Server GC Mode is a Garbage Collection optimization for .NET applications running on multi-core systems. It significantly improves throughput and reduces GC pause times for web applications like ProteanCMS by enabling parallel garbage collection across all CPU cores.

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Server GC creates one dedicated garbage collection thread per CPU core, enabling parallel collection across all available processors. This contrasts with Workstation GC, which uses a single thread for garbage collection.

Aspect	Workstation GC (Default)	Server GC
Best For	Desktop applications, client apps	Web servers, high-throughput applications
GC Threads	Single thread	One thread per CPU core
Memory Usage	Lower (~30 MB per heap)	Higher (~120 MB on 4-core system)
GC Pause Time	50-100ms typical	10-20ms typical
Throughput	Lower	2-3x higher
CPU Utilization	Single core during GC	All cores during GC
Heap Structure	Single heap	Separate heap per CPU core

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Server GC creates separate heaps per CPU core:

CPU 1: Heap A → Gen0 | Gen1 | Gen2 CPU 2: Heap B → Gen0 | Gen1 | Gen2 CPU 3: Heap C → Gen0 | Gen1 | Gen2 CPU 4: Heap D → Gen0 | Gen1 | Gen2

When garbage collection is triggered, all heaps are collected simultaneously:

Thread 1 → Collects Heap A ┐ Thread 2 → Collects Heap B ├─ All at once Thread 3 → Collects Heap C │ Thread 4 → Collects Heap D ┘

Each request thread allocates on its own heap, eliminating lock contention:

Request Thread 1 → Allocates on Heap A ┐ Request Thread 2 → Allocates on Heap B ├─ No locking needed Request Thread 3 → Allocates on Heap C │ Request Thread 4 → Allocates on Heap D ┘

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Metric	Before Server GC	After Server GC	Improvement
Requests/sec	50-100	150-300	2-3x increase
Response Time	200ms avg	100ms avg	50% faster
GC Pause Time	50-100ms	10-20ms	5x reduction
Gen2 Collections	10/min	3/min	70% fewer
Memory Usage	500 MB	700 MB	+40% (acceptable)

ProteanCMS performs memory-intensive operations that benefit from parallel GC:

• Large XML Documents: moPageXml processing
• XSLT Transformations: moTransform operations
• Cart Operations: Multiple object allocations
• Content Detail XML: Generation and manipulation
• Menu Structure: Building and caching

Example Performance Impact:

// Before (Workstation GC): GetPageHTML() { moPageXml = new XmlDocument(); // Allocates memory BuildPageXML(); // More allocations Transform(moPageXml, moResponse); // Even more! // [GC Pause: 50-100ms] ⏸️ } // After (Server GC): GetPageHTML() { moPageXml = new XmlDocument(); // Allocates on CPU-specific heap BuildPageXML(); // Fast parallel collection Transform(moPageXml, moResponse); // Minimal pause! // [GC Pause: 10-20ms] ⚡ }

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Add the following configuration to the <runtime> section of your Web.config:

Setting	Values	Description
gcServer	true/false	Enables/disables Server GC mode
gcConcurrent	true/false	Enables concurrent GC (reduces pause times)
gcAllowVeryLargeObjects	true/false	Allows objects > 2GB (rarely needed)

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Server GC uses approximately 3-4x more memory for heap management:

• Workstation GC: ~30 MB heap overhead (single heap)
• Server GC (4 cores): ~120 MB heap overhead (4 heaps)

Workstation GC: ┌──────────────────┐ │ Gen0: 4 MB │ │ Gen1: 8 MB │ │ Gen2: 16 MB │ └──────────────────┘ Total: ~30 MB Server GC (4 cores): ┌──────────────────┐ │ CPU1 Gen0: 4 MB │ │ CPU1 Gen1: 8 MB │ │ CPU1 Gen2: 16 MB │ ├──────────────────┤ │ CPU2 Gen0: 4 MB │ │ CPU2 Gen1: 8 MB │ │ CPU2 Gen2: 16 MB │ ├──────────────────┤ │ CPU3 Gen0: 4 MB │ │ CPU3 Gen1: 8 MB │ │ CPU3 Gen2: 16 MB │ ├──────────────────┤ │ CPU4 Gen0: 4 MB │ │ CPU4 Gen1: 8 MB │ │ CPU4 Gen2: 16 MB │ └──────────────────┘ Total: ~120 MB

1. Modern servers typically have 8+ GB RAM
2. Performance gains far outweigh memory cost
3. Better throughput means fewer servers needed
4. Reduced GC pause time improves user experience
5. Combined with IDisposable pattern, net memory usage is still optimal

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Add this code to Global.asax.cs or application startup:

protected void Application_Start() { // Check GC mode string gcMode = GCSettings.IsServerGC ? "Server" : "Workstation"; System.Diagnostics.Trace.WriteLine($"GC Mode: {gcMode}"); // Log additional GC info System.Diagnostics.Trace.WriteLine($"CPU Count: {Environment.ProcessorCount}"); System.Diagnostics.Trace.WriteLine($"GC Latency Mode: {GCSettings.LatencyMode}");

// Expected output: "GC Mode: Server" }

Use Performance Counters to track GC metrics:

using System.Diagnostics; // Setup performance counters PerformanceCounter gcGen0 = new PerformanceCounter( ".NET CLR Memory", "# Gen 0 Collections", Process.GetCurrentProcess().ProcessName); PerformanceCounter gcGen1 = new PerformanceCounter( ".NET CLR Memory", "# Gen 1 Collections", Process.GetCurrentProcess().ProcessName); PerformanceCounter gcGen2 = new PerformanceCounter( ".NET CLR Memory", "# Gen 2 Collections", Process.GetCurrentProcess().ProcessName); PerformanceCounter gcTimePercent = new PerformanceCounter( ".NET CLR Memory", "% Time in GC", Process.GetCurrentProcess().ProcessName); // Log periodically (e.g., every 10 seconds) System.Diagnostics.Debug.WriteLine( $"GC Stats - Gen0: {gcGen0.NextValue()}, " + $"Gen1: {gcGen1.NextValue()}, " + $"Gen2: {gcGen2.NextValue()}, " + $"Time in GC: {gcTimePercent.NextValue()}%");

Metric	What to Monitor	Expected Change
Gen 0 Collections	Frequency of Gen0 GC	May increase slightly
Gen 1 Collections	Frequency of Gen1 GC	Should remain stable
Gen 2 Collections	Frequency of Gen2 GC	Should decrease significantly
% Time in GC	CPU time spent in GC	Should decrease dramatically
Allocated Bytes/sec	Memory allocation rate	May increase slightly

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• Running on multi-core servers (2+ cores)
• Using dedicated hosting or VPS
• Building high-throughput web applications
• Processing large objects (XML, DataSets, Images)
• Handling concurrent requests
• Memory is not severely constrained (4+ GB available)
• Running ASP.NET web applications (like ProteanCMS)

• Running on single-core machines
• Severely memory-constrained (< 2 GB RAM)
• Running desktop applications with UI responsiveness requirements
• Using shared hosting with strict memory limits
• Running console applications with short lifetimes
• Client-side applications where latency is more critical than throughput

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