MillionModelStreamed.cs

// File: MillionModelStreamed.cs
// Purpose: Memory-efficient simulation for 1,000,000 AI "models" (streamed, batched, concurrency-controlled)
// No external deps. .NET 6+ recommended.
// Author: Chaiyaphop Nilpat (The Architect)
// System: Ω NEXUS — Million Model Architecture

using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;

namespace MillionModelAIStreamed
{
    // Lightweight representation: we don't allocate 1M objects.
    // Model identity is derived from an index (int) + skill level generation function.
    public static class ModelFactory
    {
        // Deterministic skill level for model index (example: pseudo-random but seedable)
        public static int GetSkillForIndex(int index, int baseSkill = 50)
        {
            // Simple deterministic "noise" — cheap and stable
            unchecked
            {
                int v = index * 2654435761; // Knuth multiplicative hashing
                return baseSkill + (v & 127) % 21 - 10; // baseSkill +/- up to 10
            }
        }

        // Simulated analyze (non-allocating except returned string)
        public static async Task<string> AnalyzeAsync(int index, string input, int simulatedMsDelay = 5)
        {
            // Simulate small async work (io/compute)
            await Task.Delay(simulatedMsDelay).ConfigureAwait(false);

            int skill = GetSkillForIndex(index);
            // Keep returned string compact
            return $"m{index:D7}-sk{skill} analyzed";
        }
    }

    // Manager that streams model indexes in batches, runs analysis with controlled concurrency,
    // and aggregates token/word counts into a concurrent map for consensus.
    public class StreamedAIManager
    {
        public int TotalModels { get; }
        public int Groups { get; } // logical groups for higher-level consensus
        public int BatchSize { get; } // number of models processed per micro-batch
        public int MaxConcurrency { get; } // max concurrent AnalyzeAsync tasks

        public StreamedAIManager(int totalModels, int groups = 1000, int batchSize = 1000, int maxConcurrency = 200)
        {
            if (groups <= 0) throw new ArgumentException(nameof(groups));
            TotalModels = totalModels;
            Groups = groups;
            BatchSize = Math.Max(1, batchSize);
            MaxConcurrency = Math.Max(1, maxConcurrency);
        }

        // Partition model index ranges for groups (returns list of (start, endExclusive) tuples)
        private List<(int start, int end)> PartitionRanges()
        {
            var ranges = new List<(int start, int end)>();
            int baseSize = TotalModels / Groups;
            int remainder = TotalModels % Groups;
            int cursor = 0;
            for (int g = 0; g < Groups; g++)
            {
                int size = baseSize + (g < remainder ? 1 : 0);
                int start = cursor;
                int end = cursor + size;
                if (size > 0) ranges.Add((start, end));
                cursor = end;
            }
            return ranges;
        }

        // Analyze a range of model indexes and produce a single group "summary" (string)
        // Implementation: stream each model in the range in micro-batches to limit concurrency
        private async Task<string> AnalyzeRangeAndSummarizeAsync((int start, int end) range, string input, CancellationToken ct)
        {
            var wordCounts = new Dictionary<string, int>(StringComparer.OrdinalIgnoreCase);
            // We'll throttle actual AnalyzeAsync concurrency via SemaphoreSlim
            using var throttler = new SemaphoreSlim(MaxConcurrency);

            var allTasks = new List<Task>();

            // Process in micro-batches to control memory and concurrency
            for (int cursor = range.start; cursor < range.end; cursor += BatchSize)
            {
                ct.ThrowIfCancellationRequested();
                int batchEnd = Math.Min(range.end, cursor + BatchSize);

                var batchTasks = new List<Task<string>>(batchEnd - cursor);
                for (int idx = cursor; idx < batchEnd; idx++)
                {
                    ct.ThrowIfCancellationRequested();
                    // await throttler inside a Task to allow scheduling many producers without blocking here
                    var task = Task.Run(async () =>
                    {
                        await throttler.WaitAsync(ct).ConfigureAwait(false);
                        try
                        {
                            return await ModelFactory.AnalyzeAsync(idx, input).ConfigureAwait(false);
                        }
                        finally
                        {
                            throttler.Release();
                        }
                    }, ct);

                    batchTasks.Add(task);
                }

                // Wait for batch to complete
                var results = await Task.WhenAll(batchTasks).ConfigureAwait(false);

