11-EFT.WP.Core.DrawingKinetics v1.0 | Chapter 10 Parallelism and Execution (Threads Interface)

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Chapter 10 Parallelism and Execution (Threads Interface)

I. Scope and Objectives

• Interface filament drawing/stretch dynamics with acquisition/control threads by defining the execution graph G = ( V , E ), its modeling, scheduling, and observability gauges, so that hb causality holds, bp back-pressure stays safe, and SLOs are verifiable.
• Define runtime metrics TS.* covering T_make(G), latency and jitter, queues and loss, critical path and alert triggers; state the strict alignment with Chapter 2–9 physical variables (lambda, s, v, A, rho_L, J, T_fil, S_xx(f), etc.) on the unified time base ts.

II. Terminology and Symbols

1. Execution graph and costs: G=(V,E), w(v) (node compute cost in s or CPU•s), m (parallel resource count), C_p(G) (critical-path duration).
2. Scheduling and deadlines: T_make(G) (job makespan), D_k (deadline for product k), slack_k = D_k - ( t_now + R_k ), R_k (remaining cost estimate on the critical path).
3. Queues and back-pressure: Q_i(ts) (queue depth at stage i, in items), r_in,i, r_out,i (input/output rates, items/s), bp (back-pressure signal).
4. Causality and time base: hb (happens-before), tau_mono (monotonic local clock), ts = alpha + beta * tau_mono (per Chapter 8).
5. Runtime metrics (TS.*, harmonized with Core.Threads)
   • TS.lat.p50, TS.lat.p99 (end-to-end latency)
   • TS.jitter.rms (period jitter)
   • TS.backlog.max (max queue depth)
   • TS.loss.rate (drop rate)
   • TS.hb.violations (causality violations)
   • TS.util.cpu, TS.util.mem (resource utilization)
   • TS.makespan.last = T_make(G)
   • TS.cp.length = C_p(G), TS.cp.set (critical-path node set)

III. Postulates and Minimal Equations

• P11-10 (hb-consistent commit)
  Any derived physical timeseries must satisfy hb(acquire) -> hb(estimate) -> hb(emit) before manifest write; commits are idempotent transactions—replays must not change meta.uid nor the digest hash.
• P11-11 (cross-thread time-base consistency)
  Each thread’s tau_mono drift vs. the master clock stays below drift_max; all published data map to ts = alpha + beta * tau_mono, with alpha, beta retained under timebase.*.
• P11-12 (back-pressure safety domain)
  Back-pressure policy must keep all intermediate queues within 0 <= Q_i(ts) <= Q_i.max without uncontrolled drops; if dropping is required, record strategy as drop.policy and annotate with *_qf.
• S12-18 (queue dynamics and rate limiting)
  ( d/dt ) Q_i = r_in,i - r_out,i; r_out,i = min( r_cap,i , r_demand,i ). When Q_i >= Q_i.max, emit bp and regulate upstream r_in,i = f_bp( Q_i ), where f_bp is monotone non-increasing and f_bp( Q_i.max ) = 0.
• S12-19 (DAG makespan lower bound and scheduling rule)
  T_make(G) >= max( C_p(G) , ( ∑_{v∈V} w(v) / m ) ). A near-optimal ready-set policy prioritizes nodes on the critical path with minimal w(v)/deadline_slack(v).
• S12-20 (deadline slack and alerts)
  slack_k = D_k - ( t_now + R_k ). When slack_k <= slack_gate, issue ALERT.DEADLINE_RISK and either raise TS.priority or downsample.
• Dimensional audits
  check_dim( T_make ) == s; check_dim( ( d/dt ) Q_i ) == 1/s; check_dim( r_in,i ) == 1/s.

IV. Data and Manifest Gauges

1. Runtime extensions (suggested under schema.core.drawing/v1 as runtime.*)
   • runtime.G.hash (structural hash of the execution graph)
   • runtime.T_make (s), runtime.C_p (s), runtime.cp.set (list of node names)
   • runtime.queues[i].Q_max, Q_p95, drops, drop.policy
   • runtime.rate.in, runtime.rate.out (items/s)
   • runtime.TS.lat.p50, TS.lat.p99, TS.jitter.rms, TS.util.cpu, TS.util.mem, TS.hb.violations
   • runtime.alerts[] (timestamps and alert types)
2. Alignment to physical quantities
   For each series.* window window.id, attach the co-window runtime.* statistics; for frequency derivatives spectral.*, record the producing job v_spectral and its w(v) estimate.

