Enhance benchmark: server power via IPMI, efficiency metrics, FP64, power limit check

- Sample server power (IPMI dcmi) during baseline+steady phases in parallel;
  compute delta vs GPU-reported sum; flag ratio < 0.75 as unreliable reporting
- Collect base_graphics_clock_mhz, multiprocessor_count, default_power_limit_w
  from nvidia-smi alongside existing GPU info
- Add tops_per_sm_per_ghz efficiency metric (model-agnostic silicon quality signal)
- Flag when enforced power limit is below default TDP by >5%
- Add fp64 profile to bee-gpu-burn worker (CUDA_R_64F, CUBLAS_COMPUTE_64F, min cc 8.0)
- Improve Executive Summary: overall pass count, FAILED GPU finding
- Throttle counters now shown as % of steady window instead of raw microseconds
- bible-local: clock calibration research, H100/H200 spec, real-world GEMM baselines

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

audit/internal/platform/benchmark.go

@@ -33,8 +33,11 @@ type benchmarkGPUInfo struct {
 	BusID                string
 	VBIOS                string
 	PowerLimitW          float64
+	DefaultPowerLimitW   float64
 	MaxGraphicsClockMHz  float64
 	MaxMemoryClockMHz    float64
+	BaseGraphicsClockMHz float64
+	MultiprocessorCount  int
 }
 
 type benchmarkBurnProfile struct {
@@ -111,6 +114,11 @@ func (s *System) RunNvidiaBenchmark(ctx context.Context, baseDir string, opts Nv
 
 	logFunc(fmt.Sprintf("NVIDIA benchmark profile=%s gpus=%s", spec.Name, joinIndexList(selected)))
 
+	// Server power characterization state — populated during per-GPU phases.
+	var serverIdleW, serverLoadedWSum float64
+	var serverIdleOK, serverLoadedOK bool
+	var serverLoadedSamples int
+
 	infoByIndex, infoErr := queryBenchmarkGPUInfo(selected)
 	if infoErr != nil {
 		result.Warnings = append(result.Warnings, "gpu inventory query failed: "+infoErr.Error())
@@ -146,7 +154,10 @@ func (s *System) RunNvidiaBenchmark(ctx context.Context, baseDir string, opts Nv
 			gpuResult.BusID = info.BusID
 			gpuResult.VBIOS = info.VBIOS
 			gpuResult.PowerLimitW = info.PowerLimitW
+			gpuResult.MultiprocessorCount = info.MultiprocessorCount
+			gpuResult.DefaultPowerLimitW = info.DefaultPowerLimitW
 			gpuResult.MaxGraphicsClockMHz = info.MaxGraphicsClockMHz
+			gpuResult.BaseGraphicsClockMHz = info.BaseGraphicsClockMHz
 			gpuResult.MaxMemoryClockMHz = info.MaxMemoryClockMHz
 		}
 		if norm := findBenchmarkNormalization(result.Normalization.GPUs, idx); norm != nil {
@@ -161,6 +172,15 @@ func (s *System) RunNvidiaBenchmark(ctx context.Context, baseDir string, opts Nv
 		gpuResult.Baseline = summarizeBenchmarkTelemetry(baselineRows)
 		writeBenchmarkMetricsFiles(runDir, fmt.Sprintf("gpu-%d-baseline", idx), baselineRows)
 
+		// Sample server idle power once (first GPU only — server state is global).
+		if !serverIdleOK {
+			if w, ok := sampleIPMIPowerSeries(ctx, maxInt(spec.BaselineSec, 10)); ok {
+				serverIdleW = w
+				serverIdleOK = true
+				logFunc(fmt.Sprintf("server idle power (IPMI): %.0f W", w))
+			}
+		}
+
 		warmupCmd := []string{
 			"bee-gpu-burn",
 			"--seconds", strconv.Itoa(spec.WarmupSec),
@@ -184,7 +204,50 @@ func (s *System) RunNvidiaBenchmark(ctx context.Context, baseDir string, opts Nv
 			"--devices", strconv.Itoa(idx),
 		}
 		logFunc(fmt.Sprintf("GPU %d: steady compute (%ds)", idx, spec.SteadySec))
+
+		// Sample server power via IPMI in parallel with the steady phase.
+		// We collect readings every 5s and average them.
+		ipmiStopCh := make(chan struct{})
+		ipmiResultCh := make(chan float64, 1)
+		go func() {
+			defer close(ipmiResultCh)
+			var samples []float64
+			ticker := time.NewTicker(5 * time.Second)
+			defer ticker.Stop()
+			// First sample after a short warmup delay.
+			select {
+			case <-ipmiStopCh:
+				return
+			case <-time.After(15 * time.Second):
+			}
+			for {
+				if w, err := queryIPMIServerPowerW(); err == nil {
+					samples = append(samples, w)
+				}
+				select {
+				case <-ipmiStopCh:
+					if len(samples) > 0 {
+						var sum float64
+						for _, w := range samples {
+							sum += w
+						}
+						ipmiResultCh <- sum / float64(len(samples))
+					}
+					return
+				case <-ticker.C:
+				}
+			}
+		}()
+
 		steadyOut, steadyRows, steadyErr := runBenchmarkCommandWithMetrics(ctx, verboseLog, fmt.Sprintf("gpu-%d-steady.log", idx), steadyCmd, nil, []int{idx}, runDir, fmt.Sprintf("gpu-%d-steady", idx), logFunc)
+		close(ipmiStopCh)
+		if loadedW, ok := <-ipmiResultCh; ok {
+			serverLoadedWSum += loadedW
+			serverLoadedSamples++
+			serverLoadedOK = true
+			logFunc(fmt.Sprintf("GPU %d: server loaded power (IPMI): %.0f W", idx, loadedW))
+		}
+
 		_ = os.WriteFile(filepath.Join(runDir, fmt.Sprintf("gpu-%d-steady.log", idx)), steadyOut, 0644)
 		afterThrottle, _ := queryThrottleCounters(idx)
 		if steadyErr != nil {
@@ -232,6 +295,17 @@ func (s *System) RunNvidiaBenchmark(ctx context.Context, baseDir string, opts Nv
 		}
 	}
 
