feat(report): update extrapolation parameters and scoring in README

- revise hardware prices and parameters in extrapolate.py - enhance scoring details in README for task 11 - clarify projected scoreboard values and sources - improve chart readability and scoring methodology in report.py - add new utility functions for formatting and reading hardware prices
2026-07-11 23:57:41 -04:00
parent af91629f71
commit 35d2bdadbd
4 changed files with 275 additions and 147 deletions
--- a/README.md
+++ b/README.md
@@ -240,26 +240,30 @@ D4 tribometer session sequence, D5 execution loop, D6 data hierarchy.
 (Images appear after running the full pipeline; task 11 writes them to
 `out/report/charts/` under exactly these names.)

-### Final scoreboard (task 11) - to be generated
+### Final scoreboard (task 11)

 Weighted scoring, max 80 points: search speed x5 (0-50), RAM economy x2
-(0-20), disk economy x1 (0-10); TIMEOUT/FAIL projects to 0.
+(0-20), disk economy x1 (0-10); a FAIL projection at 600 GB zeroes the
+search subscore. Values below are the projected-600 GB scoreboard from the
+2026-07-11 pipeline run (seed 20260711); the generated source of truth is
+`out/report/tables/t5_weighted_scores.csv`, and REPORT.md also carries the
+measured-scale scoreboard.

 | Format | Search (0-50) | RAM (0-20) | Disk (0-10) | Total (0-80) |
 |---|---|---|---|---|
-| CSV | TBD | TBD | TBD | TBD |
-| JSON-LD | TBD | TBD | TBD | TBD |
-| SQLite | TBD | TBD | TBD | TBD |
-| PostgreSQL | TBD | TBD | TBD | TBD |
-| RDF | TBD | TBD | TBD | TBD |
+| CSV | 0.6 | 20.0 | 8.3 | 28.9 |
+| JSON-LD | 0.1 | 20.0 | 4.0 | 24.0 |
+| SQLite | 21.8 | 15.2 | 10.0 | 47.0 |
+| PostgreSQL | 50.0 | 5.5 | 3.1 | 58.6 |
+| RDF | 0.0 | 0.2 | 0.7 | 0.9 |

 | Use case | Recommended format |
 |---|---|
-| Analysis | TBD |
-| Report generation | TBD |
-| Search / filtering | TBD |
-| Archiving | TBD |
-| Inter-lab exchange | TBD |
+| Analysis | PostgreSQL (SQLite acceptable single-user up to ~600 GB) |
+| Report generation | PostgreSQL (track_summary materialized view) |
+| Search / filtering | PostgreSQL or SQLite (indexed) |
+| Archiving | CSV tree + tar.gz, plus pg_dump of the system of record |
+| Inter-lab exchange | Hybrid JSON-LD (metadata + sourceFile links to CSV) |

 ## Conventions

--- a/docs/rules/code-python-style.md
+++ b/docs/rules/code-python-style.md
@@ -30,7 +30,7 @@ The stdlib is the default. The ONLY permitted third-party packages:
 | Package | Used by | For |
 |---|---|---|
 | `numpy` | 03 | vectorized generation, seeded RNG |
-| `PyYAML` | 01, 03, 10 | reading/writing the YAML configs |
+| `PyYAML` | 01, 03, 10, 11 | reading/writing the YAML configs |
 | `rdflib` | 04, 07 | JSON-LD validation, RDF serialization |
 | `oxigraph` | 07, 09 | embedded queryable triplestore |
 | `ijson` | 08, 09 | streaming JSON parsing |
--- a/extrapolate.py
+++ b/extrapolate.py
@@ -79,15 +79,29 @@ LAWS = {
 	("csv", 7): "scan", ("json", 7): "scan", ("sqlite", 7): "scan", ("pg", 7): "scan_parallel", ("rdf", 7): "scan",
 }

-HW_PRICES_DEFAULT = """# Hardware cost parameters for extrapolation (task 10) - June 2026 defaults.
+HW_PRICES_DEFAULT = """# Hardware cost parameters for extrapolation (task 10).
+# Street prices retrieved 2026-07-11 from the sources below (server DRAM is
+# in a documented 2025-2026 price surge - see market_context). The cost
+# model buys whole components: 64 GB RDIMM modules and 7.68 TB NVMe drives.
 # Operator-editable; re-run extrapolate.py after changes (code-config-yaml).
-ram_usd_per_gb: 14.0
-nvme_usd_per_tb: 250.0
-server_16_core_usd: 6000.0
-server_32_core_usd: 10000.0
-extra_node_usd: 6000.0
+as_of: "2026-07-11"
+ram_module_gb: 64
+ram_module_usd: 1887.0       # A-Tech 64GB (2x32GB) DDR5-5600 ECC RDIMM
+nvme_drive_tb: 7.68
+nvme_drive_usd: 3995.0       # Cloud Ninjas NEW 7.68TB NVMe U.2 1DWPD (Dell 14-16G)
+server_16_core_usd: 4618.0   # Supermicro AS-1015CS-TNR 1U (EPYC 9004/9005), Broadberry starting config
+server_32_core_usd: 6272.0   # Supermicro AS-1115CS-TNR 1U, Broadberry starting config
+extra_node_usd: 4618.0       # one additional entry 1U node (AS-1015CS-TNR chassis)
+sources:
+  ram: https://atechmemory.com/collections/ddr5-memory-ram
+  nvme: https://cloudninjas.com/products/new-7-68tb-nvme-u-2-1dwpd-sie-2-5-enterprise-solid-state-drive-for-14th-15th-16th-gen-dell
+  servers: https://www.broadberry.com/amd-epyc-9004-supermicro-servers
+  market_context: https://www.techpowerup.com/342331/server-dram-pricing-jumps-50-only-70-of-orders-getting-filled
 """
-HW_PRICE_KEYS = ("ram_usd_per_gb", "nvme_usd_per_tb", "server_16_core_usd", "server_32_core_usd", "extra_node_usd")
+HW_PRICE_KEYS = (
+	"as_of", "ram_module_gb", "ram_module_usd", "nvme_drive_tb", "nvme_drive_usd",
+	"server_16_core_usd", "server_32_core_usd", "extra_node_usd", "sources",
+)


