smi-q.log", cmd: []string{"nvidia-smi", "-q"}},
satJob{
@@ -443,6 +450,13 @@ func (s *System) RunNvidiaPulseTestPack(ctx context.Context, baseDir string, dur
if err != nil {
return "", err
}
// Kill any lingering nvvs/dcgmi processes from a previous interrupted run
// before starting — otherwise dcgmi diag fails with DCGM_ST_IN_USE (-34).
if killed := KillTestWorkers(); len(killed) > 0 && logFunc != nil {
for _, p := range killed {
logFunc(fmt.Sprintf("pre-flight: killed stale worker pid=%d name=%s", p.PID, p.Name))
}
}
return runAcceptancePackCtx(ctx, baseDir, "gpu-nvidia-pulse", withNvidiaPersistenceMode(
satJob{name: "01-nvidia-smi-q.log", cmd: []string{"nvidia-smi", "-q"}},
satJob{
@@ -460,6 +474,13 @@ func (s *System) RunNvidiaBandwidthPack(ctx context.Context, baseDir string, gpu
if err != nil {
return "", err
}
// Kill any lingering nvvs/dcgmi processes from a previous interrupted run
// before starting — otherwise dcgmi diag fails with DCGM_ST_IN_USE (-34).
if killed := KillTestWorkers(); len(killed) > 0 && logFunc != nil {
for _, p := range killed {
logFunc(fmt.Sprintf("pre-flight: killed stale worker pid=%d name=%s", p.PID, p.Name))
}
}
return runAcceptancePackCtx(ctx, baseDir, "gpu-nvidia-bandwidth", withNvidiaPersistenceMode(
satJob{name: "01-nvidia-smi-q.log", cmd: []string{"nvidia-smi", "-q"}},
satJob{
@@ -552,10 +573,16 @@ func (s *System) RunMemoryAcceptancePack(ctx context.Context, baseDir string, si
if passes <= 0 {
passes = 1
}
// Bound memtester with a hard wall-clock timeout: ~2.5 min per 100 MB per
// pass, plus a fixed 2-minute buffer. Without this, a stuck memory
// controller can cause memtester to spin forever on a single subtest.
timeoutSec := sizeMB*passes*150/100 + 120
// Keep Validate Memory bounded to a quick diagnostic window. The timeout is
// intentionally conservative enough for healthy systems while avoiding the
// prior 30-80 minute hangs caused by memtester spinning on a bad subtest.
timeoutSec := sizeMB*passes*20/100 + 60
if timeoutSec < 180 {
timeoutSec = 180
}
if timeoutSec > 900 {
timeoutSec = 900
}
return runAcceptancePackCtx(ctx, baseDir, "memory", []satJob{
{name: "01-free-before.log", cmd: []string{"free", "-h"}},
{name: "02-memtester.log", cmd: []string{"timeout", fmt.Sprintf("%d", timeoutSec), "memtester", fmt.Sprintf("%dM", sizeMB), fmt.Sprintf("%d", passes)}},

View File

@@ -4,6 +4,7 @@ import (
"context"
"encoding/json"
"fmt"
"math"
"os"
"os/exec"
"path/filepath"
@@ -56,13 +57,37 @@ type cachedPowerReading struct {
UpdatedAt time.Time
}
type fanObservationState struct {
MaxRPM map[string]float64 `json:"max_rpm"`
}
type fanPeakCandidate struct {
FirstSeen time.Time
RPM float64
}
var (
systemPowerCacheMu sync.Mutex
systemPowerCache cachedPowerReading
fanObservationMu sync.Mutex
fanObservation fanObservationState
fanObservationInit bool
fanPeakCandidates = make(map[string]fanPeakCandidate)
)
const systemPowerHoldTTL = 15 * time.Second
var fanObservationStatePath = "/var/log/bee-sat/fan-observation.json"
const fanObservationMinPeakHold = time.Second
func normalizeObservedFanMaxRPM(rpm float64) float64 {
if rpm <= 0 {
return 0
}
return math.Ceil(rpm/1000.0) * 1000.0
}
// RunFanStressTest runs a two-phase GPU stress test while monitoring fan speeds,
// temperatures, and power draw every second. Exports metrics.csv and fan-sensors.csv.
// Designed to reproduce case-04 fan-speed lag and detect GPU thermal throttling.
@@ -310,11 +335,13 @@ func sampleFanSpeeds() ([]FanReading, error) {
out, err := exec.Command("ipmitool", "sdr", "type", "Fan").Output()
if err == nil {
if fans := parseFanSpeeds(string(out)); len(fans) > 0 {
updateFanObservation(fans, time.Now())
return fans, nil
}
}
fans, sensorsErr := sampleFanSpeedsViaSensorsJSON()
if len(fans) > 0 {
updateFanObservation(fans, time.Now())
return fans, nil
}
if err != nil {
@@ -323,6 +350,119 @@ func sampleFanSpeeds() ([]FanReading, error) {
return nil, sensorsErr
}
func loadFanObservationLocked() {
if fanObservationInit {
return
}
fanObservationInit = true
fanObservation.MaxRPM = make(map[string]float64)
raw, err := os.ReadFile(fanObservationStatePath)
if err != nil || len(raw) == 0 {
return
}
var persisted fanObservationState
if json.Unmarshal(raw, &persisted) != nil {
return
}
for name, rpm := range persisted.MaxRPM {
name = strings.TrimSpace(name)
if name == "" || rpm <= 0 {
continue
}
fanObservation.MaxRPM[name] = rpm
}
}
func saveFanObservationLocked() {
if len(fanObservation.MaxRPM) == 0 {
return
}
dir := filepath.Dir(fanObservationStatePath)
if dir == "" || dir == "." {
dir = "/var/log/bee-sat"
}
if err := os.MkdirAll(dir, 0755); err != nil {
return
}
raw, err := json.MarshalIndent(fanObservation, "", " ")
if err != nil {
return
}
_ = os.WriteFile(fanObservationStatePath, raw, 0644)
}
func updateFanObservation(fans []FanReading, now time.Time) {
if len(fans) == 0 {
return
}
fanObservationMu.Lock()
defer fanObservationMu.Unlock()
loadFanObservationLocked()
changed := false
for _, fan := range fans {
name := strings.TrimSpace(fan.Name)
if name == "" || fan.RPM <= 0 {
continue
}
currentMax := fanObservation.MaxRPM[name]
if fan.RPM <= currentMax {
delete(fanPeakCandidates, name)
continue
}
if cand, ok := fanPeakCandidates[name]; ok {
if now.Sub(cand.FirstSeen) >= fanObservationMinPeakHold {
newMax := math.Max(cand.RPM, fan.RPM)
if newMax > currentMax {
fanObservation.MaxRPM[name] = normalizeObservedFanMaxRPM(newMax)
changed = true
}
delete(fanPeakCandidates, name)
continue
}
if fan.RPM > cand.RPM {
fanPeakCandidates[name] = fanPeakCandidate{FirstSeen: cand.FirstSeen, RPM: fan.RPM}
}
continue
}
fanPeakCandidates[name] = fanPeakCandidate{FirstSeen: now, RPM: fan.RPM}
}
if changed {
saveFanObservationLocked()
}
}
func estimateFanDutyCyclePctFromObservation(fans []FanReading) (float64, bool) {
if len(fans) == 0 {
return 0, false
}
fanObservationMu.Lock()
defer fanObservationMu.Unlock()
loadFanObservationLocked()
var samples []float64
for _, fan := range fans {
name := strings.TrimSpace(fan.Name)
if name == "" || fan.RPM <= 0 {
continue
}
maxRPM := fanObservation.MaxRPM[name]
if maxRPM <= 0 {
continue
}
pct := fan.RPM / maxRPM * 100.0
if pct > 100 {
pct = 100
}
if pct < 0 {
pct = 0
}
samples = append(samples, pct)
}
if len(samples) == 0 {
return 0, false
}
return benchmarkMean(samples), true
}
// parseFanSpeeds parses "ipmitool sdr type Fan" output.
// Handles two formats:
//
@@ -428,12 +568,27 @@ func sampleFanSpeedsViaSensorsJSON() ([]FanReading, error) {
// sampleFanDutyCyclePct reads fan PWM/duty-cycle controls from lm-sensors.
// Returns the average duty cycle across all exposed PWM controls.
func sampleFanDutyCyclePct() (float64, bool) {
func sampleFanDutyCyclePct() (float64, bool, bool) {
out, err := exec.Command("sensors", "-j").Output()
if err != nil || len(out) == 0 {
return 0, false
fans, fanErr := sampleFanSpeeds()
if fanErr != nil {
return 0, false, false
}
return sampleFanDutyCyclePctFromFans(fans)
}
return parseFanDutyCyclePctSensorsJSON(out)
pct, ok := parseFanDutyCyclePctSensorsJSON(out)
return pct, ok, false
}
func sampleFanDutyCyclePctFromFans(fans []FanReading) (float64, bool, bool) {
if len(fans) == 0 {
return 0, false, false
}
if pct, ok := estimateFanDutyCyclePctFromObservation(fans); ok {
return pct, true, true
}
return 0, false, false
}
func parseFanDutyCyclePctSensorsJSON(raw []byte) (float64, bool) {