+	// Compute server power characterization from accumulated IPMI samples.
+	var gpuReportedSumW float64
+	for _, gpu := range result.GPUs {
+		gpuReportedSumW += gpu.Steady.AvgPowerW
+	}
+	var serverLoadedW float64
+	if serverLoadedSamples > 0 {
+		serverLoadedW = serverLoadedWSum / float64(serverLoadedSamples)
+	}
+	result.ServerPower = characterizeServerPower(serverIdleW, serverLoadedW, gpuReportedSumW, serverIdleOK && serverLoadedOK)
+
 	result.Findings = buildBenchmarkFindings(result)
 	result.OverallStatus = benchmarkOverallStatus(result)
 
@@ -290,7 +364,7 @@ func resolveBenchmarkProfile(profile string) benchmarkProfileSpec {
 
 func queryBenchmarkGPUInfo(gpuIndices []int) (map[int]benchmarkGPUInfo, error) {
 	args := []string{
-		"--query-gpu=index,uuid,name,pci.bus_id,vbios_version,power.limit,clocks.max.graphics,clocks.max.memory",
+		"--query-gpu=index,uuid,name,pci.bus_id,vbios_version,power.limit,clocks.max.graphics,clocks.max.memory,clocks.base.graphics,attribute.multiprocessor_count,power.default_limit",
 		"--format=csv,noheader,nounits",
 	}
 	if len(gpuIndices) > 0 {
@@ -311,14 +385,14 @@ func queryBenchmarkGPUInfo(gpuIndices []int) (map[int]benchmarkGPUInfo, error) {
 
 	infoByIndex := make(map[int]benchmarkGPUInfo, len(rows))
 	for _, row := range rows {
-		if len(row) < 8 {
+		if len(row) < 9 {
 			continue
 		}
 		idx, err := strconv.Atoi(strings.TrimSpace(row[0]))
 		if err != nil {
 			continue
 		}
-		infoByIndex[idx] = benchmarkGPUInfo{
+		info := benchmarkGPUInfo{
 			Index:               idx,
 			UUID:                strings.TrimSpace(row[1]),
 			Name:                strings.TrimSpace(row[2]),
@@ -328,6 +402,16 @@ func queryBenchmarkGPUInfo(gpuIndices []int) (map[int]benchmarkGPUInfo, error) {
 			MaxGraphicsClockMHz: parseBenchmarkFloat(row[6]),
 			MaxMemoryClockMHz:   parseBenchmarkFloat(row[7]),
 		}
+		if len(row) >= 9 {
+			info.BaseGraphicsClockMHz = parseBenchmarkFloat(row[8])
+		}
+		if len(row) >= 10 {
+			info.MultiprocessorCount = int(parseBenchmarkFloat(row[9]))
+		}
+		if len(row) >= 11 {
+			info.DefaultPowerLimitW = parseBenchmarkFloat(row[10])
+		}
+		infoByIndex[idx] = info
 	}
 	return infoByIndex, nil
 }
@@ -551,6 +635,8 @@ func ensureBenchmarkProfile(profiles map[string]*benchmarkBurnProfile, name stri
 	}
 	category := "other"
 	switch {
+	case strings.HasPrefix(name, "fp64"):
+		category = "fp64"
 	case strings.HasPrefix(name, "fp32"):
 		category = "fp32_tf32"
 	case strings.HasPrefix(name, "fp16"):
@@ -627,6 +713,9 @@ func scoreBenchmarkGPUResult(gpu BenchmarkGPUResult) BenchmarkScorecard {
 	score.ThermalSustainScore = clampScore(100 - thermalRatio*100)
 	score.StabilityScore = clampScore(100 - (gpu.Steady.ClockCVPct*4 + gpu.Steady.PowerCVPct*2 + gpu.Steady.ClockDriftPct*2))
 	score.CompositeScore = compositeBenchmarkScore(score)
+	if gpu.MultiprocessorCount > 0 && gpu.Steady.AvgGraphicsClockMHz > 0 && score.ComputeScore > 0 {
+		score.TOPSPerSMPerGHz = score.ComputeScore / float64(gpu.MultiprocessorCount) / (gpu.Steady.AvgGraphicsClockMHz / 1000.0)
+	}
 	return score
 }
 