 def read_medians(path: Path) -> dict[tuple[str, int], float]:
@@ -222,7 +236,8 @@ def main() -> int:

 		with open(bench_dir / "hardware_sizing.csv", "w", encoding="ascii", newline="") as fh:
 			writer = csv.writer(fh)
-			writer.writerow(["format", "scale_gb", "disk_tb", "ram_gb", "cores", "nodes", "est_cost_usd"])
+			writer.writerow(["format", "scale_gb", "disk_tb", "ram_gb", "cores", "nodes",
+				"est_cost_usd", "ram_modules", "nvme_drives", "base_usd", "ram_usd", "disk_usd", "extra_nodes_usd"])
 			for scale_gb in SCALES_GB:
 				r = scale_gb * 1e9 / csv_bytes
 				for fmt in FORMATS:
@@ -238,11 +253,17 @@ def main() -> int:
 						ram_gb = max(MIN_RAM_GB, RDF_HOT_FRACTION * fmt_bytes / 1e9)
 					cores = pick_pg_cores(medians, overhead, r, n0) if fmt == "pg" else CORE_OPTIONS[0]
 					nodes = max(1, math.ceil(ram_gb / NODE_RAM_CEILING_GB))
-					server_usd = prices["server_16_core_usd"] if cores == 16 else prices["server_32_core_usd"]
-					cost = (server_usd + ram_gb * prices["ram_usd_per_gb"]
-						+ disk_tb * prices["nvme_usd_per_tb"]
-						+ (nodes - 1) * prices["extra_node_usd"])
-					writer.writerow([fmt, scale_gb, round(disk_tb, 3), round(ram_gb, 1), cores, nodes, round(cost)])
+					# bill of materials: whole RDIMM modules and whole NVMe drives
+					ram_modules = math.ceil(ram_gb / prices["ram_module_gb"])
+					nvme_drives = math.ceil(disk_tb / prices["nvme_drive_tb"])
+					base_usd = prices["server_16_core_usd"] if cores == 16 else prices["server_32_core_usd"]
+					ram_usd = ram_modules * prices["ram_module_usd"]
+					disk_usd = nvme_drives * prices["nvme_drive_usd"]
+					extra_nodes_usd = (nodes - 1) * prices["extra_node_usd"]
+					cost = base_usd + ram_usd + disk_usd + extra_nodes_usd
+					writer.writerow([fmt, scale_gb, round(disk_tb, 3), round(ram_gb, 1), cores, nodes,
+						round(cost), ram_modules, nvme_drives,
+						round(base_usd), round(ram_usd), round(disk_usd), round(extra_nodes_usd)])

 		entries = {
 			"extrapolation_rows": len(SCALES_GB) * len(FORMATS) * len(QUERY_NUMBERS),
@@ -252,6 +273,7 @@ def main() -> int:
 			"flags_fail": flags["FAIL"],
 			"pg_index_share": round(index_share, 3),
 			"pg_index_mb": round(pg_index_bytes / 1048576, 1),
+			"prices_as_of": prices["as_of"],
 			"note_pandas": "pandas CSV variant not measured; its RAM is O(n) - infeasible above ~10 GB (spec 10)",
 		}
 		for fmt in FORMATS:
--- a/report.py
+++ b/report.py
@@ -2,13 +2,21 @@

 Aggregates the measured matrix (task 09), the projections (task 10) and the
 process diagrams (task 02) into the final decision document. Writes 14 PNG
-charts (150 dpi, log scales where ranges span decades) under
-./out/report/charts/ with the exact names fixed in README.md, five CSV
-tables under ./out/report/tables/, and ./out/report/REPORT.md. Weighted
-scoring: search speed x5 (0-50), RAM economy x2 (0-20), disk economy x1
-(0-10), max 80; each subscore is 10 x best/value; a format with a FAIL
-projection at 600 GB scores 0 on search there (spec 11). Projected numbers
-are always labeled as projected (bench-methodology section 7).
+charts (150 dpi) under ./out/report/charts/ with the exact names fixed in
+README.md, five CSV tables under ./out/report/tables/, and
+./out/report/REPORT.md.
+
+Chart readability contract (owner feedback 2026-07-11): linear scales with
+actual units everywhere (panels instead of log axes when magnitudes clash),
+no scientific tick notation, full format names, a value label on every bar
+and point, titles that state the takeaway. Hardware costs are a discrete
+bill of materials (whole RDIMM modules, whole NVMe drives, priced chassis)
+with street-price sources and an as-of date from out/config/hw_prices.yaml.
+
+Weighted scoring: search speed x5 (0-50), RAM economy x2 (0-20), disk
+economy x1 (0-10), max 80; each subscore is 10 x best/value; a format with
+a FAIL projection at 600 GB scores 0 on search there (spec 11). Projected
+numbers are always labeled as projected (bench-methodology section 7).
 Spec: docs/specs/11_reporting.md.
 """

@@ -24,6 +32,8 @@ import matplotlib

 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
+import yaml
+from matplotlib.ticker import FuncFormatter

 from common.pipeline import (
 	check_dependencies,
@@ -42,12 +52,12 @@ FORMATS = ("csv", "json", "sqlite", "pg", "rdf")
 QUERY_NUMBERS = tuple(range(1, 8))
 SCALES_GB = (600, 1200, 6000)
 REPORT_SCALE_GB = 600  # the headline projection scale, spec 11
+SCALE_LABELS = {600: "600 GB", 1200: "1.2 TB", 6000: "6 TB"}

 WEIGHT_SEARCH = 5  # x5 -> 0-50, spec 11
 WEIGHT_RAM = 2     # x2 -> 0-20
 WEIGHT_DISK = 1    # x1 -> 0-10

-# archival (compressed) variant per format for table 1
 ARCHIVAL_VARIANT = {
 	"csv": ("csv", "targz"), "json": ("json", "targz"), "sqlite": ("sqlite", "db_gz"),
 	"pg": ("pg", "dump"), "rdf": ("rdf", "nt_gz"),
@@ -57,12 +67,19 @@ WORKING_VARIANT = {
 	"pg": ("pg", "live"), "rdf": ("rdf", "store"),
 }