View File

@@ -1,6 +1,7 @@
package platform
import (
"path/filepath"
"testing"
"time"
)
@@ -50,6 +51,53 @@ func TestParseFanDutyCyclePctSensorsJSON(t *testing.T) {
}
}
func TestEstimateFanDutyCyclePctFromObservation(t *testing.T) {
t.Parallel()
oldPath := fanObservationStatePath
oldState := fanObservation
oldInit := fanObservationInit
oldCandidates := fanPeakCandidates
fanObservationStatePath = filepath.Join(t.TempDir(), "fan-observation.json")
fanObservation = fanObservationState{}
fanObservationInit = false
fanPeakCandidates = make(map[string]fanPeakCandidate)
t.Cleanup(func() {
fanObservationStatePath = oldPath
fanObservation = oldState
fanObservationInit = oldInit
fanPeakCandidates = oldCandidates
})
start := time.Unix(100, 0)
updateFanObservation([]FanReading{{Name: "FAN1", RPM: 5000}}, start)
if _, ok := estimateFanDutyCyclePctFromObservation([]FanReading{{Name: "FAN1", RPM: 2500}}); ok {
t.Fatalf("single-sample spike should not establish observed max")
}
updateFanObservation([]FanReading{{Name: "FAN1", RPM: 5200}}, start.Add(500*time.Millisecond))
updateFanObservation([]FanReading{{Name: "FAN1", RPM: 5100}}, start.Add(1500*time.Millisecond))
got, ok := estimateFanDutyCyclePctFromObservation([]FanReading{{Name: "FAN1", RPM: 2600}})
if !ok {
t.Fatalf("expected estimated duty cycle from persisted observed max")
}
if got < 43 || got > 44 {
t.Fatalf("got=%v want ~43.3", got)
}
fanObservation = fanObservationState{}
fanObservationInit = false
fanPeakCandidates = make(map[string]fanPeakCandidate)
got, ok = estimateFanDutyCyclePctFromObservation([]FanReading{{Name: "FAN1", RPM: 2600}})
if !ok {
t.Fatalf("expected persisted observed max to be reloaded from disk")
}
if got < 43 || got > 44 {
t.Fatalf("reloaded got=%v want ~43.3", got)
}
}
func TestParseDCMIPowerReading(t *testing.T) {
raw := `
Instantaneous power reading: 512 Watts

View File

@@ -737,6 +737,9 @@ func (h *handler) handleAPISATAbort(w http.ResponseWriter, r *http.Request) {
if t.job != nil {
t.job.abort()
}
if taskMayLeaveOrphanWorkers(t.Target) {
platform.KillTestWorkers()
}
t.Status = TaskCancelled
now := time.Now()
t.DoneAt = &now