@@ -798,10 +887,30 @@ func finalizeBenchmarkGPUResult(gpu BenchmarkGPUResult) BenchmarkGPUResult {
 
 func buildBenchmarkFindings(result NvidiaBenchmarkResult) []string {
 	var findings []string
+
+	passed := 0
+	for _, gpu := range result.GPUs {
+		if gpu.Status == "OK" {
+			passed++
+		}
+	}
+	total := len(result.GPUs)
+	if total > 0 {
+		if passed == total {
+			findings = append(findings, fmt.Sprintf("All %d GPU(s) passed the benchmark.", total))
+		} else {
+			findings = append(findings, fmt.Sprintf("%d of %d GPU(s) passed the benchmark.", passed, total))
+		}
+	}
+
 	if result.Normalization.Status != "full" {
 		findings = append(findings, "Environment normalization was partial; compare results with caution.")
 	}
 	for _, gpu := range result.GPUs {
+		if gpu.Status == "FAILED" && len(gpu.DegradationReasons) == 0 {
+			findings = append(findings, fmt.Sprintf("GPU %d failed the benchmark (check verbose.log for details).", gpu.Index))
+			continue
+		}
 		if len(gpu.DegradationReasons) == 0 && gpu.Status == "OK" {
 			findings = append(findings, fmt.Sprintf("GPU %d held clocks without observable throttle counters during steady state.", gpu.Index))
 			continue
@@ -825,10 +934,24 @@ func buildBenchmarkFindings(result NvidiaBenchmarkResult) []string {
 		if gpu.Backend == "driver-ptx" {
 			findings = append(findings, fmt.Sprintf("GPU %d used driver PTX fallback; tensor score is intentionally degraded.", gpu.Index))
 		}
+		if gpu.DefaultPowerLimitW > 0 && gpu.PowerLimitW > 0 && gpu.PowerLimitW < gpu.DefaultPowerLimitW*0.95 {
+			findings = append(findings, fmt.Sprintf(
+				"GPU %d power limit %.0f W is below default %.0f W (%.0f%%). Performance may be artificially reduced.",
+				gpu.Index, gpu.PowerLimitW, gpu.DefaultPowerLimitW, gpu.PowerLimitW/gpu.DefaultPowerLimitW*100,
+			))
+		}
 	}
 	if result.Interconnect != nil && result.Interconnect.Supported {
 		findings = append(findings, fmt.Sprintf("Multi-GPU all_reduce max bus bandwidth: %.1f GB/s.", result.Interconnect.MaxBusBWGBps))
 	}
+	if sp := result.ServerPower; sp != nil && sp.Available && sp.GPUReportedSumW > 0 {
+		if sp.ReportingRatio < 0.75 {
+			findings = append(findings, fmt.Sprintf(
+				"GPU power reporting may be unreliable: server delta %.0f W vs GPU-reported %.0f W (ratio %.2f). GPU telemetry likely over-reports actual consumption.",
+				sp.DeltaW, sp.GPUReportedSumW, sp.ReportingRatio,
+			))
+		}
+	}
 	return dedupeStrings(findings)
 }
 