V. Algorithms and Implementation Bindings

1. I10-1 update_draw_state( state, bc:dict, dt:float ) -> StepReport
   Thread-safe: acquire slices of v_in, v_out, T_fil, update lambda, s, A, rho_L, J; commit with hb(acquire)->hb(update) markers.
2. I10-2 estimate_tension( lambda:float , s:float , params:dict ) -> float
   Batch-parallelizable; if params.model includes rate-hardening, declare w(v) estimate and an upper bound for TS.util.cpu.
3. I10-3 compute_instability_metrics( state ) -> dict
   Runs asynchronously; if TS.cp.length exceeds its threshold, downsample or switch to a lightweight metric set.
4. I10-4 calibrate_kinematics( trace:any , sensors:list , timebase:dict ) -> CalReport
   Execute only during INIT/WARMUP; returns alpha, beta and jitter_rms.
5. I10-5 emit_metrics_drawing( state ) -> dict
   Terminal node honoring hb(update)->hb(emit), writing runtime.* and aggregating TS.*.
6. New execution interfaces
   • I10-6 schedule_draw_graph( G , SLO:dict , resources:dict ) -> SchedPlan
   • I10-7 run_event_loop( plan:SchedPlan ) -> RuntimeReport
7. Pseudocode (core loop)

I10-7 run_event_loop(plan):

state <- INIT

while state != STOP:

events <- poll(plan.inputs, plan.timeout)

for e in events:

tag_hb(e, "acquire")

if backlog_high(): issue_bp(); maybe_throttle()

submit(v_update, e.payload) # I10-1

if need_spectrum(): submit(v_spectral, window_id)

if need_risk(): submit(v_instab, snapshot) # I10-3

drain_ready_tasks_by_priority() # critical-path & slack-aware

for r in ready(v_emit):

tag_hb(r, "emit")

out <- I10-5(r.state)

publish(out)

8. State machine
   INIT -> WARMUP -> RUN -> DRAIN -> STOP; in WARMUP perform I10-4 and resource probing; in DRAIN wait for queues to empty and ensure T_make(G) convergence.

VI. Metrology Flow and Run Graph

1. Run-graph stages
   • Acquisition node v_acq (encoders / tension / environment)
   • State update v_update (I10-1)
   • Constitutive estimate v_tension (I10-2)
   • Spectral derivation v_spectral (Chapter 7)
   • Instability assessment v_instab (I10-3)
   • Manifest consolidation v_emit (I10-5)
2. Key observation points
   At each node’s exit, annotate TS.lat.node, TS.queue.len, TS.cpu.slice; on job completion, update TS.makespan.last and TS.cp.set.
3. Alerts and fallback
   If TS.backlog.max > gate.backlog or TS.hb.violations > 0, trigger ALERT.PIPELINE_HB, enter downsampling mode, and freeze non-critical derivatives (e.g., high-resolution spectral.*). If slack_k <= 0, trigger ALERT.DEADLINE_MISS and record affected window.id.

VII. Verification and Test Matrix

1. Minimum cases
   • Constant-speed drawing: verify hb ordering and the S12-19 makespan bound.
   • Step drawing: validate TS.jitter.rms and stability of the S12-18 back-pressure loop.
   • Ramp drawing: verify the concurrent scaling of v_spectral under TS.util.cpu limits.
2. Extreme scenarios
   • Amplified sensor jitter and late packets.
   • Queue capacity limits and forced-drop policy.
   • Resource shrink (m halved) vs. T_make(G) and SLO preservation.
3. Conservation and gates
   Parallel execution must not violate Chapter 3 conservation audits: eps_mass and eps_norm must pass qc.gate.* under concurrency and downsampling modes.

VIII. Cross-References and Dependencies

• Chapter 2: definitions and observation mappings for lambda, s, v, A are the input/output contracts of v_update.
• Chapter 3: rho_L, J and S12-1 continuity residuals provide parallel audits.
• Chapter 4: constitutive families for T_fil influence v_tension cost w(v) and batching strategies.
• Chapter 7: S_xx(f), U_w, ENBW govern v_spectral windows and resource budgets.
• Chapter 8: ts alignment and alpha, beta from I10-4; the two arrival-time conventions’ delta_form is co-published in parallel reports.
• Chapter 9: runtime.* and TS.* fields enter the manifest for traceability and compliance.

IX. Risks, Limits, and Open Questions

• OS scheduling and GC pauses introduce tail latency; evaluate container/runtime choices against the TS.lat.p99 gate.
• Multi-source tau_mono drift amplifies in cross-device aggregation; retain corrections and confidence under timebase.*.
• Back-pressure policy boundaries on physical sampling fidelity require tighter coupling between drop.policy and quality gates.
• GPU/CPU memory contention and NUMA affinity produce second-order effects on C_p(G) at scale; quantify under large concurrency.

X. Deliverables and Version Management

1. Deliverables
   • threads.drawing.v1.yaml (execution graph, priorities, rate-limiting policies)
   • threads.drawing.metrics.json (aggregated TS.* and runtime.*)
   • threads.drawing.hbtrace (causality trace with hb tags)
2. Versioning and changes
   • Scheduling-policy or bp-gauge changes are marked MOD, with synchronized updates to runtime.* field docs.
   • New nodes or parallel branches are marked ADD, updating G.hash and the baseline T_make(G) for reference runs.
   • Any change impacting hb semantics must ship a compatibility flag and a regression matrix, recorded in CHANGELOG.md.