Expand description
GPU Kernel Optimization for Specialized Quantum Operations
This module provides highly optimized GPU kernels for quantum simulation, including specialized implementations for common gates, fused operations, and memory-optimized algorithms for large state vectors.
§Features
- Specialized kernels for common gates (H, X, Y, Z, CNOT, CZ, etc.)
- Fused gate sequences for reduced memory bandwidth
- Memory-coalesced access patterns for GPU efficiency
- Warp-level optimizations for NVIDIA GPUs
- Shared memory utilization for reduced global memory access
- Streaming execution for overlapped computation and data transfer
Structs§
- Compiled
Kernel - Compiled kernel ready for execution
- Custom
Kernel - Custom kernel implementation
- Fused
Kernel - Fused kernel for multiple operations
- GPUKernel
Config - Configuration for GPU kernel optimization
- GPUKernel
Optimizer - GPU kernel optimization framework for quantum simulation
- Kernel
Exec Params - Kernel execution parameters
- Kernel
Registry - Registry of specialized GPU kernels
- Kernel
Stats - Kernel execution statistics
- Memory
Layout Optimizer - Memory layout optimizer for GPU operations
- Single
Qubit Kernel - Single-qubit kernel implementation
- TwoQubit
Kernel - Two-qubit kernel implementation
Enums§
- Grid
Size Method - Method for calculating grid size
- Memory
Access Pattern - Memory access patterns for kernels
- Memory
Layout Strategy - Memory layout strategies
- Optimization
Level - Optimization levels for kernels
- Single
Qubit Kernel Type - Types of single-qubit kernels
- TwoQubit
Kernel Type - Types of two-qubit kernels