@@ -1007,3 +1130,76 @@ func maxInt(a, b int) int {
 	}
 	return b
 }
+
+// queryIPMIServerPowerW reads the current server power draw via ipmitool dcmi.
+// Returns 0 and an error if IPMI is unavailable or the output cannot be parsed.
+func queryIPMIServerPowerW() (float64, error) {
+	out, err := satExecCommand("ipmitool", "dcmi", "power", "reading").Output()
+	if err != nil {
+		return 0, fmt.Errorf("ipmitool dcmi power reading: %w", err)
+	}
+	for _, line := range strings.Split(string(out), "\n") {
+		if strings.Contains(line, "Current Power") {
+			parts := strings.SplitN(line, ":", 2)
+			if len(parts) == 2 {
+				val := strings.TrimSpace(strings.TrimSuffix(strings.TrimSpace(parts[1]), "Watts"))
+				val = strings.TrimSpace(val)
+				w, err := strconv.ParseFloat(val, 64)
+				if err == nil && w > 0 {
+					return w, nil
+				}
+			}
+		}
+	}
+	return 0, fmt.Errorf("could not parse ipmitool dcmi power reading output")
+}
+
+// sampleIPMIPowerSeries collects IPMI power readings every 2 seconds for
+// durationSec seconds. Returns the mean of all successful samples.
+// Returns 0, false if IPMI is unavailable.
+func sampleIPMIPowerSeries(ctx context.Context, durationSec int) (meanW float64, ok bool) {
+	if durationSec <= 0 {
+		return 0, false
+	}
+	deadline := time.Now().Add(time.Duration(durationSec) * time.Second)
+	var samples []float64
+	for {
+		if w, err := queryIPMIServerPowerW(); err == nil {
+			samples = append(samples, w)
+		}
+		if time.Now().After(deadline) {
+			break
+		}
+		select {
+		case <-ctx.Done():
+			break
+		case <-time.After(2 * time.Second):
+		}
+	}
+	if len(samples) == 0 {
+		return 0, false
+	}
+	var sum float64
+	for _, w := range samples {
+		sum += w
+	}
+	return sum / float64(len(samples)), true
+}
+
+// characterizeServerPower computes BenchmarkServerPower from idle and loaded
+// IPMI samples plus the GPU-reported average power during steady state.
+func characterizeServerPower(idleW, loadedW, gpuReportedSumW float64, ipmiAvailable bool) *BenchmarkServerPower {
+	sp := &BenchmarkServerPower{Available: ipmiAvailable}
+	if !ipmiAvailable {
+		sp.Notes = append(sp.Notes, "IPMI power reading unavailable; server-side power characterization skipped")
+		return sp
+	}
+	sp.IdleW = idleW
+	sp.LoadedW = loadedW
+	sp.DeltaW = loadedW - idleW
+	sp.GPUReportedSumW = gpuReportedSumW
+	if gpuReportedSumW > 0 && sp.DeltaW > 0 {
+		sp.ReportingRatio = sp.DeltaW / gpuReportedSumW
+	}
+	return sp
+}

audit/internal/platform/benchmark_report.go

@@ -56,6 +56,9 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult, charts []benc
 		fmt.Fprintf(&b, "  Status: %s\n", gpu.Status)
 		fmt.Fprintf(&b, "  Composite score: %.2f\n", gpu.Scores.CompositeScore)
 		fmt.Fprintf(&b, "  Compute score: %.2f\n", gpu.Scores.ComputeScore)
+		if gpu.Scores.TOPSPerSMPerGHz > 0 {
+			fmt.Fprintf(&b, "  Compute efficiency: %.3f TOPS/SM/GHz\n", gpu.Scores.TOPSPerSMPerGHz)
+		}
 		fmt.Fprintf(&b, "  Power sustain: %.1f\n", gpu.Scores.PowerSustainScore)
 		fmt.Fprintf(&b, "  Thermal sustain: %.1f\n", gpu.Scores.ThermalSustainScore)
 		fmt.Fprintf(&b, "  Stability: %.1f\n", gpu.Scores.StabilityScore)
@@ -77,13 +80,7 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult, charts []benc
 				}
 			}
 		}
-		fmt.Fprintf(&b, "  Throttle counters (us): sw_power=%d sw_thermal=%d sync_boost=%d hw_thermal=%d hw_power_brake=%d\n",
+		fmt.Fprintf(&b, "  Throttle: %s\n", formatThrottleLine(gpu.Throttle, gpu.Steady.DurationSec))
-			gpu.Throttle.SWPowerCapUS,
-			gpu.Throttle.SWThermalSlowdownUS,
-			gpu.Throttle.SyncBoostUS,
-			gpu.Throttle.HWThermalSlowdownUS,
-			gpu.Throttle.HWPowerBrakeSlowdownUS,
-		)
 		if len(gpu.Notes) > 0 {
 			fmt.Fprintf(&b, "  Notes:\n")
 			for _, note := range gpu.Notes {
@@ -121,6 +118,26 @@ func renderBenchmarkReportWithCharts(result NvidiaBenchmarkResult, charts []benc
 		}
 	}
 
+	if sp := result.ServerPower; sp != nil {
+		fmt.Fprintf(&b, "Server Power (IPMI)\n")
+		fmt.Fprintf(&b, "-------------------\n")
+		if !sp.Available {
+			fmt.Fprintf(&b, "Unavailable\n")
+		} else {
+			fmt.Fprintf(&b, "  Server idle:         %.0f W\n", sp.IdleW)
+			fmt.Fprintf(&b, "  Server under load:   %.0f W\n", sp.LoadedW)
+			fmt.Fprintf(&b, "  Server delta:        %.0f W\n", sp.DeltaW)
+			fmt.Fprintf(&b, "  GPU reported (sum):  %.0f W\n", sp.GPUReportedSumW)
+			if sp.ReportingRatio > 0 {
+				fmt.Fprintf(&b, "  Reporting ratio:     %.2f  (1.0 = accurate, <0.75 = GPU over-reports)\n", sp.ReportingRatio)
+			}
+		}
+		for _, note := range sp.Notes {
+			fmt.Fprintf(&b, "  Note: %s\n", note)
+		}
+		b.WriteString("\n")
+	}
+
 	fmt.Fprintf(&b, "Methodology\n")
 	fmt.Fprintf(&b, "-----------\n")
 	fmt.Fprintf(&b, "- Profile %s uses standardized baseline, warmup, steady-state, interconnect, and cooldown phases.\n", result.BenchmarkProfile)
@@ -175,6 +192,42 @@ func stripANSIEscapeSequences(raw string) string {
 	return ansiEscapePattern.ReplaceAllString(raw, "")
 }
 
