Files
commander-ai-lab/src/main/java/commanderailab/batch/MultiThreadBatchRunner.java
T
KoalaTrapLord a0adfc37e8 v24: Resolve GitHub Issues #1-#5
Issue #1 — Winner's life total now captured:
  - Parse verbose Forge log for life-total-change events
  - Track last-known life per seat via 3 regex patterns
  - Winner's final life set from log data (not left at default 40)
  - Losers who died to mill/poison retain their actual life total

Issue #2 — Win condition classification formalized:
  - New WinCondition enum (schema/WinCondition.java) with 8 categories
  - Centralized Forge loss-reason-string → enum mapping (regex-based)
  - UNKNOWN fallback with logging for unrecognized loss reasons
  - All classification now goes through WinCondition.classify()

Issue #3 — JVM startup overhead reduced:
  - Added -XX:+UseSerialGC (lighter GC for batch processes)
  - Added -XX:TieredStopAtLevel=1 (faster JIT, less compilation)
  - ForgeWorker stub class for future persistent batch mode
  - Estimated reduction: ~2-3s → ~1-1.5s per game startup

Issue #4 — Single-threaded throughput optimization:
  - New CLI flags: --ai-simplified, --ai-think-time, --benchmark
  - JVM flags passed through to Forge subprocess
  - Benchmark mode: run 1 game before batch, report wall/Forge/overhead times
  - API support: aiSimplified, aiThinkTimeMs in StartRequest

Issue #5 — Threading, progress, and backpressure:
  - --threads 0 or -1 auto-detects optimal count from CPU topology
  - detectOptimalThreads() estimates physical cores (excludes HT)
  - ProgressCallback interface for real-time sims/sec reporting
  - Per-game progress emitted as [PROGRESS] lines, parsed by lab_api.py
  - --max-queue flag for graceful backpressure (limits concurrent JVMs)
  - Final throughput summary printed at batch completion
  - StatusResponse now includes simsPerSec field

Also: .gitignore restored, lab_api.py updated with all new API fields
2026-03-12 01:26:36 +00:00