                // Aggregate word counts incrementally (not thread-safe here because we are single-threaded after awaiting)
                foreach (var s in results)
                {
                    // simple tokenization (non-alloc heavy)
                    var tokens = s.Split(new[] { ' ', ',', '.', ':', ';', '-', '_' }, StringSplitOptions.RemoveEmptyEntries);
                    foreach (var t in tokens)
                    {
                        if (t.Length == 0) continue;
                        if (wordCounts.TryGetValue(t, out var c)) wordCounts[t] = c + 1;
                        else wordCounts[t] = 1;
                    }
                }
            }

            // Derive top tokens as group summary
            var top = wordCounts.OrderByDescending(kv => kv.Value).Take(5).Select(kv => kv.Key).ToArray();
            return $"Group[{range.start:D7}-{range.end - 1:D7}] top: {string.Join(",", top)}";
        }

        // Run across all groups in parallel (but limit number of group tasks to avoid overload)
        public async Task<string> RunAllStreamedAsync(string input, CancellationToken ct = default)
        {
            var ranges = PartitionRanges();

            // We'll allow limited parallelism for groups as well (so not all 1000 groups start at once)
            int groupsParallel = Math.Min(64, Math.Max(1, Environment.ProcessorCount * 2)); // heuristic
            using var groupThrottler = new SemaphoreSlim(groupsParallel);

            var groupSummaries = new ConcurrentBag<string>();
            var exceptions = new ConcurrentBag<Exception>();

            var groupTasks = ranges.Select(async range =>
            {
                await groupThrottler.WaitAsync(ct).ConfigureAwait(false);
                try
                {
                    var summary = await AnalyzeRangeAndSummarizeAsync(range, input, ct).ConfigureAwait(false);
                    groupSummaries.Add(summary);
                }
                catch (Exception ex)
                {
                    exceptions.Add(ex);
                }
                finally
                {
                    groupThrottler.Release();
                }
            });

            await Task.WhenAll(groupTasks).ConfigureAwait(false);

            if (exceptions.Count > 0)
            {
                throw new AggregateException(exceptions);
            }

            // Final consensus: simple token count across group summaries
            var globalCounts = new Dictionary<string, int>(StringComparer.OrdinalIgnoreCase);
            foreach (var gs in groupSummaries)
            {
                var tokens = gs.Split(new[] { ' ', ',', ':', ';', '-', '_' }, StringSplitOptions.RemoveEmptyEntries);
                foreach (var t in tokens)
                {
                    if (t.Length == 0) continue;
                    if (globalCounts.TryGetValue(t, out var c)) globalCounts[t] = c + 1;
                    else globalCounts[t] = 1;
                }
            }

            var topGlobal = globalCounts.OrderByDescending(kv => kv.Value).Take(10).Select(kv => kv.Key).ToArray();
            return $"Final Key Insights: {string.Join(", ", topGlobal)}";
        }
    }

    // Demo runner
    public static class Program
    {
        public static async Task Main(string[] args)
        {
            // Parameters
            int totalModels = 1_000_000;
            int groups = 1000;         // logical groups
            int batchSize = 1000;      // micro-batch per group
            int maxConcurrency = 200;  // tune based on machine: reduce if limited resources

            var manager = new StreamedAIManager(totalModels, groups, batchSize, maxConcurrency);

            string input = "ข้อมูลสำหรับวิเคราะห์และสกัดองค์ความรู้จากโมเดล AI จำนวนมาก";

            Console.WriteLine($"Starting streamed processing for {totalModels:N0} models...");
            var cts = new CancellationTokenSource();

            var sw = System.Diagnostics.Stopwatch.StartNew();
            string final = await manager.RunAllStreamedAsync(input, cts.Token);
            sw.Stop();

            Console.WriteLine("Processing completed in " + sw.Elapsed);
            Console.WriteLine(final);
        }
    }
}