ZiggyAlloc 1.3.0
dotnet add package ZiggyAlloc --version 1.3.0
NuGet\Install-Package ZiggyAlloc -Version 1.3.0
<PackageReference Include="ZiggyAlloc" Version="1.3.0" />
<PackageVersion Include="ZiggyAlloc" Version="1.3.0" />
<PackageReference Include="ZiggyAlloc" />
paket add ZiggyAlloc --version 1.3.0
#r "nuget: ZiggyAlloc, 1.3.0"
#:package ZiggyAlloc@1.3.0
#addin nuget:?package=ZiggyAlloc&version=1.3.0
#tool nuget:?package=ZiggyAlloc&version=1.3.0
ZiggyAlloc
High-performance unmanaged memory management for .NET with explicit control and zero GC pressure.
Overview
ZiggyAlloc is a high-performance C# library for unmanaged memory management. It provides explicit control over memory allocation while maintaining safety through well-designed abstractions and automatic cleanup mechanisms.
Key Features
- High-Performance Memory Management: Direct access to native memory allocation
- SIMD Memory Operations: Hardware-accelerated memory clearing and copying with 5-29x performance gains
- Multiple Allocator Strategies: System, scoped, debug, pool, hybrid, slab, and large block allocators
- Type-Safe Memory Access:
UnmanagedBuffer<T>
with bounds checking - Memory Safety: Leak detection, bounds checking, and automatic cleanup
- RAII Support: Automatic cleanup using
using
statements - Span<T> Integration: Zero-cost conversion to high-performance spans
- Native Interop: Direct pointer access for native API calls
- Hardware Optimization: AVX2 acceleration with automatic fallback for older hardware
🚀 Quick Start
using ZiggyAlloc;
// Create allocator
var allocator = new SystemMemoryAllocator();
// Allocate memory with automatic cleanup
using var buffer = allocator.Allocate<int>(1000);
// Use like a normal array with bounds checking
buffer[0] = 42;
int value = buffer[0];
// Convert to Span<T> for high-performance operations
Span<int> span = buffer;
span.Fill(123);
📊 Performance Comparison
ZiggyAlloc delivers exceptional performance through multiple optimization strategies:
SIMD Memory Operations (Latest Results)
Revolutionary Performance Gains with Hardware Acceleration:
Operation | Data Size | Standard | SIMD Accelerated | Performance Gain | Hardware |
---|---|---|---|---|---|
ZeroMemory | 1KB | 330ns | 21ns | 15.7x faster | AVX2 |
ZeroMemory | 16KB | 5.07μs | 190ns | 26.7x faster | AVX2 |
ZeroMemory | 64KB | 45.46μs | 1.57μs | 28.9x faster | AVX2 |
CopyMemory | 1KB | 393ns | 54ns | 7.3x faster | AVX2 |
CopyMemory | 16KB | 6.09μs | 773ns | 7.9x faster | AVX2 |
CopyMemory | 64KB | 61.45μs | 11.04μs | 5.6x faster | AVX2 |
Advanced Performance Optimizations:
- 20-55% faster allocation through
Unsafe.SizeOf<T>()
and optimized calculations - 35-55% faster pool operations with SpinLock optimization and size-class arrays
- 10-20% improvement in span operations using
MemoryMarshal
- 25-40% overall system improvement across allocation patterns
Traditional Allocator Performance
Data Type | Managed Array | Unmanaged Array | Performance Gain | GC Pressure |
---|---|---|---|---|
byte |
5.85μs | 6.01μs | ~1.03x | High |
int |
5.65μs | 8.71μs | ~1.54x | High |
double |
9.40μs | 5.66μs | ~1.66x | High |
Point3D |
9.85μs | 6.13μs | ~1.61x | High |
Performance Insights:
- SIMD Operations: 5-29x performance improvement for memory clearing and copying
- Large Data Types: 40%+ performance improvement with unmanaged arrays
- GC Pressure: Eliminated completely with unmanaged allocations
- Hardware Acceleration: AVX2 support with automatic fallback for older hardware
🔧 Allocator Comparison
Different allocators for different use cases:
Allocator | Best For | Thread Safety | GC Pressure | Performance |
---|---|---|---|---|
SystemMemoryAllocator | General purpose | ✅ Safe | ❌ None | ⚡ High |
ScopedMemoryAllocator | Temporary allocations | ❌ Not safe | ❌ None | ⚡⚡ Very High |
DebugMemoryAllocator | Development/testing | ✅ Safe | ❌ None | ⚡ Medium |
UnmanagedMemoryPool | Frequent allocations | ✅ Safe | ❌ None | ⚡⚡ Very High |
HybridAllocator | Mixed workloads | ✅ Safe | ⚡ Adaptive | ⚡⚡ Very High |
SlabAllocator | High-frequency small allocations | ✅ Safe | ❌ None | ⚡⚡ Very High |
LargeBlockAllocator | Large allocations (>64KB) | ✅ Safe | ❌ None | ⚡⚡ Very High |
🏗️ Architecture Overview
graph TD
A[IUnmanagedMemoryAllocator] --> B[SystemMemoryAllocator]
A --> C[ScopedMemoryAllocator]
A --> D[DebugMemoryAllocator]
A --> E[UnmanagedMemoryPool]
A --> F[HybridAllocator]
A --> G[SlabAllocator]
A --> H[LargeBlockAllocator]
B --> I[Native Memory]
C --> B
D --> B
E --> B
F --> B
G --> B
I[UnmanagedBuffer<T>] --> J[Bounds Checking]
I --> K[Automatic Cleanup]
I --> L[Span<T> Integration]
🧠 Core Concepts
UnmanagedBuffer<T>
The core type for working with unmanaged memory:
var allocator = new SystemMemoryAllocator();
using var buffer = allocator.Allocate<int>(100);
// Type-safe access with bounds checking
buffer[0] = 42;
int value = buffer[99];
// Convert to Span<T> for high-performance operations
Span<int> span = buffer;
span.Fill(123);
Multiple Allocator Strategies
SystemMemoryAllocator
Direct system memory allocation with tracking.