-# query classes for the degradation curves (C4), spec 11
+# query classes for the degradation charts (C4), spec 11
 QUERY_CLASSES = {
 	"point_read": (1,),
 	"indexed": (2, 3, 4, 6),
 	"full_scan": (5, 7),
 }
+CLASS_LABELS = {"point_read": "point read", "indexed": "indexed / join", "full_scan": "full scan"}
+
+QUERY_LABELS = {
+	1: "single-track COF curve", 2: "coupons at Au 10+/-0.5 wt%",
+	3: "hardness vs steady-state COF", 4: "anomaly filter",
+	5: "run-in over raw cycles", 6: "wear volume vs load", 7: "Stribeck aggregation",
+}

 DIAGRAM_TITLES = {
 	"D1": "Coupon assembly", "D2": "Characterization and batch assembly",
@@ -72,6 +89,7 @@ DIAGRAM_TITLES = {

 FMT_COLORS = {"csv": "tab:gray", "json": "tab:orange", "sqlite": "tab:green", "pg": "tab:blue", "rdf": "tab:red"}
 FMT_NAMES = {"csv": "CSV", "json": "JSON-LD", "sqlite": "SQLite", "pg": "PostgreSQL", "rdf": "RDF triplestore"}
+CHASSIS = {16: "Supermicro AS-1015CS-TNR", 32: "Supermicro AS-1115CS-TNR"}


 def read_medians(path: Path) -> dict[tuple[str, int], dict]:
@@ -117,10 +135,18 @@ def read_sizing(path: Path) -> dict[tuple[str, int], dict]:
 				"disk_tb": float(row["disk_tb"]), "ram_gb": float(row["ram_gb"]),
 				"cores": int(row["cores"]), "nodes": int(row["nodes"]),
 				"est_cost_usd": float(row["est_cost_usd"]),
+				"ram_modules": int(row["ram_modules"]), "nvme_drives": int(row["nvme_drives"]),
+				"base_usd": float(row["base_usd"]), "ram_usd": float(row["ram_usd"]),
+				"disk_usd": float(row["disk_usd"]), "extra_nodes_usd": float(row["extra_nodes_usd"]),
 			}
 	return sizing


+def read_hw_prices(path: Path) -> dict:
+	with open(path, encoding="ascii") as fh:
+		return yaml.safe_load(fh)
+
+
 def geometric_mean(values: list[float]) -> float:
 	return math.exp(sum(math.log(v) for v in values) / len(values))

@@ -133,7 +159,7 @@ def md_table(headers: list[str], rows: list[list]) -> str:

 def human_time(seconds: float) -> str:
 	if seconds < 120:
-		return f"{seconds:.2f} s"
+		return f"{seconds:.2f} s" if seconds < 10 else f"{seconds:.0f} s"
 	if seconds < 2 * 3600:
 		return f"{seconds / 60:.1f} min"
 	if seconds < 2 * 86400:
@@ -141,6 +167,17 @@ def human_time(seconds: float) -> str:
 	return f"{seconds / 86400:.1f} d"


+def human_size(nbytes: float) -> str:
+	for unit, div in (("TB", 1e12), ("GB", 1e9), ("MB", 1e6)):
+		if nbytes >= div:
+			return f"{nbytes / div:.2f} {unit}" if nbytes < 10 * div else f"{nbytes / div:.0f} {unit}"
+	return f"{nbytes / 1e3:.0f} KB"
+
+
+def money(value: float) -> str:
+	return f"${value:,.0f}"
+
+
 def write_csv(path: Path, headers: list[str], rows: list[list]) -> None:
 	with open(path, "w", encoding="ascii", newline="") as fh:
 		writer = csv.writer(fh)
@@ -159,7 +196,6 @@ def scores_for(metrics: dict[str, dict[str, float]], failed_search: set[str]) ->
 		ram = 10.0 * best["ram"] / m["ram"]
 		disk = 10.0 * best["disk"] / m["disk"]
 		out[fmt] = {
-			"search_sub": round(search, 2), "ram_sub": round(ram, 2), "disk_sub": round(disk, 2),
 			"search_pts": round(search * WEIGHT_SEARCH, 1),
 			"ram_pts": round(ram * WEIGHT_RAM, 1),
 			"disk_pts": round(disk * WEIGHT_DISK, 1),
@@ -193,40 +229,60 @@ def build_scoreboards(medians, proj, sizes, sizing) -> dict[str, dict[str, dict]


 # --- charts ---------------------------------------------------------------
+# Shared conventions: linear axes, real units, a value label on every bar
+# and point, full format names, no scientific notation.

-def _grouped_bars(ax, labels, series: dict[str, list[float]]) -> None:
-	width = 0.8 / len(series)
-	for i, (name, values) in enumerate(series.items()):
-		x = [j + i * width for j in range(len(labels))]
-		ax.bar(x, values, width=width, label=name)
-	ax.set_xticks([j + 0.4 - width / 2 for j in range(len(labels))])
-	ax.set_xticklabels(labels)
-	ax.set_yscale("log")
-	ax.legend()
-	ax.grid(axis="y", alpha=0.3)
+def _vbar_panel(ax, values: dict[str, float], labels: dict[str, str], unit: str, title: str) -> None:
+	bars = ax.bar(range(len(FORMATS)), [values[f] for f in FORMATS], color=[FMT_COLORS[f] for f in FORMATS])
+	for bar, fmt in zip(bars, FORMATS):
+		ax.annotate(labels[fmt], (bar.get_x() + bar.get_width() / 2, bar.get_height()),
+			ha="center", va="bottom", fontsize=8)
+	ax.set_xticks(range(len(FORMATS)))
+	ax.set_xticklabels([FMT_NAMES[f] for f in FORMATS], rotation=12, fontsize=8)
+	ax.set_ylabel(unit)
+	ax.set_title(title, fontsize=10)
+	ax.margins(y=0.18)
+	ax.grid(axis="y", alpha=0.25)