+// formatThrottleLine renders throttle counters as human-readable percentages of
+// the steady-state window.  Only non-zero counters are shown.  When the steady
+// duration is unknown (0), raw seconds are shown instead.
+func formatThrottleLine(t BenchmarkThrottleCounters, steadyDurationSec float64) string {
+	type counter struct {
+		label string
+		us    uint64
+	}
+	counters := []counter{
+		{"sw_power", t.SWPowerCapUS},
+		{"sw_thermal", t.SWThermalSlowdownUS},
+		{"sync_boost", t.SyncBoostUS},
+		{"hw_thermal", t.HWThermalSlowdownUS},
+		{"hw_power_brake", t.HWPowerBrakeSlowdownUS},
+	}
+	var parts []string
+	for _, c := range counters {
+		if c.us == 0 {
+			continue
+		}
+		sec := float64(c.us) / 1e6
+		if steadyDurationSec > 0 {
+			pct := sec / steadyDurationSec * 100
+			parts = append(parts, fmt.Sprintf("%s=%.1f%% (%.0fs)", c.label, pct, sec))
+		} else if sec < 1 {
+			parts = append(parts, fmt.Sprintf("%s=%.0fms", c.label, sec*1000))
+		} else {
+			parts = append(parts, fmt.Sprintf("%s=%.1fs", c.label, sec))
+		}
+	}
+	if len(parts) == 0 {
+		return "none"
+	}
+	return strings.Join(parts, "  ")
+}
+
 func renderBenchmarkSummary(result NvidiaBenchmarkResult) string {
 	var b strings.Builder
 	fmt.Fprintf(&b, "run_at_utc=%s\n", result.GeneratedAt.Format(time.RFC3339))

audit/internal/platform/benchmark_types.go

@@ -28,6 +28,7 @@ type NvidiaBenchmarkResult struct {
 	Normalization      BenchmarkNormalization       `json:"normalization"`
 	GPUs               []BenchmarkGPUResult         `json:"gpus"`
 	Interconnect       *BenchmarkInterconnectResult `json:"interconnect,omitempty"`
+	ServerPower        *BenchmarkServerPower        `json:"server_power,omitempty"`
 }
 
 type BenchmarkNormalization struct {
@@ -56,7 +57,10 @@ type BenchmarkGPUResult struct {
 	Backend                string                     `json:"backend,omitempty"`
 	Status                 string                     `json:"status"`
 	PowerLimitW            float64                    `json:"power_limit_w,omitempty"`
+	MultiprocessorCount    int                        `json:"multiprocessor_count,omitempty"`
+	DefaultPowerLimitW     float64                    `json:"default_power_limit_w,omitempty"`
 	MaxGraphicsClockMHz    float64                    `json:"max_graphics_clock_mhz,omitempty"`
+	BaseGraphicsClockMHz   float64                    `json:"base_graphics_clock_mhz,omitempty"`
 	MaxMemoryClockMHz      float64                    `json:"max_memory_clock_mhz,omitempty"`
 	LockedGraphicsClockMHz float64                    `json:"locked_graphics_clock_mhz,omitempty"`
 	LockedMemoryClockMHz   float64                    `json:"locked_memory_clock_mhz,omitempty"`
@@ -117,6 +121,24 @@ type BenchmarkScorecard struct {
 	StabilityScore      float64 `json:"stability_score"`
 	InterconnectScore   float64 `json:"interconnect_score"`
 	CompositeScore      float64 `json:"composite_score"`
+	// TOPSPerSMPerGHz is compute efficiency independent of clock speed and SM count.
+	// Comparable across throttle levels and GPU generations. Low value at normal
+	// clocks indicates silicon degradation.
+	TOPSPerSMPerGHz float64 `json:"tops_per_sm_per_ghz,omitempty"`
+}
+
+// BenchmarkServerPower captures server-side power via IPMI alongside GPU-reported
+// power. The reporting_ratio (delta / gpu_reported_sum) near 1.0 means GPU power
+// telemetry is accurate; a ratio well below 1.0 (e.g. 0.5) means the GPU is
+// over-reporting its power consumption.
+type BenchmarkServerPower struct {
+	Available       bool     `json:"available"`
+	IdleW           float64  `json:"idle_w,omitempty"`
+	LoadedW         float64  `json:"loaded_w,omitempty"`
+	DeltaW          float64  `json:"delta_w,omitempty"`
+	GPUReportedSumW float64  `json:"gpu_reported_sum_w,omitempty"`
+	ReportingRatio  float64  `json:"reporting_ratio,omitempty"`
+	Notes           []string `json:"notes,omitempty"`
 }
 