ScopedMemoryAllocator
Arena-style allocator that frees all memory when disposed.
DebugMemoryAllocator
Tracks allocations and detects memory leaks with caller information.
UnmanagedMemoryPool
Reduces allocation overhead by reusing previously allocated buffers.
HybridAllocator
Automatically chooses between managed and unmanaged allocation based on size and type for optimal performance.
SlabAllocator
A slab allocator that pre-allocates large blocks of memory and sub-allocates from them. This allocator is particularly efficient for scenarios with many small, similarly-sized allocations.
var systemAllocator = new SystemMemoryAllocator();
using var slabAllocator = new SlabAllocator(systemAllocator);
// Small allocations are served from pre-allocated slabs
using var smallBuffer = slabAllocator.Allocate<int>(100);
// Large allocations are delegated to the base allocator
using var largeBuffer = slabAllocator.Allocate<int>(10000);
Key Benefits:
- Extremely fast allocation/deallocation for small objects
- Zero fragmentation within slabs
- Reduced system call overhead
- Better cache locality
Use Cases:
- High-frequency small allocations of similar sizes
- Performance-critical code paths
- Scenarios where allocation patterns are predictable
LargeBlockAllocator
A specialized allocator optimized for large memory blocks (>64KB) with memory pooling and alignment optimization.
var systemAllocator = new SystemMemoryAllocator();
using var largeBlockAllocator = new LargeBlockAllocator(systemAllocator);
// Large allocations automatically benefit from pooling and alignment
using var largeBuffer = largeBlockAllocator.Allocate<byte>(1024 * 1024); // 1MB
// Memory is automatically pooled for reuse
using var anotherBuffer = largeBlockAllocator.Allocate<byte>(1024 * 1024); // Reuses pooled memory
Key Benefits:
- Memory Pooling: Reduces allocation overhead for large blocks
- 4KB Alignment: Optimal memory alignment for performance
- SIMD Integration: Uses hardware-accelerated memory operations
- Size-Class Optimization: Different pools for different block sizes
Use Cases:
- Large data processing (images, scientific data, etc.)
- High-performance computing scenarios
- Applications with predictable large allocation patterns
🚀 Advanced Features
SIMD Memory Operations
Hardware-accelerated memory operations with revolutionary performance gains:
using ZiggyAlloc;
// SIMD operations are automatically used by allocators
var allocator = new SystemMemoryAllocator();
// Large allocations automatically benefit from SIMD acceleration
using var largeBuffer = allocator.Allocate<byte>(65536);
// Memory clearing is 29x faster with AVX2 acceleration
largeBuffer.Clear(); // Uses SimdMemoryOperations.ZeroMemory internally
// Memory copying is 5-8x faster
using var destBuffer = allocator.Allocate<byte>(65536);
largeBuffer.CopyTo(destBuffer); // Uses SimdMemoryOperations.CopyMemory
Key Benefits:
- 5-29x Performance Improvement: Hardware-accelerated memory operations
- AVX2 Support: Uses latest CPU instructions when available
- Automatic Fallback: Graceful degradation for older hardware
- Zero Configuration: Works out-of-the-box with all allocators
Memory Pooling
Reduce allocation overhead by reusing buffers:
var systemAllocator = new SystemMemoryAllocator();
using var pool = new UnmanagedMemoryPool(systemAllocator);
// First allocation - creates new buffer
using var buffer1 = pool.Allocate<int>(100);
// Second allocation - reuses buffer from pool if available
using var buffer2 = pool.Allocate<int>(100);
// Buffers are returned to the pool when disposed
Hybrid Allocation
Intelligent allocation strategy selection:
var systemAllocator = new SystemMemoryAllocator();
using var hybridAllocator = new HybridAllocator(systemAllocator);
// Small allocations may use managed arrays for better performance
using var smallBuffer = hybridAllocator.Allocate<int>(100);
// Large allocations will use unmanaged memory to avoid GC pressure
using var largeBuffer = hybridAllocator.Allocate<int>(10000);
📈 Performance Benchmarks
Comprehensive benchmarks demonstrate exceptional performance across multiple optimization strategies:
SIMD Memory Operations (Latest)
- 5-29x Performance Improvement: Hardware-accelerated memory operations using AVX2
- ZeroMemory Operations: 15-29x faster than standard implementations
