Multi-round iterative CUDA kernel optimization with automatic compilation recovery, correctness verification, and hardware-aware intelligence.
What is Agentic AI Optimization?
Agentic AI optimization is an autonomous multi-round process where the AI iteratively improves CUDA kernels by profiling, analyzing bottlenecks, applying optimizations, verifying correctness, and repeating until performance targets are met.
Autonomous Workflow
- Profile baseline kernel performance
- Analyze profiling data and identify bottlenecks
- Generate optimized kernel code with AI
- Compile and validate correctness
- Profile optimized version and compare
- Repeat until performance target achieved
Key Capabilities
- Automatic Compilation Recovery: Fix compilation errors autonomously
- Correctness Verification: Validate output matches original kernel
- Architecture-Aware: Optimizations tailored to detected GPU
- Performance Tracking: Historical optimization progress visualization
Unique to RightNow AI: No other CUDA editor offers autonomous multi-round optimization with automatic error recovery and correctness verification.
Iterative Optimization Process
The AI performs multiple optimization rounds, learning from profiling data and previous attempts:
Round 1: Baseline Profiling
- Profile original kernel with Nsight Compute
- Collect SM efficiency, memory bandwidth, occupancy, cache hit rates
- Identify primary bottleneck (memory-bound, compute-bound, latency-bound)
- Establish baseline performance metrics
Round 2-N: Optimization Iterations
Each iteration:
- AI analyzes profiling data and proposes specific optimization strategy
- Generate optimized kernel code targeting identified bottleneck
- Attempt compilation with
nvcc - If compilation fails, invoke automatic error recovery
- If compilation succeeds, verify correctness against original kernel
- Profile optimized kernel and measure performance improvement
- If target not met, analyze new profiling data and iterate
Termination Conditions
- Target Achieved: Performance goal met (e.g., 90% memory bandwidth)
- Diminishing Returns: Improvement less than 5% for 2 consecutive rounds
- Max Iterations: User-configured iteration limit reached (default: 5)
- Theoretical Limit: Approaching hardware theoretical peak performance
Automatic Compilation Recovery
When AI-generated code fails to compile, RightNow AI automatically analyzes errors and fixes them without user intervention.
Error Detection
- Parse
nvcc compiler output for errors - Extract error type, line number, and context
- Classify error category (syntax, type, undefined symbol, etc.)
- Associate errors with generated code changes
Automatic Fixes
- AI analyzes error message and code context
- Generates targeted fix for specific error
- Applies fix and retries compilation
- Maximum 3 recovery attempts per optimization round
Example Compilation Recovery Workflow:
Round 2: Optimization Attempt
├── Generated optimized kernel with shared memory tiling
├── nvcc compilation failed: "error: identifier '__shared__' is undefined"
├── AI Analysis: Missing __shared__ keyword before shared memory declaration
├── Applied fix: Added __shared__ to shared memory array
├── Retry compilation: SUCCESS
├── Correctness verification: PASSED
└── Performance improvement: 1.42x speedup
Recovery successful - continuing to Round 3
Correctness Verification
Every optimized kernel is validated to ensure it produces identical output to the original kernel.
Verification Methods
- Numerical Comparison: Compare floating-point outputs with configurable tolerance (default: 1e-5)
- Exact Integer Comparison: Validate integer outputs match exactly
- Statistical Validation: For stochastic kernels, validate statistical properties
- Shape Validation: Verify output tensor dimensions and memory layout
Test Case Generation
- AI generates diverse input test cases covering edge cases
- Includes boundary conditions (zeros, infinities, NaN handling)
- Tests multiple data sizes to validate scaling behavior
- Validates thread block configurations and launch parameters
Safety First: If verification fails, the optimized kernel is rejected and the AI attempts a different optimization strategy in the next round.
Hardware-Aware Intelligence
Optimization strategies are tailored to your specific GPU architecture, leveraging hardware capabilities and avoiding architectural limitations.