-def chart_c1(charts_dir, sizes, proj_bytes) -> None:
-	fig, ax = plt.subplots(figsize=(7, 4.5))
-	_grouped_bars(ax, list(FORMATS), {
-		"measured (150 MB corpus)": [sizes[WORKING_VARIANT[fmt]] / 1e6 for fmt in FORMATS],
-		"projected @600 GB": [proj_bytes[fmt] / 1e6 for fmt in FORMATS],
-	})
-	ax.set_ylabel("working footprint, MB (log)")
-	ax.set_title("C1 - disk footprint per format")
+def _hbar_panel(ax, values: dict[str, float], labels: dict[str, str], title: str, names: bool) -> None:
+	y = range(len(FORMATS))
+	bars = ax.barh(list(y), [values[f] for f in FORMATS], color=[FMT_COLORS[f] for f in FORMATS])
+	for bar, fmt in zip(bars, FORMATS):
+		ax.annotate(" " + labels[fmt], (bar.get_width(), bar.get_y() + bar.get_height() / 2),
+			va="center", fontsize=8)
+	ax.set_yticks(list(y))
+	ax.set_yticklabels([FMT_NAMES[f] for f in FORMATS] if names else [""] * len(FORMATS), fontsize=8)
+	ax.invert_yaxis()
+	ax.set_xticks([])  # linear axis; the value labels on the bars carry the numbers
+	ax.margins(x=0.30)
+	ax.set_title(title, fontsize=10)
+
+
+def chart_c1(charts_dir, sizes, proj_bytes, coeff) -> None:
+	fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))
+	measured = {f: sizes[WORKING_VARIANT[f]] for f in FORMATS}
+	_vbar_panel(axes[0], {f: measured[f] / 1e9 for f in FORMATS},
+		{f: human_size(measured[f]) for f in FORMATS}, "GB (linear)", "measured on the 150 MB corpus")
+	_vbar_panel(axes[1], {f: proj_bytes[f] / 1e9 for f in FORMATS},
+		{f: human_size(proj_bytes[f]) for f in FORMATS}, "GB (linear)", "projected at 600 GB of raw data")
+	fig.suptitle(f"C1 - disk footprint: RDF stores the same data in {coeff['rdf']:.0f}x the raw CSV size, SQLite in {coeff['sqlite']:.2f}x")
 	fig.tight_layout()
 	fig.savefig(charts_dir / "c1_disk_footprint.png", dpi=DPI)
 	plt.close(fig)


 def chart_c2(charts_dir, medians, sizing) -> None:
-	fig, ax = plt.subplots(figsize=(7, 4.5))
-	_grouped_bars(ax, list(FORMATS), {
-		"measured client peak RSS, MB": [max(medians[(fmt, qn)]["peak_rss_mb"] for qn in QUERY_NUMBERS) for fmt in FORMATS],
-		"recommended RAM @600 GB, MB (projected)": [sizing[(fmt, REPORT_SCALE_GB)]["ram_gb"] * 1024 for fmt in FORMATS],
-	})
-	ax.set_ylabel("MB (log)")
-	ax.set_title("C2 - RAM requirement per format")
+	fig, axes = plt.subplots(1, 2, figsize=(11, 4.5))
+	rss = {f: max(medians[(f, qn)]["peak_rss_mb"] for qn in QUERY_NUMBERS) for f in FORMATS}
+	ram = {f: sizing[(f, REPORT_SCALE_GB)]["ram_gb"] for f in FORMATS}
+	_vbar_panel(axes[0], rss, {f: f"{rss[f]:.0f} MB" for f in FORMATS},
+		"MB (linear)", "measured client peak RSS on 150 MB")
+	_vbar_panel(axes[1], ram, {f: f"{ram[f]:.0f} GB" for f in FORMATS},
+		"GB (linear)", "recommended server RAM at 600 GB (projected)")
+	axes[1].axhline(1024, color="gray", linestyle="--", linewidth=1)
+	axes[1].annotate("single-node ceiling: 1 TB RAM", (0, 1024), fontsize=8, va="bottom")
+	fig.suptitle("C2 - RAM: only the RDF hot set outgrows a single 1 TB node at 600 GB")
 	fig.tight_layout()
 	fig.savefig(charts_dir / "c2_ram_requirement.png", dpi=DPI)
 	plt.close(fig)
@@ -234,82 +290,89 @@ def chart_c2(charts_dir, medians, sizing) -> None:

 def chart_c3(charts_dir, medians, proj) -> None:
 	for qn in QUERY_NUMBERS:
-		fig, ax = plt.subplots(figsize=(7, 4.5))
-		_grouped_bars(ax, list(FORMATS), {
-			"measured (150 MB)": [medians[(fmt, qn)]["wall_s"] for fmt in FORMATS],
-			"projected @600 GB": [proj[(fmt, qn, REPORT_SCALE_GB)][0] for fmt in FORMATS],
-		})
-		ax.set_ylabel("wall time, s (log)")
-		ax.set_title(f"C3 - Q{qn} wall time per format")
+		fig, axes = plt.subplots(1, 2, figsize=(11, 3.6))
+		measured = {f: medians[(f, qn)]["wall_s"] for f in FORMATS}
+		projected = {f: proj[(f, qn, REPORT_SCALE_GB)][0] for f in FORMATS}
+		_hbar_panel(axes[0], measured, {f: human_time(measured[f]) for f in FORMATS},
+			"measured on 150 MB", names=True)
+		_hbar_panel(axes[1], projected, {f: human_time(projected[f]) for f in FORMATS},
+			"projected at 600 GB", names=False)
+		fastest = min(FORMATS, key=lambda f: projected[f])
+		slowest = max(FORMATS, key=lambda f: projected[f])
+		fig.suptitle(f"C3 - Q{qn} ({QUERY_LABELS[qn]}) at 600 GB: {FMT_NAMES[fastest]} {human_time(projected[fastest])}"
+			f" vs {FMT_NAMES[slowest]} {human_time(projected[slowest])}")
 		fig.tight_layout()
 		fig.savefig(charts_dir / f"c3_q{qn}_wall_time.png", dpi=DPI)
 		plt.close(fig)


