paracodex-cuda-omp-analysis

name	paracodex-cuda-omp-analysis
description	ParaCodex prompt for cuda omp analysis step.

Loop Classification for OMP Migration - Analysis Phase

Directory: {kernel_dir}/
Files: {file_listing}
Reference: Check Makefile in {kernel_dir}/ (do not modify)

Task

Analyze CUDA kernels in {source_dir}/ and produce {kernel_dir}/analysis.md. Copy source files to {kernel_dir}/ with suffix conversion (.cu → .c or .cpp). Do not modify code in this step.

Process

0. Copy Source Files (Suffix Conversion)

• Copy {file_listing} from {source_dir}/ to {kernel_dir}/.
• Convert suffixes: .cu → .c (C) or .cpp (C++).
• Get baseline output (run in source dir, copy to kernel dir).
• No Code Mod: Preserve content exactly.
• Reference: references/output.md (File Conversion Mapping).

1. Create Environment (Makefile.nvc)

• Create Makefile.nvc using nvc++.
• Create any needed headers/utils.

2. Find Kernels and Loops

grep -n "__global__\|__device__" *.cu 2>/dev/null
grep -n "<<<.*>>>" *.cu 2>/dev/null
grep -n "for\s*(" *.cu 2>/dev/null | head -100
grep -n "for.*iter\|for.*it\|while" *.cu 2>/dev/null | head -50

3. Classify Priority

• CRITICAL: >50% runtime OR called every iteration.
• IMPORTANT: 5-50% runtime.
• SECONDARY: Setup only.
• AVOID: <10K threads.

4. Determine Type & Data Analysis

Reference: references/examples.md (Decision Tree for CUDA Kernels).

• Check __syncthreads, atomicAdd, __shared__.
• Analyze each array (Memory type, Transfer pattern).
• Identify whether the CUDA code's natural unit of work is a single kernel, a fused multi-stage pipeline, or a sequence of distinct kernels.
• Flag any CUDA kernel structure that should be preserved or deliberately fused during OMP migration.
• Check whether the default run size is too small to expose real launch, transfer, or occupancy behavior after migration.
• Mark kernels as small-input sensitive only when tiny runs are likely to bias the OpenMP design choice.
• Recommend a larger profiling size that materially exercises the GPU on the available hardware; do not stop at an input that still produces only short, launch-dominated kernels unless memory or timeout limits force it.
• Read system_info_summary.txt and choose that size against the actual device memory and compute capability; prefer the largest short-run, memory-safe input that should load the GPU meaningfully.
• If the chosen larger size still leaves the GPU weakly utilized or mostly launch/setup bound, mark it insufficient and recommend pushing higher within the practical budget.

5. Create Analysis Report

Reference: references/output.md (Use this template).

• Fill "File Conversion Mapping".
• Fill "Kernel/Loop Nesting".
• Fill "Kernel Details".
• Fill "Summary Table".
• Fill "CUDA-Specific Details".
• Fill "Structural Recommendations".
• Fill "Scalability Check".
• Flag Issues: __syncthreads, shared memory, atomics.

Constraints

• Critical: Identify __syncthreads() patterns (no direct OMP equivalent).
• Copy files with suffix conversion.
• No code modifications.