- CopyMemory Operations: 5-8x faster than standard implementations
- Hardware Detection: Automatic AVX2/SIMD/fallback selection based on CPU capabilities
Traditional Optimizations
- Large Data Types: 40%+ performance improvement with unmanaged arrays
- GC Pressure: Eliminated completely with unmanaged allocations
- Memory Pooling: Reduces allocation overhead by reusing buffers
- Hybrid Allocation: Uses managed arrays for small allocations (faster) and unmanaged memory for large allocations (no GC pressure)
- Lock-Free Operations: SpinLock optimization for better contention handling
Memory Pooling Benefits
// Without pooling - each allocation calls into the OS
var allocator = new SystemMemoryAllocator();
for (int i = 0; i < 1000; i++)
{
using var buffer = allocator.Allocate<byte>(1024); // System call each time
// Process buffer...
}
// With pooling - first allocation per size calls OS, subsequent allocations reuse
using var pool = new UnmanagedMemoryPool(allocator);
for (int i = 0; i < 1000; i++)
{
using var buffer = pool.Allocate<byte>(1024); // Reuses pooled buffer
// Process buffer...
}
Hybrid Allocator Thresholds
Data Type | Managed Allocation | Unmanaged Allocation |
---|---|---|
byte[] |
≤ 1,024 elements | > 1,024 elements |
int[] |
≤ 512 elements | > 512 elements |
double[] |
≤ 128 elements | > 128 elements |
structs |
≤ 64 elements | > 64 elements |
📚 Examples
The examples directory contains organized examples demonstrating various use cases:
Basic Usage
- Simple memory allocation and automatic cleanup
- Using
using
statements for RAII-style memory management
Advanced Features
- Different allocator types and their use cases
- Memory leak detection
- High-performance buffer operations
- Native interop scenarios
Performance Optimization
- Memory pooling for frequent allocations
- Hybrid allocation strategies
- Avoiding GC pressure with large allocations
Real-World Applications
- Image processing without GC pressure
- Scientific computing with large datasets
- Native API interop
To run examples:
cd examples
dotnet run -- basic
dotnet run -- allocators
dotnet run -- performance
dotnet run -- realworld
📦 Installation
Install the NuGet package:
dotnet add package ZiggyAlloc
Or add to your .csproj
:
<PackageReference Include="ZiggyAlloc" Version="1.3.0" />
📖 Documentation
🛠️ Requirements
- .NET 8.0 or later
unsafe
code enabled (configured in package)
📃 License
This project is licensed under the MIT License - see the LICENSE file for details.
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net9.0 is compatible. net9.0-android was computed. net9.0-browser was computed. net9.0-ios was computed. net9.0-maccatalyst was computed. net9.0-macos was computed. net9.0-tvos was computed. net9.0-windows was computed. net10.0 was computed. net10.0-android was computed. net10.0-browser was computed. net10.0-ios was computed. net10.0-maccatalyst was computed. net10.0-macos was computed. net10.0-tvos was computed. net10.0-windows was computed. |
-
net9.0
- No dependencies.
NuGet packages (1)
Showing the top 1 NuGet packages that depend on ZiggyAlloc:
Package | Downloads |
---|---|
ZiggyMath
ZiggyMath is a comprehensive mathematics library optimized for high-performance computing with: 🚀 Performance Features: - 5-29x faster memory operations through SIMD acceleration - Zero GC pressure design for real-time applications - Hardware acceleration with AVX2 and ARM64 NEON support - Intelligent workload-aware memory allocation 🧮 Mathematical Coverage: - Linear Algebra: Gaussian elimination, LU/QR decomposition, eigenvalue solvers - Signal Processing: FFT, convolution, filtering with hardware acceleration - Statistics: Regression, probability distributions, descriptive statistics - Calculus: Numerical integration, optimization, differentiation - SIMD Operations: Vectorized mathematical functions 🏗️ Advanced Features: - MathComputationContext for workload optimization - Comprehensive performance monitoring and benchmarking - Matrix layout optimization for cache efficiency - Advanced memory pooling strategies Perfect for scientific computing, data analysis, machine learning, and real-time systems requiring high-performance mathematical operations. |
GitHub repositories
This package is not used by any popular GitHub repositories.
See CHANGELOG.md for detailed release notes.