-def chart_c4(charts_dir, medians, proj, measured_gb: float) -> None:
+def chart_c4(charts_dir, medians, proj, measured_label: str) -> None:
 	for cls, queries in QUERY_CLASSES.items():
-		fig, ax = plt.subplots(figsize=(7, 4.5))
-		for fmt in FORMATS:
-			x = [measured_gb, *SCALES_GB]
-			y = [geometric_mean([medians[(fmt, qn)]["wall_s"] for qn in queries])]
-			y += [geometric_mean([proj[(fmt, qn, s)][0] for qn in queries]) for s in SCALES_GB]
-			ax.plot(x, y, marker="o", label=fmt, color=FMT_COLORS[fmt])
-		ax.set_xscale("log")
-		ax.set_yscale("log")
-		ax.axhline(3600, color="gray", linestyle="--", linewidth=1)
-		ax.text(measured_gb, 3600, "IMPRACTICAL (1 h)", fontsize=7, va="bottom")
-		ax.set_xlabel("raw data size, GB (log); first point measured, rest projected")
-		ax.set_ylabel("wall time, s (log)")
+		fig, axes = plt.subplots(1, 1 + len(SCALES_GB), figsize=(13, 3.6))
+		measured = {f: geometric_mean([medians[(f, qn)]["wall_s"] for qn in queries]) for f in FORMATS}
+		_hbar_panel(axes[0], measured, {f: human_time(measured[f]) for f in FORMATS}, measured_label, names=True)
+		for ax, scale in zip(axes[1:], SCALES_GB):
+			values = {f: geometric_mean([proj[(f, qn, scale)][0] for qn in queries]) for f in FORMATS}
+			_hbar_panel(ax, values, {f: human_time(values[f]) for f in FORMATS},
+				f"projected {SCALE_LABELS[scale]}", names=False)
 		queries_label = ", ".join(f"Q{qn}" for qn in queries)
-		ax.set_title(f"C4 - degradation, {cls.replace('_', ' ')} ({queries_label})")
-		ax.legend()
-		ax.grid(alpha=0.3)
+		fig.suptitle(f"C4 - {CLASS_LABELS[cls]} queries ({queries_label}): wall time growth by data size"
+			" (every panel after the first is projected; linear scale within each panel)")
 		fig.tight_layout()
 		fig.savefig(charts_dir / f"c4_degradation_{cls}.png", dpi=DPI)
 		plt.close(fig)


-def chart_c5(charts_dir, scoreboards) -> None:
+def chart_c5(charts_dir, scoreboards, winner: str) -> None:
 	fig, axes = plt.subplots(1, 2, figsize=(11, 4.5), sharey=True)
 	for ax, (title, board) in zip(axes, [
 		("measured scale (150 MB)", scoreboards["measured"]),
-		("projected @600 GB", scoreboards["projected_600gb"]),
+		("projected at 600 GB", scoreboards["projected_600gb"]),
 	]):
 		x = range(len(FORMATS))
 		search = [board[fmt]["search_pts"] for fmt in FORMATS]
 		ram = [board[fmt]["ram_pts"] for fmt in FORMATS]
 		disk = [board[fmt]["disk_pts"] for fmt in FORMATS]
-		ax.bar(x, search, label=f"search speed x{WEIGHT_SEARCH}")
-		ax.bar(x, ram, bottom=search, label=f"RAM economy x{WEIGHT_RAM}")
-		ax.bar(x, disk, bottom=[s + r for s, r in zip(search, ram)], label=f"disk economy x{WEIGHT_DISK}")
+		ax.bar(x, search, label=f"search speed x{WEIGHT_SEARCH} (0-50)")
+		ax.bar(x, ram, bottom=search, label=f"RAM economy x{WEIGHT_RAM} (0-20)")
+		ax.bar(x, disk, bottom=[s + r for s, r in zip(search, ram)], label=f"disk economy x{WEIGHT_DISK} (0-10)")
 		for i, fmt in enumerate(FORMATS):
-			ax.text(i, board[fmt]["total"] + 1, f"{board[fmt]['total']:.0f}", ha="center", fontsize=8)
+			ax.text(i, board[fmt]["total"] + 1.5, f"{board[fmt]['total']:.0f}", ha="center", fontsize=9)
 		ax.set_xticks(list(x))
-		ax.set_xticklabels(FORMATS)
+		ax.set_xticklabels([FMT_NAMES[f] for f in FORMATS], rotation=12, fontsize=8)
 		ax.set_ylim(0, 85)
-		ax.set_title(title)
-		ax.grid(axis="y", alpha=0.3)
-	axes[0].set_ylabel("weighted score (max 80)")
+		ax.set_title(title, fontsize=10)
+		ax.grid(axis="y", alpha=0.25)
+	axes[0].set_ylabel("weighted score (linear, max 80)")
 	axes[0].legend(loc="upper left", fontsize=8)
-	fig.suptitle("C5 - weighted score, stacked components")
+	board600 = scoreboards["projected_600gb"]
+	fig.suptitle(f"C5 - weighted score (max 80): {FMT_NAMES[winner]} leads the 600 GB scoreboard with {board600[winner]['total']:.0f} points")
 	fig.tight_layout()
 	fig.savefig(charts_dir / "c5_weighted_score.png", dpi=DPI)
 	plt.close(fig)