 type BenchmarkInterconnectResult struct {

bible-local/docs/benchmark-clock-calibration.md

@@ -0,0 +1,248 @@
+# Benchmark clock calibration research
+
+## Status
+In progress. Baseline data from production servers pending.
+
+## Background
+
+The benchmark locks GPU clocks to `MaxGraphicsClockMHz` (boost) via `nvidia-smi -lgc`
+before the steady-state phase. The metric `low_sm_clock_vs_target` fires when
+`avg_steady_clock < locked_target * 0.90`.
+
+Problem: boost clock is the theoretical maximum under ideal cooling. In practice,
+even a healthy GPU in a non-ideal server will sustain clocks well below boost.
+The 90% threshold has no empirical basis.
+
+## Key observations (2026-04-06)
+
+### H100 PCIe — new card, server not designed for it
+- avg clock 1384 MHz, P95 1560 MHz (unstable, proba boost 1755 MHz)
+- Thermal sustain: 0.0 (sw_thermal covers entire steady window)
+- Stability: 70.0 — clocks erratic, no equilibrium found
+- Degradation: power_capped, thermal_limited, low_sm_clock_vs_target, variance_too_high
+
+### H200 NVL — new card, server not designed for it
+- avg clock = P95 = 1635 MHz (perfectly stable)
+- Thermal sustain: 0.0 (sw_thermal + sw_power cover entire steady window)
+- Stability: 92.0 — found stable thermal equilibrium at 1635 MHz
+- Degradation: power_capped, thermal_limited
+- Compute: 989 TOPS — card is computing correctly for its frequency
+
+### Key insight
+The meaningful distinction is not *whether* the card throttles but *how stably*
+it throttles. H200 found a thermal equilibrium (avg == P95, Stability 92),
+H100 did not (avg << P95, Stability 70). Both are new cards; the H100's
+instability may reflect a more severe thermal mismatch or a card issue.
+
+`sw_power ≈ sw_thermal` pattern = server cooling constraint, card likely OK.
+`hw_thermal >> sw_thermal` pattern = card itself overheating, investigate.
+
+## Hypothesis for baseline
+
+After testing on servers designed for their GPUs (proper cooling):
+- Healthy GPU under sustained load will run at a stable fraction of boost
+- Expected: avg_steady ≈ 80–95% of boost depending on model and TDP class
+- Base clock (`clocks.base.gr`) may be a better reference than boost:
+  a healthy card under real workload should comfortably exceed base clock
+
+## Baseline: H100 PCIe HBM2e — designed server (2026-04-06, 10 samples)
+
+Source: external stress test tool, ~90s runs, designed server, adequate power.
+
+### Healthy fingerprint
+
+- **Power**: hits cap ~340–360W immediately, stays flat throughout — HEALTHY
+- **Clock**: starts ~1750 MHz, oscillates and declines to ~1540–1600 MHz by 90s
+  - Avg steady (visual): **~1580–1620 MHz**
+  - vs boost 1755 MHz: **~91–92%**
+  - Oscillation is NORMAL — this is the boost algorithm balancing under power cap
+  - Stable power + oscillating clocks = healthy power-cap behavior
+- **Temperature**: linear rise ~38°C → 75–80°C over 90s (no runaway)
+- **Consistency**: all 10 samples within ±20 MHz — very repeatable
+
+### Characteristic patten
+Flat power line + oscillating/declining clock line = GPU correctly managed by
+power cap algorithm. Do NOT flag this as instability.
+
+### Clock CV implication
+The healthy oscillation WILL produce moderate ClockCVPct (~5–10%).
+The current `variance_too_high` threshold (StabilityScore < 85) may fire on
+healthy HBM2e PCIe cards. Needs recalibration.
+
+---
+
+## Baseline: H100 HBM3 OEM SXM Custom (restored) — 2 confirmed samples
+
+Source: pytorch_training_loop stress test, 120s (90s stress + 30s cooldown).
+Confirmed GPU: NVIDIA H100 80GB HBM3, GH100 rev a1.
+
+### GPU clock reference (from nvidia-smi, idle):
+- base_clock_mhz: **1095**
+- boost_clock_mhz: **1755** (nvidia-smi `clocks.max.graphics` at idle)
+- achieved_max_clock_mhz: **1980** (actual burst max observed by tool)
+- Our benchmark locks to `clocks.max.graphics` = likely 1980 MHz for this chip
+
+### Observed under 700W sustained load (both samples nearly identical):
+- Power: ~700W flat — SXM slot, adequate power confirmed