Ampere (RTX 30 Series)
- Async copy for global-to-shared transfers
- 48KB shared memory optimization
- L2 cache residency hints
Ada Lovelace (RTX 40 Series)
- 100KB shared memory exploitation
- Tensor core integration opportunities
- Improved warp scheduling awareness
Hopper (H100)
- Thread block cluster usage
- Distributed shared memory access
- Asynchronous transaction barriers
Example: Matrix Transpose Optimization
Iterative optimization of a naive matrix transpose kernel on RTX 4090:
Baseline Kernel (Round 1):
├── Execution Time: 2.45 ms
├── Memory Bandwidth: 312 GB/s (31% of peak 1008 GB/s)
├── SM Efficiency: 42%
├── Bottleneck: Memory-bound (bank conflicts in global memory writes)
Round 2: Shared Memory Tiling
├── Optimization: Added 32x32 shared memory tile with padding
├── Compilation: SUCCESS
├── Correctness: PASSED (tolerance 1e-6)
├── Execution Time: 1.28 ms (1.91x speedup)
├── Memory Bandwidth: 687 GB/s (68% of peak)
├── SM Efficiency: 71%
├── Improvement: +1.91x, continuing optimization
Round 3: Coalesced Memory Access
├── Optimization: Transposed shared memory access pattern to coalesce writes
├── Compilation: SUCCESS
├── Correctness: PASSED
├── Execution Time: 0.89 ms (2.75x total speedup)
├── Memory Bandwidth: 894 GB/s (89% of peak)
├── SM Efficiency: 86%
├── Improvement: +2.14x from Round 2, continuing optimization
Round 4: Bank Conflict Elimination
├── Optimization: Added +1 column padding to eliminate shared memory bank conflicts
├── Compilation: SUCCESS
├── Correctness: PASSED
├── Execution Time: 0.78 ms (3.14x total speedup)
├── Memory Bandwidth: 951 GB/s (94% of peak)
├── SM Efficiency: 91%
├── Improvement: +1.14x from Round 3
Round 5: Prefetching Optimization
├── Optimization: Added prefetching for next tile during computation
├── Compilation: FAILED (undefined variable in prefetch logic)
├── Recovery: Fixed variable scope issue
├── Retry Compilation: SUCCESS
├── Correctness: PASSED
├── Execution Time: 0.75 ms (3.27x total speedup)
├── Memory Bandwidth: 968 GB/s (96% of peak)
├── SM Efficiency: 93%
├── Improvement: +1.04x from Round 4 (< 5% threshold)
Termination: Diminishing returns detected
Final Performance: 3.27x speedup, 96% memory bandwidth utilization
Optimization successful!
How to Use Agentic AI Optimization
Enable Iterative Mode
- Open sidebar with
Ctrl+L - Toggle "Iterative Optimization" mode in CUDA tools section
- Configure optimization parameters:
- Max iterations (default: 5)
- Performance target (e.g., "90% memory bandwidth")
- Optimization focus (memory, compute, latency)
Start Optimization
Two ways to initiate agentic optimization:
- From Editor: Right-click kernel → "Optimize with Iterative AI"
- From Chat: Type "Iteratively optimize the matmul kernel to achieve 85% SM efficiency"
Monitor Progress
- Real-time optimization display shows current round, performance metrics, and AI reasoning
- Performance chart visualizes improvement trajectory across rounds
- Compilation recovery attempts are logged with error details and fixes
- Correctness verification results displayed with pass/fail status
Interactive Control: You can pause, resume, or stop optimization at any round. Accept optimized kernel or revert to original at any time.
Traditional vs Agentic Optimization
Traditional Manual Optimization
- Developer profiles kernel manually
- Analyzes metrics and identifies bottleneck
- Writes optimized code by hand
- Manually compiles and debugs errors
- Manually verifies correctness
- Re-profiles and repeats
- Time: Hours to days per kernel
Agentic AI Optimization
- AI profiles kernel automatically
- AI analyzes metrics and identifies bottleneck
- AI generates optimized code
- AI compiles and auto-recovers from errors
- AI verifies correctness automatically
- AI re-profiles and repeats autonomously
- Time: Minutes per kernel