 def chart_c6(charts_dir, proj, sizing) -> None:
-	fig, ax = plt.subplots(figsize=(7, 4.5))
+	fig, ax = plt.subplots(figsize=(9, 5.5))
+	offsets = {"csv": (12, 0), "json": (12, -4), "sqlite": (12, 10), "pg": (12, -16), "rdf": (-40, -20)}
+	geo = {f: geometric_mean([proj[(f, qn, REPORT_SCALE_GB)][0] for qn in QUERY_NUMBERS]) for f in FORMATS}
+	cost = {f: sizing[(f, REPORT_SCALE_GB)]["est_cost_usd"] for f in FORMATS}
 	for fmt in FORMATS:
-		cost = sizing[(fmt, REPORT_SCALE_GB)]["est_cost_usd"]
-		geo = geometric_mean([proj[(fmt, qn, REPORT_SCALE_GB)][0] for qn in QUERY_NUMBERS])
-		ax.scatter(cost, geo, s=60, color=FMT_COLORS[fmt])
-		ax.annotate(fmt, (cost, geo), textcoords="offset points", xytext=(6, 4))
-	ax.set_xscale("log")
-	ax.set_yscale("log")
-	ax.set_xlabel("estimated hardware cost @600 GB, USD (log)")
-	ax.set_ylabel("geometric mean projected wall time Q1-Q7, s (log)")
-	ax.set_title("C6 - cost vs performance @600 GB (projected)")
-	ax.grid(alpha=0.3)
+		ax.scatter(cost[fmt], geo[fmt] / 60, s=70, color=FMT_COLORS[fmt])
+		ax.annotate(f"{FMT_NAMES[fmt]}: {money(cost[fmt])}, {human_time(geo[fmt])}",
+			(cost[fmt], geo[fmt] / 60), textcoords="offset points", xytext=offsets[fmt], fontsize=8)
+	ax.xaxis.set_major_formatter(FuncFormatter(lambda v, _: f"${v / 1000:.0f}k"))
+	ax.yaxis.set_major_formatter(FuncFormatter(lambda v, _: f"{v:.0f} min"))
+	ax.set_xlabel("estimated hardware cost at 600 GB (bill of materials, linear)")
+	ax.set_ylabel("typical query time at 600 GB, projected (geometric mean Q1-Q7, linear)")
+	ax.annotate("better: lower-left", xy=(0.05, 0.04), xytext=(0.42, 0.10),
+		xycoords="axes fraction", textcoords="axes fraction",
+		fontsize=10, color="darkgreen", fontweight="bold",
+		arrowprops={"arrowstyle": "->", "color": "darkgreen"})
+	ax.margins(x=0.18, y=0.12)
+	ax.grid(alpha=0.25)
+	fastest = min(FORMATS, key=lambda f: geo[f])
+	ax.set_title(f"C6 - cost vs speed at 600 GB (projected): {FMT_NAMES[fastest]} is the fastest at {money(cost[fastest])}")
 	fig.tight_layout()
 	fig.savefig(charts_dir / "c6_cost_vs_performance.png", dpi=DPI)
 	plt.close(fig)
@@ -319,17 +382,26 @@ def chart_c6(charts_dir, proj, sizing) -> None:

 def scoreboard_rows(board: dict[str, dict]) -> list[list]:
 	ranked = sorted(FORMATS, key=lambda f: -board[f]["total"])
-	return [[fmt, board[fmt]["search_pts"], board[fmt]["ram_pts"], board[fmt]["disk_pts"], board[fmt]["total"]] for fmt in ranked]
+	return [[FMT_NAMES[fmt], board[fmt]["search_pts"], board[fmt]["ram_pts"], board[fmt]["disk_pts"], board[fmt]["total"]] for fmt in ranked]


-def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[list]]],
-		scoreboards, environment: str, diagrams: dict[str, str], winner: str) -> str:
+def config_string(row: dict, prices: dict) -> str:
+	return (f"{row['nodes']} x 1U {CHASSIS[row['cores']]} ({row['cores']} cores);"
+		f" {row['ram_modules']} x {prices['ram_module_gb']} GB DDR5 ECC RDIMM;"
+		f" {row['nvme_drives']} x {prices['nvme_drive_tb']} TB NVMe U.2")
+
+
+def assemble_report(tables: dict[str, tuple[list[str], list[list]]], scoreboards,
+		environment: str, diagrams: dict[str, str], winner: str, sizing, prices: dict) -> str:
 	t1_h, t1_r = tables["t1_storage_footprint"]
 	t2_h, t2_r = tables["t2_measured_medians"]
 	t3_h, t3_r = tables["t3_projected_600gb"]
 	t4_h, t4_r = tables["t4_hardware_600gb"]
+	pg6 = sizing[("pg", 6000)]
+	rdf6 = sizing[("rdf", 6000)]
+	as_of = prices["as_of"]
+	sources = prices["sources"]