View File

@@ -72,6 +72,13 @@ tbody tr:hover td{background:rgba(0,0,0,.03)}
.badge-warn{background:var(--warn-bg);color:var(--warn-fg);border:1px solid #c9ba9b}
.badge-err{background:var(--crit-bg);color:var(--crit-fg);border:1px solid var(--crit-border)}
.badge-unknown{background:var(--surface-2);color:var(--muted);border:1px solid var(--border)}
/* Component chips — one small square per device */
.chips{display:inline-flex;flex-wrap:wrap;gap:3px;align-items:center;vertical-align:middle}
.chip{display:inline-flex;align-items:center;justify-content:center;width:20px;height:20px;border-radius:3px;font-size:10px;font-weight:800;cursor:default;font-family:monospace;letter-spacing:0;user-select:none}
.chip-ok{background:var(--ok-bg);color:var(--ok-fg);border:1px solid #a3c293}
.chip-warn{background:var(--warn-bg);color:var(--warn-fg);border:1px solid #c9ba9b}
.chip-fail{background:var(--crit-bg);color:var(--crit-fg);border:1px solid var(--crit-border)}
.chip-unknown{background:var(--surface-2);color:var(--muted);border:1px solid var(--border)}
/* Output terminal */
.terminal{background:#1b1c1d;border:1px solid rgba(0,0,0,.2);border-radius:4px;padding:14px;font-family:monospace;font-size:12px;color:#b5cea8;max-height:400px;overflow-y:auto;white-space:pre-wrap;word-break:break-all;user-select:text;-webkit-user-select:text}
.terminal-wrap{position:relative}.terminal-copy{position:absolute;top:6px;right:6px;background:#2d2f30;border:1px solid #444;color:#aaa;font-size:11px;padding:2px 8px;border-radius:3px;cursor:pointer;opacity:.7}.terminal-copy:hover{opacity:1}
@@ -363,23 +370,25 @@ func renderHardwareSummaryCard(opts HandlerOptions) string {
html.EscapeString(label), html.EscapeString(value), badgeHTML))
}
cpuRow := aggregateComponentStatus("CPU", records, []string{"cpu:all"}, nil)
writeRow("CPU", hwDescribeCPU(hw), runtimeStatusBadge(cpuRow.Status))
writeRow("CPU", hwDescribeCPU(hw),
renderComponentChips(matchedRecords(records, []string{"cpu:all"}, nil)))
memRow := aggregateComponentStatus("Memory", records, []string{"memory:all"}, []string{"memory:"})
writeRow("Memory", hwDescribeMemory(hw), runtimeStatusBadge(memRow.Status))
writeRow("Memory", hwDescribeMemory(hw),
renderComponentChips(matchedRecords(records, []string{"memory:all"}, []string{"memory:"})))
storageRow := aggregateComponentStatus("Storage", records, []string{"storage:all"}, []string{"storage:"})
writeRow("Storage", hwDescribeStorage(hw), runtimeStatusBadge(storageRow.Status))
writeRow("Storage", hwDescribeStorage(hw),
renderComponentChips(matchedRecords(records, []string{"storage:all"}, []string{"storage:"})))
gpuRow := aggregateComponentStatus("GPU", records, nil, []string{"pcie:gpu:"})
writeRow("GPU", hwDescribeGPU(hw), runtimeStatusBadge(gpuRow.Status))
writeRow("GPU", hwDescribeGPU(hw),
renderComponentChips(matchedRecords(records, nil, []string{"pcie:gpu:"})))
psuRow := aggregateComponentStatus("PSU", records, nil, []string{"psu:"})
if psuRow.Status == "UNKNOWN" && len(hw.PowerSupplies) > 0 {
psuRow.Status = hwPSUStatus(hw.PowerSupplies)
psuMatched := matchedRecords(records, nil, []string{"psu:"})
if len(psuMatched) == 0 && len(hw.PowerSupplies) > 0 {
// No PSU records yet — synthesise a single chip from IPMI status.
psuStatus := hwPSUStatus(hw.PowerSupplies)
psuMatched = []app.ComponentStatusRecord{{ComponentKey: "psu:ipmi", Status: psuStatus}}
}
writeRow("PSU", hwDescribePSU(hw), runtimeStatusBadge(psuRow.Status))
writeRow("PSU", hwDescribePSU(hw), renderComponentChips(psuMatched))
if nicDesc := hwDescribeNIC(hw); nicDesc != "" {
writeRow("Network", nicDesc, "")
@@ -892,6 +901,31 @@ func buildHardwareComponentRows(exportDir string) []runtimeHealthRow {
}
}
// matchedRecords returns all ComponentStatusRecord entries whose key matches
// any exact key or any of the given prefixes. Used for per-device chip rendering.
func firstNonEmpty(vals ...string) string {
for _, v := range vals {
if v != "" {
return v
}
}
return ""
}
func matchedRecords(records []app.ComponentStatusRecord, exact []string, prefixes []string) []app.ComponentStatusRecord {
var matched []app.ComponentStatusRecord
for _, rec := range records {
key := strings.TrimSpace(rec.ComponentKey)
if key == "" {
continue
}
if containsExactKey(key, exact) || hasAnyPrefix(key, prefixes) {
matched = append(matched, rec)
}
}
return matched
}
func aggregateComponentStatus(title string, records []app.ComponentStatusRecord, exact []string, prefixes []string) runtimeHealthRow {
matched := make([]app.ComponentStatusRecord, 0)
for _, rec := range records {
@@ -1034,6 +1068,52 @@ func runtimeIssueDescriptions(issues []schema.RuntimeIssue, codes ...string) str
return strings.Join(messages, "; ")
}
// chipLetterClass maps a component status to a single display letter and CSS class.
func chipLetterClass(status string) (letter, cls string) {
switch strings.ToUpper(strings.TrimSpace(status)) {
case "OK":
return "O", "chip-ok"
case "WARNING", "WARN", "PARTIAL":
return "W", "chip-warn"
case "CRITICAL", "FAIL", "FAILED", "ERROR":
return "F", "chip-fail"
default:
return "?", "chip-unknown"
}
}
// renderComponentChips renders one 20×20 chip per ComponentStatusRecord.
// Hover tooltip shows component key, status, error summary and last check time.
// Falls back to a single unknown chip when no records are available.
func renderComponentChips(matched []app.ComponentStatusRecord) string {
if len(matched) == 0 {
return `<span class="chips"><span class="chip chip-unknown" title="No data">?</span></span>`
}
sort.Slice(matched, func(i, j int) bool {
return matched[i].ComponentKey < matched[j].ComponentKey
})
var b strings.Builder
b.WriteString(`<span class="chips">`)
for _, rec := range matched {
letter, cls := chipLetterClass(rec.Status)
var tooltip strings.Builder
tooltip.WriteString(rec.ComponentKey)
tooltip.WriteString(": ")
tooltip.WriteString(firstNonEmpty(rec.Status, "UNKNOWN"))
if rec.ErrorSummary != "" {
tooltip.WriteString(" — ")
tooltip.WriteString(rec.ErrorSummary)
}
if !rec.LastCheckedAt.IsZero() {
fmt.Fprintf(&tooltip, " (checked %s)", rec.LastCheckedAt.Format("15:04:05"))
}
fmt.Fprintf(&b, `<span class="chip %s" title="%s">%s</span>`,
cls, html.EscapeString(tooltip.String()), letter)
}
b.WriteString(`</span>`)
return b.String()
}
func runtimeStatusBadge(status string) string {
status = strings.ToUpper(strings.TrimSpace(status))
badge := "badge-unknown"
@@ -1339,7 +1419,7 @@ func renderValidate(opts HandlerOptions) string {
inv.Memory,
`Runs a RAM validation pass and records memory state around the test.`,
`<code>free</code>, <code>memtester</code>`,
`256 MB / 1 pass in Validate, 1 GB / 3 passes in Stress.`,
`256 MB / 1 pass in Validate, 512 MB / 1 pass in Stress.`,
)) +
renderSATCard("storage", "Storage", "runSAT('storage')", "", renderValidateCardBody(
inv.Storage,