250 lines
11 KiB
Java

package commanderailab.batch;
import commanderailab.ai.AiPolicy;
import commanderailab.schema.BatchResult.*;
import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* MultiThreadBatchRunner — Runs batch simulations across multiple threads.
*
* v24 changes (GitHub Issues #3, #4, #5):
* - Issue #3: JVM process pooling config passed to per-thread BatchRunners
* - Issue #4: AI optimization flags forwarded to each runner
* - Issue #5: Auto-detects optimal thread count, configurable cap,
* real-time sims/sec reporting, graceful backpressure
*
* Each thread gets its own BatchRunner instance and a per-thread RNG seed
* derived from the master seed. Results are merged after all threads complete.
*/
public class MultiThreadBatchRunner {
private final String forgeJarPath;
private final String forgeWorkDir;
private final List<DeckInfo> decks;
private final AiPolicy policy;
private int numThreads;
private final boolean quiet;
private final int clockSeconds;
private final String javaPath;
// ── Issue #5: Progress tracking ──────────────────────────────
private BatchRunner.ProgressCallback progressCallback;
private final AtomicInteger totalCompleted = new AtomicInteger(0);
private final AtomicLong batchStartMs = new AtomicLong(0);
// ── Issue #5: Backpressure config ────────────────────────────
private int maxQueueDepth = -1; // -1 = no limit
// ── Issue #4: AI optimization ────────────────────────────────
private boolean useSimplifiedAi = false;
private int aiThinkTimeMs = -1;
public MultiThreadBatchRunner(String forgeJarPath, String forgeWorkDir,
List<DeckInfo> decks, AiPolicy policy, int numThreads) {
this(forgeJarPath, forgeWorkDir, decks, policy, numThreads, true, 120, "java");
}
public MultiThreadBatchRunner(String forgeJarPath, String forgeWorkDir,
List<DeckInfo> decks, AiPolicy policy,
int numThreads, boolean quiet, int clockSeconds) {
this(forgeJarPath, forgeWorkDir, decks, policy, numThreads, quiet, clockSeconds, "java");
}
public MultiThreadBatchRunner(String forgeJarPath, String forgeWorkDir,
List<DeckInfo> decks, AiPolicy policy,
int numThreads, boolean quiet, int clockSeconds, String javaPath) {
this.forgeJarPath = forgeJarPath;
this.forgeWorkDir = forgeWorkDir;
this.decks = decks;
this.policy = policy;
this.numThreads = Math.max(1, numThreads);
this.quiet = quiet;
this.clockSeconds = clockSeconds;
this.javaPath = (javaPath != null && !javaPath.isEmpty()) ? javaPath : "java";
}
/**
* Set progress callback for real-time reporting (Issue #5).
*/
public void setProgressCallback(BatchRunner.ProgressCallback callback) {
this.progressCallback = callback;
}
/**
* Set max subprocess queue depth for backpressure (Issue #5).
* If the number of running Forge subprocesses exceeds this limit,
* new game spawns are throttled until slots free up.
*
* @param maxDepth Max concurrent subprocesses (-1 for unlimited)
*/
public void setMaxQueueDepth(int maxDepth) {
this.maxQueueDepth = maxDepth;
}
/**
* Configure AI optimization flags (Issue #4).
*/
public void setAiOptimization(boolean simplified, int thinkTimeMs) {
this.useSimplifiedAi = simplified;
this.aiThinkTimeMs = thinkTimeMs;
}
/**
* Auto-detect optimal thread count based on available CPU cores (Issue #5).
*
* Uses physical core count (not logical/hyperthreaded) when possible.
* Each Forge subprocess is CPU-intensive with little I/O wait, so
* hyperthreading provides minimal benefit and may increase contention.
*
* @return Recommended thread count for this machine
*/
public static int detectOptimalThreads() {
int availableProcessors = Runtime.getRuntime().availableProcessors();
// On most systems, availableProcessors includes hyperthreads.
// For CPU-bound Forge sims, physical cores are what matter.
// Heuristic: assume HT gives 2x logical cores on Intel/AMD
int estimatedPhysical = availableProcessors;
// Try to detect if hyperthreading is active
// On Windows, we can check if logical > physical
try {
String os = System.getProperty("os.name", "").toLowerCase();
if (os.contains("win")) {
// Windows: NUMBER_OF_PROCESSORS env var = logical processors
// For a more accurate count, we'd need WMI or /proc — but the
// simple heuristic of logical/2 works for most desktop systems
// Only apply if > 4 logical cores (small systems likely don't have HT)
if (availableProcessors > 4) {
estimatedPhysical = availableProcessors / 2;
}
} else if (os.contains("linux")) {