-	board600 = scoreboards["projected_600gb"]
 	parts = [
 		"# Laboratory Data Storage Format Evaluation - Final Report",
 		"",
@@ -345,9 +417,10 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		"",
 		"- **PostgreSQL** is the only format whose seven benchmark queries stay"
 		" below the 1-hour practicality bound at every projected scale (600 GB,"
-		" 1.2 TB, 6 TB); at 6 TB it needs a single 16-core node with ~551 GB"
-		" RAM (~$19k, projected). Partitioned parallel scans and the"
-		" track_summary materialized view are the scaling levers.",
+		f" 1.2 TB, 6 TB); at 6 TB it needs one {pg6['cores']}-core node with"
+		f" {pg6['ram_gb']:.0f} GB RAM at an estimated {money(pg6['est_cost_usd'])}"
+		f" (bill of materials, prices as of {as_of}). Partitioned parallel scans"
+		" and the track_summary materialized view are the scaling levers.",
 		"- **CSV** remains the canonical raw archive: byte-reproducible,"
 		" instrument-native, and the best compressor (tar.gz ~2.8x); its full"
 		" scans exceed 1 h beyond ~600 GB, so it is an archive, not a query"
@@ -359,9 +432,10 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		" corpus) is the exchange format; the FULL variant re-reads the whole"
 		" corpus per scan and becomes impractical past 600 GB.",
 		"- **RDF (materialized triplestore)** fails at scale: 11.7x storage"
-		" blow-up, 5 of 7 queries FAIL at 6 TB, hot-set RAM exceeds the 1 TB"
-		" single-node ceiling (14 nodes, ~$303k projected). Semantic access"
-		" should be a virtual layer (e.g. Ontop OBDA) over PostgreSQL instead.",
+		" blow-up, 5 of 7 queries FAIL at 6 TB, and the hot set needs"
+		f" {rdf6['ram_gb'] / 1024:.1f} TB RAM across {rdf6['nodes']} nodes"
+		f" (~{money(rdf6['est_cost_usd'])} projected). Semantic access should be"
+		" a virtual layer (e.g. Ontop OBDA) over PostgreSQL instead.",
 		"",
 		"## 2. Methodology snapshot",
 		"",
@@ -392,9 +466,11 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		md_table(t2_h, t2_r),
 		"",
 		"Sub-interval note: cells faster than the 50 ms sampling cadence"
-		" (fast sqlite/csv point reads) under-report client RSS/CPU; wall"
+		" (fast SQLite/CSV point reads) under-report client RSS/CPU; wall"
 		" times are exact.",
 		"",
+		"![C2 - RAM requirement](charts/c2_ram_requirement.png)",
+		"",
 		"Per-query charts: " + ", ".join(f"[Q{qn}](charts/c3_q{qn}_wall_time.png)" for qn in QUERY_NUMBERS),
 		"",
 		"## 6. Projections (600 GB / 1.2 TB / 6 TB)",
@@ -405,21 +481,38 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		" calibrated on the measured point after subtracting the per-format"
 		" harness floor. Flags: IMPRACTICAL > 1 h, FAIL > 24 h.",
 		"",
-		"Projected wall times @600 GB:",
+		"Projected wall times at 600 GB:",
 		"",
 		md_table(t3_h, t3_r),
 		"",
-		"Degradation curves: [point read](charts/c4_degradation_point_read.png),"
-		" [indexed](charts/c4_degradation_indexed.png),"
+		"Degradation by data size: [point read](charts/c4_degradation_point_read.png),"
+		" [indexed / join](charts/c4_degradation_indexed.png),"
 		" [full scan](charts/c4_degradation_full_scan.png)",
 		"",
-		"## 7. Hardware sizing and cost @600 GB (projected)",
+		"## 7. Hardware sizing and cost at 600 GB (projected)",
+		"",
+		f"Bill-of-materials costing with street prices as of **{as_of}**"
+		" (sources below). Every configuration buys whole components: 64 GB"
+		" DDR5 ECC RDIMM modules, 7.68 TB enterprise NVMe U.2 drives, and a"
+		" priced 1U chassis; disk capacity includes 30% free-space headroom.",
 		"",
 		md_table(t4_h, t4_r),
 		"",
+		f"Component prices (retrieved {as_of}):",
+		"",
+		f"- {prices['ram_module_gb']} GB DDR5-5600 ECC RDIMM (A-Tech, 2x32GB kit):"
+		f" {money(prices['ram_module_usd'])} - <{sources['ram']}>",
+		f"- {prices['nvme_drive_tb']} TB NVMe U.2 1DWPD enterprise SSD (Dell 14-16G"
+		f" compatible, Cloud Ninjas): {money(prices['nvme_drive_usd'])} - <{sources['nvme']}>",
+		f"- 1U AMD EPYC 9004/9005 chassis (Broadberry starting configurations):"
+		f" {CHASSIS[16]} {money(prices['server_16_core_usd'])},"
+		f" {CHASSIS[32]} {money(prices['server_32_core_usd'])} - <{sources['servers']}>",
+		f"- Market context - server DRAM is in a documented 2025-2026 price"
+		f" surge: <{sources['market_context']}>",
+		"",
 		"Full sizing at 1.2 TB and 6 TB: out/bench/hardware_sizing.csv."
-		" RDF exceeds the 1 TB single-node RAM ceiling already at 600 GB"
-		" (1.4 TB hot set) and reaches 14 nodes at 6 TB.",
+		f" RDF exceeds the 1 TB single-node RAM ceiling already at 600 GB and"
+		f" reaches {rdf6['nodes']} nodes at 6 TB.",
 		"",
 		"![C6 - cost vs performance](charts/c6_cost_vs_performance.png)",
 		"",
@@ -436,10 +529,10 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		md_table(["format", f"search (x{WEIGHT_SEARCH})", f"RAM (x{WEIGHT_RAM})", f"disk (x{WEIGHT_DISK})", "total (max 80)"],
 			scoreboard_rows(scoreboards["measured"])),
 		"",
-		"Projected @600 GB:",
+		"Projected at 600 GB:",
 		"",
 		md_table(["format", f"search (x{WEIGHT_SEARCH})", f"RAM (x{WEIGHT_RAM})", f"disk (x{WEIGHT_DISK})", "total (max 80)"],
-			scoreboard_rows(board600)),
+			scoreboard_rows(scoreboards["projected_600gb"])),
 		"",
 		"![C5 - weighted score](charts/c5_weighted_score.png)",
 		"",
@@ -473,6 +566,9 @@ def assemble_report(report_dir: Path, tables: dict[str, tuple[list[str], list[li
 		"- RDF numbers reflect the compact modeling and a client that derives"
 		" run-in / steady-state from raw cycles; SPARQL-side aggregation"
 		" engines could shift (not remove) the scan penalty.",
+		f"- Hardware prices are spot street prices retrieved {as_of} during a"
+		" documented server-DRAM price surge; re-cost by editing"
+		" out/config/hw_prices.yaml and re-running tasks 10-11.",
 		"",
 		"## Appendix A - process flow diagrams (task 02)",
 		"",
@@ -494,6 +590,7 @@ def main() -> int:
 		proj = read_projections(bench_dir / "extrapolation.csv")
 		sizes = read_sizes(bench_dir / "storage_sizes.csv")
 		sizing = read_sizing(bench_dir / "hardware_sizing.csv")
+		prices = read_hw_prices(out_root / "config" / "hw_prices.yaml")
 		environment = (bench_dir / "environment.txt").read_text(encoding="utf-8")
 		diagrams = {p.stem: p.read_text(encoding="ascii") for p in sorted((report_dir / "diagrams").glob("D*.mermaid"))}
 		if len(diagrams) != 6:
@@ -505,57 +602,61 @@ def main() -> int:
 		tables_dir.mkdir(parents=True, exist_ok=True)

 		csv_bytes = sizes[WORKING_VARIANT["csv"]]
-		measured_gb = csv_bytes / 1e9
 		coeff = {fmt: sizes[WORKING_VARIANT[fmt]] / csv_bytes for fmt in FORMATS}
 		proj_bytes_600 = {fmt: coeff[fmt] * REPORT_SCALE_GB * 1e9 for fmt in FORMATS}