+- Clock steady range: **~1380–1480 MHz**, avg **~1420–1460 MHz**
+- vs 1980 MHz (lock target): **72–74%** — severely below
+- vs 1755 MHz (nvidia-smi boost): **81–83%**
+- vs 1095 MHz (base): 130% — above base but far below expected for SXM
+- Clock/Watt: ~2.1 MHz/W vs HBM2e ~4.6 MHz/W — 2× worse efficiency
+- Temperature: 38°C → 79–80°C (same rate as HBM2e)
+- Oscillation: present, similar character to HBM2e but at much lower frequency
+
+### Diagnosis
+These restored cards are degraded. A healthy H100 SXM in a designed server
+(DGX H100, HGX H100) should sustain ~1800–1900 MHz at 700W (~91–96% of 1980).
+The 72–74% result is a clear signal of silicon or VRM degradation from the
+refurbishment process.
+
+### Clock pattern note
+Images 8/9 (previously marked as "HBM3 restored") are now confirmed identical
+to images 19/20. Both sample sets show same degraded pattern — same batch.
+
+---
+
+## Baseline matrix (filled where data available)
+
+| GPU model | Config | Avg clock steady | vs boost | Clock/Watt | Notes |
+|---|---|---|---|---|---|
+| H100 PCIe HBM2e | designed server | 1580–1620 MHz | 91–92% | ~4.6 MHz/W | 10 samples, healthy |
+| H100 SXM HBM3 restored | 700W full | 1420–1460 MHz | 72–74% of 1980 | ~2.1 MHz/W | 4 samples confirmed, degraded |
+| H100 SXM HBM3 healthy | designed | ~1800–1900 MHz est. | ~91–96% est. | ~2.7 MHz/W est. | need real baseline |
+| H200 NVL | designed | TBD | TBD | TBD | need baseline |
+
+---
+
+## H100 official spec (from NVIDIA datasheet)
+
+Source: NVIDIA H100 Tensor Core GPU Datasheet (image 23, 2026-04-06).
+All TOPS marked * are with structural sparsity enabled. Divide by 2 for dense.
+
+| Model | FP16 Tensor (dense) | TF32 (dense) | FP8 (dense) | TDP | Memory |
+|---|---|---|---|---|---|
+| H100 80GB PCIe | 756 TFLOPS | 378 TFLOPS | 1,513 TFLOPS | 350W | HBM2e |
+| H100 NVL 94GB PCIe | 990 TFLOPS | 495 TFLOPS | 1,980 TFLOPS | 400W | HBM3 |
+| H100 80GB SXM (BQQV) | 989 TFLOPS | 494 TFLOPS | — | 700W | HBM3 |
+| H100 94GB SXM (BUBB) | 989 TFLOPS | 494 TFLOPS | — | 700W | HBM2e |
+
+Notes:
+- SXM boards do NOT list FP8 peak in this table (field empty)
+- fp8_e5m2 is unsupported on H100 PCIe HBM2e — confirmed in our tests
+- Tensor Cores: PCIe = 456, SXM = 528 (16% more on SXM)
+
+## Observed efficiency (H100 80GB PCIe, throttled server)
+
+From the report in this session (power+thermal throttle throughout steady):
+
+| Precision | Measured | Spec (dense) | % of spec |
+|---|---|---|---|
+| fp16_tensor | 329 TOPS | 756 TFLOPS | 44% |
+| fp32_tf32 | 115 TOPS | 378 TFLOPS | 30% |
+| fp8_e4m3 | 505 TOPS | 1,513 TFLOPS | 33% |
+
+33–44% of spec is expected given sustained power+thermal throttle (avg clock
+1384 MHz vs boost 1755 MHz = 79%). The GPU is computing correctly for its
+actual frequency — the low TOPS comes from throttle, not silicon defect.
+
+## H200 official spec (from NVIDIA datasheet, image 24, 2026-04-06)
+
+Format: without sparsity / with sparsity.
+
+| Model | FP16 Tensor (dense) | TF32 (dense) | FP8 (dense) | TDP | Memory |
+|---|---|---|---|---|---|
+| H200 NVL PCIe | 836 TFLOPS | 418 TFLOPS | 1,570 TFLOPS | 600W | HBM3e 141GB |
+| H200 SXM | 990 TFLOPS | 495 TFLOPS | 1,979 TFLOPS | 700W | HBM3e 141GB |
+
+## Observed efficiency (H200 NVL PCIe, throttled non-designed server)
+
+Avg clock 1635 MHz (62% of boost ~2619 MHz). Entire steady in thermal throttle.
+
+| Precision | Measured | Spec (dense) | % of spec |
+|---|---|---|---|
+| fp16_tensor | 340 TOPS | 836 TFLOPS | 41% |
+| fp32_tf32 | 120 TOPS | 418 TFLOPS | 29% |
+| fp8_e4m3 | 529 TOPS | 1,570 TFLOPS | 34% |
+
+Comparable to H100 PCIe efficiency (33–44%) despite different architecture —
+both are throttle-limited. Confirms that % of spec is not a quality signal,
+it reflects the thermal environment. tops_per_sm_per_ghz is the right metric.
+
+## Real-world GEMM efficiency reference (2026-04-06, web research)
+
+Sources: SemiAnalysis MI300X vs H100 vs H200 training benchmark; cuBLAS optimization