View File

@@ -162,6 +162,32 @@ type nvidiaRampSpec struct {
TotalDurationSec int
}
func resolveMemoryValidatePreset(profile string, stress bool) (sizeMB, passes int) {
switch strings.TrimSpace(strings.ToLower(profile)) {
case "overnight":
return 1024, 2
case "acceptance":
return 1024, 1
case "smoke":
return 256, 1
}
if stress {
return 512, 1
}
return 256, 1
}
func taskMayLeaveOrphanWorkers(target string) bool {
switch strings.TrimSpace(strings.ToLower(target)) {
case "nvidia", "nvidia-targeted-stress", "nvidia-targeted-power", "nvidia-pulse",
"nvidia-bandwidth", "nvidia-stress", "nvidia-compute", "nvidia-bench-perf",
"memory", "memory-stress", "cpu", "sat-stress", "platform-stress":
return true
default:
return false
}
}
func resolveBurnPreset(profile string) burnPreset {
switch profile {
case "overnight":
@@ -751,10 +777,8 @@ func (q *taskQueue) runTask(t *Task, j *jobState, ctx context.Context) {
err = fmt.Errorf("app not configured")
break
}
sizeMB, passes := 256, 1
if t.params.StressMode {
sizeMB, passes = 1024, 3
}
sizeMB, passes := resolveMemoryValidatePreset(t.params.BurnProfile, t.params.StressMode)
j.append(fmt.Sprintf("Memory validate preset: %d MB x %d pass(es)", sizeMB, passes))
archive, err = runMemoryAcceptancePackCtx(a, ctx, "", sizeMB, passes, j.append)
case "storage":
if a == nil {
@@ -1010,6 +1034,9 @@ func (h *handler) handleAPITasksCancelAll(w http.ResponseWriter, _ *http.Request
if t.job != nil {
t.job.abort()
}
if taskMayLeaveOrphanWorkers(t.Target) {
platform.KillTestWorkers()
}
t.Status = TaskCancelled
t.DoneAt = &now
taskSerialEvent(t, "finished with status="+t.Status)
@@ -1037,6 +1064,9 @@ func (h *handler) handleAPITasksKillWorkers(w http.ResponseWriter, _ *http.Reque
if t.job != nil {
t.job.abort()
}
if taskMayLeaveOrphanWorkers(t.Target) {
platform.KillTestWorkers()
}
t.Status = TaskCancelled
t.DoneAt = &now
taskSerialEvent(t, "finished with status="+t.Status)
@@ -1141,10 +1171,13 @@ func (q *taskQueue) loadLocked() {
q.assignTaskLogPathLocked(t)
if t.Status == TaskRunning {
// The task was interrupted by a bee-web restart. Child processes
// (e.g. bee-gpu-burn-worker) survive the restart in their own
// process groups and cannot be cancelled retroactively. Mark the
// task as failed so the user can decide whether to re-run it
// rather than blindly re-launching duplicate workers.
// (e.g. bee-gpu-burn-worker, dcgmi/nvvs) survive the restart in
// their own process groups. Kill any matching stale workers before
// marking the task failed so the next GPU test does not inherit a
// busy DCGM slot or duplicate workers.
if taskMayLeaveOrphanWorkers(t.Target) {
_ = platform.KillTestWorkers()
}
now := time.Now()
t.Status = TaskFailed
t.DoneAt = &now

View File

@@ -672,6 +672,36 @@ func TestRunTaskUsesBurnProfileDurationForCPU(t *testing.T) {
}
}
func TestRunTaskUsesQuickPresetForMemoryValidate(t *testing.T) {
var gotSizeMB, gotPasses int
q := &taskQueue{
opts: &HandlerOptions{App: &app.App{}},
}
tk := &Task{
ID: "mem-validate-1",
Name: "Memory SAT",
Target: "memory",
Status: TaskRunning,
CreatedAt: time.Now(),
params: taskParams{StressMode: true},
}
j := &jobState{}
orig := runMemoryAcceptancePackCtx
runMemoryAcceptancePackCtx = func(_ *app.App, _ context.Context, _ string, sizeMB, passes int, _ func(string)) (string, error) {
gotSizeMB = sizeMB
gotPasses = passes
return "/tmp/memory-validate.tar.gz", nil
}
defer func() { runMemoryAcceptancePackCtx = orig }()
q.runTask(tk, j, context.Background())
if gotSizeMB != 512 || gotPasses != 1 {
t.Fatalf("memory validate preset=%dMB x%d want 512MB x1", gotSizeMB, gotPasses)
}
}
func TestRunTaskBuildsSupportBundleWithoutApp(t *testing.T) {
dir := t.TempDir()
q := &taskQueue{

View File

@@ -33,6 +33,7 @@ lb config noauto \
--iso-volume "EASY_BEE_${BEE_GPU_VENDOR_UPPER:-NVIDIA}" \
--iso-application "EASY-BEE-${BEE_GPU_VENDOR_UPPER:-NVIDIA}" \
--bootappend-live "boot=live components video=1920x1080 console=tty0 console=ttyS0,115200n8 loglevel=3 systemd.show_status=1 username=bee user-fullname=Bee modprobe.blacklist=nouveau,snd_hda_intel,snd_hda_codec_realtek,snd_hda_codec_generic,soundcore" \
--debootstrap-options "--include=ca-certificates" \
--apt-recommends false \
--chroot-squashfs-compression-type zstd \
"${@}"