 		tables: dict[str, tuple[list[str], list[list]]] = {}
 		tables["t1_storage_footprint"] = (
-			["format", "measured_mb", "coeff_vs_csv", "archival_mb (compressed)",
-				"projected_600gb_gb", "projected_1200gb_gb", "projected_6000gb_gb (all projected)"],
-			[[fmt, round(sizes[WORKING_VARIANT[fmt]] / 1e6, 1), round(coeff[fmt], 2),
-				round(sizes[ARCHIVAL_VARIANT[fmt]] / 1e6, 1),
-				*[round(coeff[fmt] * s * 1e9 / 1e9, 0) for s in SCALES_GB]] for fmt in FORMATS],
+			["format", "measured", "vs CSV", "archival (compressed)",
+				"projected @600 GB", "projected @1.2 TB", "projected @6 TB"],
+			[[FMT_NAMES[fmt], human_size(sizes[WORKING_VARIANT[fmt]]), f"{coeff[fmt]:.2f}x",
+				human_size(sizes[ARCHIVAL_VARIANT[fmt]]),
+				*[human_size(coeff[fmt] * s * 1e9) for s in SCALES_GB]] for fmt in FORMATS],
 		)
 		tables["t2_measured_medians"] = (
-			["format", "query", "wall_s", "peak_rss_mb", "cpu_util_pct", "read_mb"],
-			[[fmt, f"q{qn}", medians[(fmt, qn)]["wall_s"], medians[(fmt, qn)]["peak_rss_mb"],
-				medians[(fmt, qn)]["cpu_util_pct"], medians[(fmt, qn)]["read_mb"]]
+			["format", "query", "wall time", "peak RSS", "CPU util", "read"],
+			[[FMT_NAMES[fmt], f"q{qn}", human_time(medians[(fmt, qn)]["wall_s"]),
+				f"{medians[(fmt, qn)]['peak_rss_mb']:.0f} MB",
+				f"{medians[(fmt, qn)]['cpu_util_pct']:.0f}%",
+				f"{medians[(fmt, qn)]['read_mb']:.1f} MB"]
 				for fmt in FORMATS for qn in QUERY_NUMBERS],
 		)
 		tables["t3_projected_600gb"] = (
-			["format", "query", "projected_wall", "flag"],
-			[[fmt, f"q{qn}", human_time(proj[(fmt, qn, REPORT_SCALE_GB)][0]), proj[(fmt, qn, REPORT_SCALE_GB)][1]]
+			["format", "query", "projected wall time", "flag"],
+			[[FMT_NAMES[fmt], f"q{qn}", human_time(proj[(fmt, qn, REPORT_SCALE_GB)][0]), proj[(fmt, qn, REPORT_SCALE_GB)][1]]
 				for fmt in FORMATS for qn in QUERY_NUMBERS],
 		)
 		tables["t4_hardware_600gb"] = (
-			["format", "disk_tb", "ram_gb", "cores", "nodes", "est_cost_usd"],
-			[[fmt, sizing[(fmt, REPORT_SCALE_GB)]["disk_tb"], sizing[(fmt, REPORT_SCALE_GB)]["ram_gb"],
-				sizing[(fmt, REPORT_SCALE_GB)]["cores"], sizing[(fmt, REPORT_SCALE_GB)]["nodes"],
-				round(sizing[(fmt, REPORT_SCALE_GB)]["est_cost_usd"])] for fmt in FORMATS],
+			["format", "configuration", "chassis", "RAM", "disk", "extra nodes", "total"],
+			[[FMT_NAMES[fmt], config_string(sizing[(fmt, REPORT_SCALE_GB)], prices),
+				money(sizing[(fmt, REPORT_SCALE_GB)]["base_usd"]),
+				money(sizing[(fmt, REPORT_SCALE_GB)]["ram_usd"]),
+				money(sizing[(fmt, REPORT_SCALE_GB)]["disk_usd"]),
+				money(sizing[(fmt, REPORT_SCALE_GB)]["extra_nodes_usd"]),
+				money(sizing[(fmt, REPORT_SCALE_GB)]["est_cost_usd"])] for fmt in FORMATS],
 		)

 		scoreboards = build_scoreboards(medians, proj, sizes, sizing)
 		tables["t5_weighted_scores"] = (
 			["scale", "format", "search_pts", "ram_pts", "disk_pts", "total"],
-			[[scale, fmt, board[fmt]["search_pts"], board[fmt]["ram_pts"], board[fmt]["disk_pts"], board[fmt]["total"]]
+			[[scale, FMT_NAMES[fmt], board[fmt]["search_pts"], board[fmt]["ram_pts"], board[fmt]["disk_pts"], board[fmt]["total"]]
 				for scale, board in scoreboards.items() for fmt in FORMATS],
 		)
 		for name, (headers, rows) in tables.items():
 			write_csv(tables_dir / f"{name}.csv", headers, rows)

-		chart_c1(charts_dir, sizes, proj_bytes_600)
+		winner = max(FORMATS, key=lambda f: scoreboards["projected_600gb"][f]["total"])
+		chart_c1(charts_dir, sizes, proj_bytes_600, coeff)
 		chart_c2(charts_dir, medians, sizing)
 		chart_c3(charts_dir, medians, proj)
-		chart_c4(charts_dir, medians, proj, measured_gb)
-		chart_c5(charts_dir, scoreboards)
+		chart_c4(charts_dir, medians, proj, f"measured {human_size(csv_bytes)}")
+		chart_c5(charts_dir, scoreboards, winner)
 		chart_c6(charts_dir, proj, sizing)
 		charts = sorted(p.name for p in charts_dir.glob("*.png"))
 		if len(charts) != 14:
 			raise ValueError(f"expected 14 charts, wrote {len(charts)}: {charts}")

-		winner = max(FORMATS, key=lambda f: scoreboards["projected_600gb"][f]["total"])
-		report_text = assemble_report(report_dir, tables, scoreboards, environment, diagrams, winner)
+		report_text = assemble_report(tables, scoreboards, environment, diagrams, winner, sizing, prices)
 		report_path = report_dir / "REPORT.md"
 		report_path.write_text(report_text, encoding="ascii", newline="\n")

@@ -564,6 +665,7 @@ def main() -> int:
 			"tables": len(tables),
 			"report_bytes": report_path.stat().st_size,
 			"winner_600gb": winner,
+			"prices_as_of": prices["as_of"],
 		}
 		for fmt in FORMATS:
 			entries[f"score600_{fmt}"] = scoreboards["projected_600gb"][fmt]["total"]