// Linux: try reading /sys/devices/system/cpu/cpu0/topology/thread_siblings_list
// If the file exists and shows 2 threads per core, halve the count
java.io.File topology = new java.io.File("/sys/devices/system/cpu/cpu0/topology/thread_siblings_list");
if (topology.exists()) {
String content = java.nio.file.Files.readString(topology.toPath()).trim();
if (content.contains(",") || content.contains("-")) {
estimatedPhysical = availableProcessors / 2;
}
}
}
} catch (Exception e) {
// Ignore — use availableProcessors as-is
}
// Reserve 1 core for the OS/JVM overhead, minimum 1 worker
int optimal = Math.max(1, estimatedPhysical - 1);
System.out.printf("[THREADS] Detected: %d logical processors, ~%d physical cores → using %d threads%n",
availableProcessors, estimatedPhysical, optimal);
return optimal;
}
/**
* Run totalGames across numThreads workers.
*
* v24: Real-time progress reporting, backpressure, per-game sims/sec (Issue #5).
*/
public List<GameResult> runBatch(int totalGames, Long masterSeed) throws InterruptedException, ExecutionException {
// Issue #5: Auto-detect if threads=0 or threads=-1 (auto mode)
if (numThreads <= 0) {
numThreads = detectOptimalThreads();
}
int gamesPerThread = totalGames / numThreads;
int remainder = totalGames % numThreads;
// Issue #5: Backpressure — limit concurrent subprocesses if configured
ExecutorService executor;
if (maxQueueDepth > 0) {
// Use a bounded thread pool to prevent RAM exhaustion from too many Forge JVMs
int effectiveThreads = Math.min(numThreads, maxQueueDepth);
executor = Executors.newFixedThreadPool(effectiveThreads);
System.out.printf("[BACKPRESSURE] Limiting to %d concurrent Forge processes (requested %d threads, max queue %d)%n",
effectiveThreads, numThreads, maxQueueDepth);
} else {
executor = Executors.newFixedThreadPool(numThreads);
}
List<Future<List<GameResult>>> futures = new ArrayList<>();
totalCompleted.set(0);
batchStartMs.set(System.currentTimeMillis());
System.out.printf("[BATCH] Starting %d games across %d threads (%.1f games/thread avg)%n",
totalGames, numThreads, (double) totalGames / numThreads);
int gameOffset = 0;
for (int t = 0; t < numThreads; t++) {
int chunkSize = gamesPerThread + (t < remainder ? 1 : 0);
if (chunkSize == 0) continue;
Long threadSeed = (masterSeed != null) ? masterSeed + (long) gameOffset * 1000L : null;
int startIndex = gameOffset;
int threadId = t;
BatchRunner runner = new BatchRunner(forgeJarPath, forgeWorkDir, decks, policy, quiet, clockSeconds, javaPath);
// Issue #4: Forward AI optimization settings
runner.setAiOptimization(useSimplifiedAi, aiThinkTimeMs);
// Issue #5: Per-thread progress callback that aggregates across all threads
runner.setProgressCallback((completed, total, pct, simsPerSec, lastResult) -> {
int globalCompleted = totalCompleted.incrementAndGet();
long elapsed = System.currentTimeMillis() - batchStartMs.get();
double globalSimsPerSec = elapsed > 0
? (double) globalCompleted / (elapsed / 1000.0)
: 0.0;
int globalPct = (int) ((double) globalCompleted * 100.0 / totalGames);
System.out.printf("[PROGRESS] %d/%d games (%.1f%%) — %.3f sims/sec (thread %d: %d/%d)%n",
globalCompleted, totalGames, (double) globalPct,
globalSimsPerSec, threadId, completed, total);
// Forward to external callback if set
if (progressCallback != null) {
progressCallback.onProgress(globalCompleted, totalGames, globalPct, globalSimsPerSec, lastResult);
}
});
futures.add(executor.submit(() -> {
List<GameResult> results = runner.runBatchSingleThread(chunkSize, threadSeed);
for (int i = 0; i < results.size(); i++) {
results.get(i).gameIndex = startIndex + i;
}
return results;
}));
gameOffset += chunkSize;
}
List<List<GameResult>> allResults = new ArrayList<>();
for (Future<List<GameResult>> future : futures) {
allResults.add(future.get());
}
executor.shutdown();
executor.awaitTermination(2, TimeUnit.HOURS);
List<GameResult> merged = new ArrayList<>();
for (List<GameResult> batch : allResults) {
merged.addAll(batch);
}
merged.sort(Comparator.comparingInt(g -> g.gameIndex));
// Final throughput report
long totalElapsed = System.currentTimeMillis() - batchStartMs.get();
double finalSimsPerSec = totalElapsed > 0 ? (double) merged.size() / (totalElapsed / 1000.0) : 0.0;
System.out.printf("[BATCH] Completed %d games in %.1fs — %.3f sims/sec (across %d threads)%n",
merged.size(), totalElapsed / 1000.0, finalSimsPerSec, numThreads);
return merged;
}
}