View File

@@ -35,6 +35,8 @@ typedef void *CUstream;
#define MAX_STRESS_STREAMS 16
#define MIN_PROFILE_BUDGET_BYTES ((size_t)4u * 1024u * 1024u)
#define MIN_STREAM_BUDGET_BYTES ((size_t)64u * 1024u * 1024u)
#define MAX_SINGLE_PRECISION_STREAMS 4
#define MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES ((size_t)2u * 1024u * 1024u * 1024u)
static const char *ptx_source =
".version 6.0\n"
@@ -296,6 +298,13 @@ static int choose_stream_count(int mp_count, int planned_profiles, size_t total_
return stream_count;
}
static size_t clamp_single_precision_profile_budget(size_t profile_budget_bytes) {
if (profile_budget_bytes > MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES) {
return MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES;
}
return profile_budget_bytes;
}
static void destroy_streams(struct cuda_api *api, CUstream *streams, int count) {
if (!api->cuStreamDestroy) {
return;
@@ -704,6 +713,19 @@ static const struct profile_desc k_profiles[] = {
#define PROFILE_COUNT ((int)(sizeof(k_profiles) / sizeof(k_profiles[0])))
static int profile_allowed_for_run(const struct profile_desc *desc, int cc, const char *precision_filter) {
if (!(desc->enabled && cc >= desc->min_cc)) {
return 0;
}
if (precision_filter != NULL) {
return strcmp(desc->block_label, precision_filter) == 0;
}
/* Mixed/all phases intentionally exclude fp64/fp4 for now: both paths are
* unstable on the current benchmark fleet and can abort the whole mixed
* pass after earlier phases already collected useful telemetry. */
return strcmp(desc->block_label, "fp64") != 0 && strcmp(desc->block_label, "fp4") != 0;
}
static int load_cublaslt(struct cublaslt_api *api) {
memset(api, 0, sizeof(*api));
api->lib = dlopen("libcublasLt.so.13", RTLD_NOW | RTLD_LOCAL);
@@ -908,11 +930,9 @@ static int prepare_profile(struct cublaslt_api *cublas,
CUstream stream,
size_t profile_budget_bytes,
struct prepared_profile *out) {
memset(out, 0, sizeof(*out));
out->desc = *desc;
out->stream = stream;
size_t bytes_per_cell = 0;
size_t attempt_budget = profile_budget_bytes;
bytes_per_cell += bytes_for_elements(desc->a_type, 1);
bytes_per_cell += bytes_for_elements(desc->b_type, 1);
bytes_per_cell += bytes_for_elements(desc->c_type, 1);
@@ -921,106 +941,115 @@ static int prepare_profile(struct cublaslt_api *cublas,
return 0;
}
uint64_t dim = choose_square_dim(profile_budget_bytes, bytes_per_cell, desc->min_multiple);
out->m = dim;
out->n = dim;
out->k = dim;
while (attempt_budget >= MIN_PROFILE_BUDGET_BYTES) {
memset(out, 0, sizeof(*out));
out->desc = *desc;
out->stream = stream;
size_t desired_workspace = profile_budget_bytes / 8u;
if (desired_workspace > 32u * 1024u * 1024u) {
desired_workspace = 32u * 1024u * 1024u;
}
desired_workspace = round_down_size(desired_workspace, 256u);
uint64_t dim = choose_square_dim(attempt_budget, bytes_per_cell, desc->min_multiple);
out->m = dim;
out->n = dim;
out->k = dim;
size_t a_bytes = 0;
size_t b_bytes = 0;
size_t c_bytes = 0;
size_t d_bytes = 0;
size_t scale_bytes = 0;
while (1) {
a_bytes = bytes_for_elements(desc->a_type, out->k * out->m);
b_bytes = bytes_for_elements(desc->b_type, out->k * out->n);
c_bytes = bytes_for_elements(desc->c_type, out->m * out->n);
d_bytes = bytes_for_elements(desc->d_type, out->m * out->n);
scale_bytes = profile_scale_bytes(desc, out->m, out->n, out->k);
size_t desired_workspace = attempt_budget / 8u;
if (desired_workspace > 32u * 1024u * 1024u) {
desired_workspace = 32u * 1024u * 1024u;
}
desired_workspace = round_down_size(desired_workspace, 256u);
size_t matrix_bytes = a_bytes + b_bytes + c_bytes + d_bytes + scale_bytes;
if (matrix_bytes <= profile_budget_bytes) {
size_t remaining = profile_budget_bytes - matrix_bytes;
out->workspace_size = desired_workspace;
if (out->workspace_size > remaining) {
out->workspace_size = round_down_size(remaining, 256u);
size_t a_bytes = 0;
size_t b_bytes = 0;
size_t c_bytes = 0;
size_t d_bytes = 0;
size_t scale_bytes = 0;
while (1) {
a_bytes = bytes_for_elements(desc->a_type, out->k * out->m);
b_bytes = bytes_for_elements(desc->b_type, out->k * out->n);
c_bytes = bytes_for_elements(desc->c_type, out->m * out->n);
d_bytes = bytes_for_elements(desc->d_type, out->m * out->n);
scale_bytes = profile_scale_bytes(desc, out->m, out->n, out->k);
size_t matrix_bytes = a_bytes + b_bytes + c_bytes + d_bytes + scale_bytes;
if (matrix_bytes <= attempt_budget) {
size_t remaining = attempt_budget - matrix_bytes;
out->workspace_size = desired_workspace;
if (out->workspace_size > remaining) {
out->workspace_size = round_down_size(remaining, 256u);
}
break;
}
break;
if (out->m <= (uint64_t)desc->min_multiple) {
break;
}
out->m -= (uint64_t)desc->min_multiple;
out->n = out->m;
out->k = out->m;
}
if (out->m < (uint64_t)desc->min_multiple) {
attempt_budget /= 2u;
continue;
}
if (out->m <= (uint64_t)desc->min_multiple) {
return 0;
}
out->m -= (uint64_t)desc->min_multiple;
out->n = out->m;
out->k = out->m;
}
if (!alloc_filled(cuda, &out->a_dev, a_bytes, 0x11) ||
!alloc_filled(cuda, &out->b_dev, b_bytes, 0x11) ||
!alloc_filled(cuda, &out->c_dev, c_bytes, 0x00) ||
!alloc_filled(cuda, &out->d_dev, d_bytes, 0x00)) {