+worklog (hamzaelshafie.bearblog.dev); Lambda AI H100 performance analysis.
+
+### What healthy systems actually achieve:
+- H100 SXM in designed server: **~720 TFLOPS FP16 = ~73% of spec**
+- cuBLAS large square GEMM (8192³): up to **~83% flop utilization**
+- H200 NVL PCIe: no public data, extrapolating ~73% → ~610 TFLOPS FP16
+
+### Our results vs expectation:
+| GPU | Our FP16 | Expected (73%) | Our % of spec | Gap |
+|---|---|---|---|---|
+| H100 PCIe HBM2e | 329 TOPS | ~552 TFLOPS | 44% | ~1.7× below |
+| H200 NVL PCIe | 340 TOPS | ~610 TFLOPS | 41% | ~1.8× below |
+
+Our results are roughly **half** of what a healthy system achieves even under throttle.
+This is NOT normal — 30-44% is not the industry baseline.
+
+### Likely causes of the gap (in order of probability):
+1. **Thermal throttle** — confirmed, sw_thermal covers entire steady window
+2. **Power limit below TDP** — GPU may be software-limited below 350W/600W.
+   Previous user may have set a lower limit via nvidia-smi -pl and it was not
+   reset. Our normalization sets clock locks but does NOT reset power limit.
+   Key check: `nvidia-smi -q | grep "Power Limit"` — default vs enforced.
+3. **Matrix size** — ruled out. bee-gpu-burn uses 4096×4096×4096 for fp16,
+   8192×8192×4096 for fp8. These are large enough for peak tensor utilization.
+
+### Power limit gap analysis (H100 PCIe):
+- Avg clock 1384 MHz = 79% of boost 1755 MHz
+- Expected TOPS at 79% clock: 756 × 0.79 ≈ 597 TFLOPS
+- Actually measured: 329 TOPS = 55% of that estimate
+- Remaining gap after accounting for clock throttle: ~45%
+- Most likely explanation: enforced power limit < 350W TDP, further reducing
+  sustainable clock beyond what sw_thermal alone would cause.
+
+### Action item:
+Add `power.limit` (enforced) AND `power.default_limit` to queryBenchmarkGPUInfo
+so result.json shows if the card was pre-configured with a non-default limit.
+If enforced < default × 0.95 → add finding "GPU power limit is below default TDP".
+
+### CPU/RAM impact on GPU FLOPS:
+None. Pure on-GPU GEMM is fully compute-bound once data is in VRAM.
+CPU core count and host RAM are irrelevant.
+
+## Compute efficiency metric (proposed, no hardcode)
+
+Instead of comparing TOPS to a hardcoded spec, compute:
+  tops_per_sm_per_ghz = measured_tops / (sm_count × avg_clock_ghz)
+
+This is model-agnostic. A GPU computing correctly at its actual frequency
+will show a consistent tops_per_sm_per_ghz regardless of throttle level.
+A GPU with degraded silicon will show low tops_per_sm_per_ghz even at
+normal clocks.
+
+SM count is queryable: nvidia-smi --query-gpu=attribute.multiprocessor_count
+(needs to be added to queryBenchmarkGPUInfo).
+
+Reference values to establish after baseline runs:
+- H100 PCIe fp16_tensor: TBD tops/SM/GHz
+- H100 SXM fp16_tensor: TBD tops/SM/GHz
+
+## Proposed threshold changes (pending more data)
+
+1. **`low_sm_clock_vs_target`**: raise threshold from 90% to 85% based on observed
+   91–92% on healthy HBM2e. Or remove entirely — sw_power/sw_thermal already
+   capture the root cause.
+
+2. **`variance_too_high`** (StabilityScore < 85): healthy HBM2e WILL oscillate
+   under power cap. Consider suppressing this flag when power is flat and usage
+   is 100% (oscillation is expected). Or lower threshold to 70.
+
+3. **New signal: MHz/Watt efficiency**: if base_graphics_clock_mhz is available,
+   ratio avg_clock / power_w could identify degraded silicon (HBM3 restored S1
+   would have been caught by this).
+
+Decision deferred until baseline on SXM designed servers collected.

iso/builder/bee-gpu-stress.c

@@ -606,6 +606,20 @@ struct prepared_profile {
 };
 
 static const struct profile_desc k_profiles[] = {
+    {
+        "fp64",
+        "fp64",
+        80,
+        1,
+        0,
+        0,
+        8,
+        CUDA_R_64F,
+        CUDA_R_64F,
+        CUDA_R_64F,
+        CUDA_R_64F,
+        CUBLAS_COMPUTE_64F,
+    },
     {
         "fp32_tf32",
         "fp32",