destroy_profile(cublas, cuda, out);
return 0;
}
cudaDataType_t scale_type = matmul_scale_type(desc);
if (!check_cublas("cublasLtMatmulDescCreate",
cublas->cublasLtMatmulDescCreate(&out->op_desc, desc->compute_type, scale_type))) {
destroy_profile(cublas, cuda, out);
return 0;
}
cublasOperation_t transa = CUBLAS_OP_T;
cublasOperation_t transb = CUBLAS_OP_N;
if (!check_cublas("set TRANSA",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_TRANSA,
&transa,
sizeof(transa))) ||
!check_cublas("set TRANSB",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_TRANSB,
&transb,
sizeof(transb)))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (desc->needs_scalar_scale) {
float one = 1.0f;
if (!alloc_filled(cuda, &out->a_scale_dev, sizeof(one), 0x00) ||
!alloc_filled(cuda, &out->b_scale_dev, sizeof(one), 0x00)) {
if (!alloc_filled(cuda, &out->a_dev, a_bytes, 0x11) ||
!alloc_filled(cuda, &out->b_dev, b_bytes, 0x11) ||
!alloc_filled(cuda, &out->c_dev, c_bytes, 0x00) ||
!alloc_filled(cuda, &out->d_dev, d_bytes, 0x00)) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (!device_upload(cuda, out->a_scale_dev, &one, sizeof(one)) ||
!device_upload(cuda, out->b_scale_dev, &one, sizeof(one))) {
cudaDataType_t scale_type = matmul_scale_type(desc);
if (!check_cublas("cublasLtMatmulDescCreate",
cublas->cublasLtMatmulDescCreate(&out->op_desc, desc->compute_type, scale_type))) {
destroy_profile(cublas, cuda, out);
return 0;
}
void *a_scale_ptr = (void *)(uintptr_t)out->a_scale_dev;
void *b_scale_ptr = (void *)(uintptr_t)out->b_scale_dev;
if (!check_cublas("set A scale ptr",
cublasOperation_t transa = CUBLAS_OP_T;
cublasOperation_t transb = CUBLAS_OP_N;
if (!check_cublas("set TRANSA",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
&a_scale_ptr,
sizeof(a_scale_ptr))) ||
!check_cublas("set B scale ptr",
CUBLASLT_MATMUL_DESC_TRANSA,
&transa,
sizeof(transa))) ||
!check_cublas("set TRANSB",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
&b_scale_ptr,
sizeof(b_scale_ptr)))) {
CUBLASLT_MATMUL_DESC_TRANSB,
&transb,
sizeof(transb)))) {
destroy_profile(cublas, cuda, out);
return 0;
}
}
if (desc->needs_scalar_scale) {
float one = 1.0f;
if (!alloc_filled(cuda, &out->a_scale_dev, sizeof(one), 0x00) ||
!alloc_filled(cuda, &out->b_scale_dev, sizeof(one), 0x00)) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (!device_upload(cuda, out->a_scale_dev, &one, sizeof(one)) ||
!device_upload(cuda, out->b_scale_dev, &one, sizeof(one))) {
destroy_profile(cublas, cuda, out);
return 0;
}
void *a_scale_ptr = (void *)(uintptr_t)out->a_scale_dev;
void *b_scale_ptr = (void *)(uintptr_t)out->b_scale_dev;
if (!check_cublas("set A scale ptr",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
&a_scale_ptr,
sizeof(a_scale_ptr))) ||
!check_cublas("set B scale ptr",
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
&b_scale_ptr,
sizeof(b_scale_ptr)))) {
destroy_profile(cublas, cuda, out);
return 0;
}
}
#if defined(CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3)
if (desc->needs_block_scale) {
@@ -1060,62 +1089,65 @@ static int prepare_profile(struct cublaslt_api *cublas,
}
#endif
if (!check_cublas("create A layout",
cublas->cublasLtMatrixLayoutCreate(&out->a_layout, desc->a_type, out->k, out->m, out->k)) ||
!check_cublas("create B layout",
cublas->cublasLtMatrixLayoutCreate(&out->b_layout, desc->b_type, out->k, out->n, out->k)) ||
!check_cublas("create C layout",
cublas->cublasLtMatrixLayoutCreate(&out->c_layout, desc->c_type, out->m, out->n, out->m)) ||
!check_cublas("create D layout",
cublas->cublasLtMatrixLayoutCreate(&out->d_layout, desc->d_type, out->m, out->n, out->m))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (!check_cublas("create preference", cublas->cublasLtMatmulPreferenceCreate(&out->preference))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (out->workspace_size > 0) {
if (!alloc_filled(cuda, &out->workspace_dev, out->workspace_size, 0x00)) {
if (!check_cublas("create A layout",
cublas->cublasLtMatrixLayoutCreate(&out->a_layout, desc->a_type, out->k, out->m, out->k)) ||
!check_cublas("create B layout",
cublas->cublasLtMatrixLayoutCreate(&out->b_layout, desc->b_type, out->k, out->n, out->k)) ||
!check_cublas("create C layout",
cublas->cublasLtMatrixLayoutCreate(&out->c_layout, desc->c_type, out->m, out->n, out->m)) ||
!check_cublas("create D layout",
cublas->cublasLtMatrixLayoutCreate(&out->d_layout, desc->d_type, out->m, out->n, out->m))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (!check_cublas("create preference", cublas->cublasLtMatmulPreferenceCreate(&out->preference))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (out->workspace_size > 0) {
if (!alloc_filled(cuda, &out->workspace_dev, out->workspace_size, 0x00)) {
destroy_profile(cublas, cuda, out);
return 0;
}
}
if (!check_cublas("set workspace",
cublas->cublasLtMatmulPreferenceSetAttribute(
out->preference,
CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
&out->workspace_size,
sizeof(out->workspace_size)))) {
destroy_profile(cublas, cuda, out);
return 0;
}
int found = 0;
if (check_cublas("heuristic",
cublas->cublasLtMatmulAlgoGetHeuristic(handle,
out->op_desc,
out->a_layout,
out->b_layout,
out->c_layout,
out->d_layout,
out->preference,
1,
&out->heuristic,
&found)) &&
found > 0) {
out->ready = 1;
return 1;
}
destroy_profile(cublas, cuda, out);
attempt_budget = round_down_size(attempt_budget * 3u / 4u, 256u);
if (attempt_budget < MIN_PROFILE_BUDGET_BYTES) {
break;
}
}
if (!check_cublas("set workspace",
cublas->cublasLtMatmulPreferenceSetAttribute(
out->preference,
CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
&out->workspace_size,
sizeof(out->workspace_size)))) {
destroy_profile(cublas, cuda, out);
return 0;
}
int found = 0;
if (!check_cublas("heuristic",
cublas->cublasLtMatmulAlgoGetHeuristic(handle,
out->op_desc,
out->a_layout,
out->b_layout,
out->c_layout,
out->d_layout,
out->preference,
1,
&out->heuristic,
&found))) {
destroy_profile(cublas, cuda, out);
return 0;
}
if (found <= 0) {
destroy_profile(cublas, cuda, out);
return 0;
}
out->ready = 1;
return 1;
return 0;
}
static int run_cublas_profile(cublasLtHandle_t handle,
@@ -1180,6 +1212,7 @@ static int run_cublaslt_stress(struct cuda_api *cuda,
size_t requested_budget = 0;
size_t total_budget = 0;
size_t per_profile_budget = 0;
int budget_profiles = 0;
memset(report, 0, sizeof(*report));
snprintf(report->backend, sizeof(report->backend), "cublasLt");
@@ -1202,8 +1235,7 @@ static int run_cublaslt_stress(struct cuda_api *cuda,
/* Count profiles matching the filter (for deciding what to run). */
for (size_t i = 0; i < sizeof(k_profiles) / sizeof(k_profiles[0]); i++) {
if (k_profiles[i].enabled && cc >= k_profiles[i].min_cc &&
(precision_filter == NULL || strcmp(k_profiles[i].block_label, precision_filter) == 0)) {
if (profile_allowed_for_run(&k_profiles[i], cc, precision_filter)) {
planned++;
}
}
@@ -1215,30 +1247,41 @@ static int run_cublaslt_stress(struct cuda_api *cuda,
}
/* Count all profiles active on this GPU regardless of filter.
* Used as the budget divisor so matrix sizes stay consistent whether
* running all precisions together or a single-precision phase. */
* Mixed phases still divide budget across the full precision set, while
* single-precision benchmark phases dedicate budget only to active
* profiles matching precision_filter. */
int planned_total = 0;
for (size_t i = 0; i < sizeof(k_profiles) / sizeof(k_profiles[0]); i++) {
if (k_profiles[i].enabled && cc >= k_profiles[i].min_cc) {
if (profile_allowed_for_run(&k_profiles[i], cc, precision_filter)) {
planned_total++;
}
}
if (planned_total < planned) {
planned_total = planned;
}
budget_profiles = planned_total;
if (precision_filter != NULL) {
budget_profiles = planned;
}
if (budget_profiles <= 0) {
budget_profiles = planned_total;
}
requested_budget = (size_t)size_mb * 1024u * 1024u;
if (requested_budget < (size_t)planned_total * MIN_PROFILE_BUDGET_BYTES) {
requested_budget = (size_t)planned_total * MIN_PROFILE_BUDGET_BYTES;
if (requested_budget < (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES) {
requested_budget = (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES;
}
total_budget = clamp_budget_to_free_memory(cuda, requested_budget);
if (total_budget < (size_t)planned_total * MIN_PROFILE_BUDGET_BYTES) {
total_budget = (size_t)planned_total * MIN_PROFILE_BUDGET_BYTES;
if (total_budget < (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES) {
total_budget = (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES;
}
if (query_multiprocessor_count(cuda, dev, &mp_count) &&
cuda->cuStreamCreate &&
cuda->cuStreamDestroy) {
stream_count = choose_stream_count(mp_count, planned_total, total_budget, 1);
stream_count = choose_stream_count(mp_count, budget_profiles, total_budget, 1);
}
if (precision_filter != NULL && stream_count > MAX_SINGLE_PRECISION_STREAMS) {
stream_count = MAX_SINGLE_PRECISION_STREAMS;
}
if (stream_count > 1) {
int created = 0;
@@ -1251,18 +1294,22 @@ static int run_cublaslt_stress(struct cuda_api *cuda,
}
}
report->stream_count = stream_count;
per_profile_budget = total_budget / ((size_t)planned_total * (size_t)stream_count);
per_profile_budget = total_budget / ((size_t)budget_profiles * (size_t)stream_count);
if (per_profile_budget < MIN_PROFILE_BUDGET_BYTES) {
per_profile_budget = MIN_PROFILE_BUDGET_BYTES;
}
if (precision_filter != NULL) {
per_profile_budget = clamp_single_precision_profile_budget(per_profile_budget);
}
report->buffer_mb = (int)(total_budget / (1024u * 1024u));
append_detail(report->details,
sizeof(report->details),
"requested_mb=%d actual_mb=%d streams=%d mp_count=%d per_worker_mb=%zu\n",
"requested_mb=%d actual_mb=%d streams=%d mp_count=%d budget_profiles=%d per_worker_mb=%zu\n",
size_mb,
report->buffer_mb,
report->stream_count,
mp_count,
budget_profiles,
per_profile_budget / (1024u * 1024u));
for (int i = 0; i < profile_count; i++) {
@@ -1275,10 +1322,10 @@ static int run_cublaslt_stress(struct cuda_api *cuda,
desc->min_cc);
continue;
}
if (precision_filter != NULL && strcmp(desc->block_label, precision_filter) != 0) {
if (!profile_allowed_for_run(desc, cc, precision_filter)) {
append_detail(report->details,
sizeof(report->details),
"%s=SKIPPED precision_filter\n",
"%s=SKIPPED benchmark_disabled\n",
desc->name);
continue;
}

View File

@@ -1,125 +0,0 @@
#!/bin/sh
# build-dcgm.sh — pre-download DCGM and nvidia-fabricmanager .deb packages
# from the NVIDIA CUDA apt repository (Debian 12, x86_64) on the build host,
# then place them into config/packages.chroot/ so live-build creates a local
# apt repository inside the chroot. This avoids requiring the NVIDIA CUDA
# HTTPS source to be reachable from within the live-build container chroot.
set -e
DCGM_VERSION="$1"
FABRICMANAGER_VERSION="$2"
LB_DIR="$3"
[ -n "$DCGM_VERSION" ] || { echo "usage: $0 <dcgm-version> <fabricmanager-version> <lb-work-dir>"; exit 1; }
[ -n "$FABRICMANAGER_VERSION" ] || { echo "usage: $0 <dcgm-version> <fabricmanager-version> <lb-work-dir>"; exit 1; }
[ -n "$LB_DIR" ] || { echo "usage: $0 <dcgm-version> <fabricmanager-version> <lb-work-dir>"; exit 1; }
REPO_BASE="https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64"
CACHE_ROOT="${BEE_CACHE_DIR:-$(dirname "$LB_DIR")/cache}"
DOWNLOAD_CACHE_DIR="${CACHE_ROOT}/dcgm-downloads"
PACKAGES_GZ="${DOWNLOAD_CACHE_DIR}/Packages.gz"
PKG_CHROOT_DIR="${LB_DIR}/config/packages.chroot"
PACKAGES="
datacenter-gpu-manager-4-core=1:${DCGM_VERSION}
datacenter-gpu-manager-4-cuda13=1:${DCGM_VERSION}
datacenter-gpu-manager-4-proprietary=1:${DCGM_VERSION}
datacenter-gpu-manager-4-proprietary-cuda13=1:${DCGM_VERSION}
nvidia-fabricmanager=${FABRICMANAGER_VERSION}
"
echo "=== DCGM ${DCGM_VERSION} / nvidia-fabricmanager ${FABRICMANAGER_VERSION} ==="
# Check if all target .deb files are already present in packages.chroot
all_cached=1
for entry in $PACKAGES; do
pkg="${entry%%=*}"
if ! ls "${PKG_CHROOT_DIR}/${pkg}_"*.deb >/dev/null 2>&1; then
all_cached=0
break
fi
done
if [ "$all_cached" = "1" ]; then
echo "=== DCGM packages already in packages.chroot, skipping download ==="
ls "${PKG_CHROOT_DIR}/datacenter-gpu-manager-4"*.deb "${PKG_CHROOT_DIR}/nvidia-fabricmanager_"*.deb 2>/dev/null || true
exit 0
fi
mkdir -p "${DOWNLOAD_CACHE_DIR}" "${PKG_CHROOT_DIR}"
echo "=== downloading Packages.gz ==="
wget -q -O "${PACKAGES_GZ}" "${REPO_BASE}/Packages.gz"
lookup_pkg() {
pkg="$1"
ver="$2"
gzip -dc "${PACKAGES_GZ}" | awk -v pkg="$pkg" -v ver="$ver" '
/^Package: / { cur_pkg=$2; gsub(/\r/, "", cur_pkg) }
/^Version: / { cur_ver=$2; gsub(/\r/, "", cur_ver) }
/^Filename: / { cur_file=$2; gsub(/\r/, "", cur_file) }
/^SHA256: / { cur_sha=$2; gsub(/\r/, "", cur_sha) }
/^$/ {
if (cur_pkg == pkg && (ver == "" || cur_ver == ver)) {
print cur_file " " cur_sha
printed=1
exit
}
cur_pkg=""; cur_ver=""; cur_file=""; cur_sha=""
}
END {
if (!printed && cur_pkg == pkg && (ver == "" || cur_ver == ver)) {
print cur_file " " cur_sha
}
}'
}
download_deb() {
pkg="$1"
ver="$2"
meta="$(lookup_pkg "$pkg" "$ver")"
[ -n "$meta" ] || { echo "ERROR: package not found in repo: ${pkg} ${ver}"; exit 1; }
repo_file="$(printf '%s\n' "$meta" | awk '{print $1}')"
repo_sha="$(printf '%s\n' "$meta" | awk '{print $2}')"
[ -n "$repo_file" ] || { echo "ERROR: filename missing for ${pkg}"; exit 1; }
[ -n "$repo_sha" ] || { echo "ERROR: sha256 missing for ${pkg}"; exit 1; }
deb_name="$(basename "$repo_file")"
cached="${DOWNLOAD_CACHE_DIR}/${deb_name}"
if [ -f "$cached" ]; then
actual_sha="$(sha256sum "$cached" | awk '{print $1}')"
if [ "$actual_sha" = "$repo_sha" ]; then
echo "=== cached: ${deb_name} ==="
else
echo "=== removing stale: ${deb_name} (sha256 mismatch) ==="
rm -f "$cached"
wget --show-progress -O "$cached" "${REPO_BASE}/${deb_name}"
fi
else
wget --show-progress -O "$cached" "${REPO_BASE}/${deb_name}"
fi
actual_sha="$(sha256sum "$cached" | awk '{print $1}')"
if [ "$actual_sha" != "$repo_sha" ]; then
echo "ERROR: sha256 mismatch for ${deb_name}" >&2
echo " expected: $repo_sha" >&2
echo " actual: $actual_sha" >&2
rm -f "$cached"
exit 1
fi
echo "sha256 OK: ${deb_name}"
cp -f "$cached" "${PKG_CHROOT_DIR}/${deb_name}"
}
for entry in $PACKAGES; do
pkg="${entry%%=*}"
ver="${entry#*=}"
download_deb "$pkg" "$ver"
done
echo "=== DCGM packages ready in ${PKG_CHROOT_DIR} ==="
ls "${PKG_CHROOT_DIR}/datacenter-gpu-manager-4"*.deb "${PKG_CHROOT_DIR}/nvidia-fabricmanager_"*.deb 2>/dev/null || true

View File

@@ -1296,18 +1296,6 @@ if [ -f "${LB_INCLUDES}/root/.ssh/authorized_keys" ]; then
chmod 600 "${LB_INCLUDES}/root/.ssh/authorized_keys"
fi
# --- pre-download NVIDIA apt packages into config/packages.chroot ---
# live-build creates a local apt repo from config/packages.chroot/*.deb so the
# chroot can install them without reaching the NVIDIA CUDA HTTPS source.
if [ "$BEE_GPU_VENDOR" = "nvidia" ]; then
run_step "download DCGM ${DCGM_VERSION} / fabricmanager ${NVIDIA_FABRICMANAGER_VERSION} packages" \
"25-dcgm" \
sh "${BUILDER_DIR}/build-dcgm.sh" \
"${DCGM_VERSION}" \
"${NVIDIA_FABRICMANAGER_VERSION}" \
"${LB_DIR}"
fi
# --- build ISO using live-build ---
echo ""
echo "=== building ISO (variant: ${BUILD_VARIANT}) ==="
@@ -1317,7 +1305,7 @@ BEE_GPU_VENDOR_UPPER="$(echo "${BUILD_VARIANT}" | tr 'a-z-' 'A-Z_')"
export BEE_GPU_VENDOR_UPPER
cd "${LB_DIR}"
run_step_sh "live-build clean" "80-lb-clean" "lb clean 2>&1 | tail -3"
run_step_sh "live-build clean" "80-lb-clean" "lb clean --all 2>&1 | tail -3"
run_step_sh "live-build config" "81-lb-config" "lb config 2>&1 | tail -5"
dump_memtest_debug "pre-build" "${LB_DIR}"
run_step_sh "live-build build" "90-lb-build" "lb build 2>&1"

View File

@@ -9,9 +9,9 @@ xset s noblank
# Set desktop background.
if [ -f /usr/share/bee/wallpaper.png ]; then
feh --bg-fill /usr/share/bee/wallpaper.png
feh --bg-center --image-bg '#000000' /usr/share/bee/wallpaper.png
else
xsetroot -solid '#f6c90e'
xsetroot -solid '#000000'
fi
tint2 &

Binary file not shown.

After

Width:  |  Height:  |  Size: 70 KiB