Optimize For Gpu

GPU Optimization for Python with NVIDIA

Treat GPU acceleration as an evidence-driven optimization, not an automatic rewrite. Preserve the user's numerical and algorithmic contract, measure with representative data, and keep the GPU version only when synchronized end-to-end benchmarks show a useful improvement.

When This Skill Applies

• User wants to speed up numerical/scientific Python code
• User is working with large arrays, matrices, or dataframes
• User mentions CUDA, GPU, NVIDIA, or parallel computing
• User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets
• User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication)
• User is doing machine learning (training, inference, hyperparameter tuning, preprocessing)
• User is doing graph analytics (centrality, community detection, shortest paths, PageRank, etc.)
• User is doing vector search, nearest neighbor search, similarity search, or building a RAG pipeline
• User has Faiss, Annoy, ScaNN, or sklearn NearestNeighbors code that could be GPU-accelerated
• User wants GPU-accelerated interactive dashboards, cross-filtering, or exploratory data analysis on large datasets
• User is doing geospatial analysis (point-in-polygon, spatial joins, trajectory analysis, distance calculations) with GeoPandas or shapely
• User is doing image processing, computer vision, or medical imaging (filtering, segmentation, morphology, feature detection) with scikit-image or OpenCV
• User is working with whole-slide images (WSI), digital pathology, microscopy, or remote sensing imagery
• User is loading large binary data files into GPU memory (numpy.fromfile → cupy, or Python open() → GPU array)
• User needs to read files from S3, HTTP, or WebHDFS directly into GPU memory
• User mentions GPUDirect Storage (GDS) or wants to bypass CPU-memory staging for file IO
• User is doing physics simulation (particles, cloth, fluids, rigid bodies) or differentiable simulation
• User needs mesh operations (ray casting, closest-point queries, signed distance fields) or geometry processing on GPU
• User is doing robotics (kinematics, dynamics, control) with transforms and quaternions
• User has Python simulation loops that could be JIT-compiled to GPU kernels
• User mentions NVIDIA Warp or wants differentiable GPU simulation integrated with PyTorch/JAX
• User is doing simulations, signal processing, financial modeling, bioinformatics, physics, or any compute-intensive work
• User wants to optimize existing code and GPU acceleration is the right answer

Choose the Smallest Suitable Layer

Prefer a maintained library implementation over a custom kernel:

Do not move code out of PyTorch, JAX, TensorFlow, or another GPU-native framework merely to use one of these libraries. First remove CPU round trips and use the framework's compiler, profiler, mixed-precision, and batching facilities.

Treat these as legacy-only:

Full per-library guidance, including when each is the wrong choice and how to combine them, is in references/decision_framework.md. Install commands and CUDA version selection are in references/installation.md. Before/after conversions for every library are in references/code_transformation_patterns.md.

Optimization Workflow

1. Define the contract and baseline

• Capture a representative input, expected output, and acceptable numerical tolerance.
• Measure the current end-to-end path, including input, transfers, compute, and output.
• Profile before changing code. Use CPU profilers for CPU code and identify whether the real limit is compute, memory bandwidth, allocation, transfer, synchronization, or storage.
• Record hardware, package versions, dtypes, shapes, batch size, and warm-up policy with results.

2. Check suitability before porting

GPU execution is promising when the hot path exposes substantial independent work, runs often enough to amortize initialization and transfer, and has a working set that fits available device memory with room for temporaries. Keep a CPU path when the workload is small, mostly sequential, dominated by unsupported operations, or requires frequent host-device round trips.

Do not use fixed row-count thresholds as proof. Benchmark the user's actual shapes and hardware. For out-of-core data, estimate peak working memory and choose chunking, Dask, or a streaming design before allocating.

3. Try the least disruptive implementation

1. If the code already uses a GPU-native framework, optimize within that framework.
2. Try accelerator or backend modes (cudf.pandas, cuml.accel, nx-cugraph).
3. Move to a native GPU API only where accelerator coverage or performance is insufficient.
4. Write a custom kernel only when profiling shows an operation without a suitable library implementation.

Read the relevant library reference before writing code; compatible names can still differ in defaults, dtypes, output types, and supported arguments.

4. Keep a coherent GPU data path

• Transfer inputs once and keep intermediates device-resident.
• Reuse allocations and prefer out= or in-place forms when semantics allow.
• Batch small operations; fuse elementwise work when it removes intermediate arrays.
• Use pinned host memory and non-default streams only after profiling shows transfer overlap matters.
• Choose float32, mixed precision, or reduced-precision storage only when the contract permits it.

5. Validate semantics before speed

• Compare CPU and GPU outputs on small deterministic fixtures and representative data.
• Use explicit tolerances for floating-point results and test edge cases, NaNs, ordering, and dtypes.
• For approximate nearest-neighbor indexes, report recall@k against exact search; do not compare an exact CPU algorithm with an approximate GPU algorithm as if they were equivalent.
• Check accelerator warnings and logs for CPU fallback.

6. Benchmark GPU code correctly

GPU work is asynchronous, so a CPU timer around an unsynchronized call measures enqueue time. Warm up context creation and JIT compilation, then use CUDA events or a library-aware timer:

from cupyx.profiler import benchmark

print(benchmark(gpu_function, (arg1, arg2), n_warmup=10, n_repeat=100))

Use %gpu_timeit in notebooks, Nsight Systems (nsys) for end-to-end timelines, and Nsight Compute (ncu) for kernel analysis. Report both synchronized kernel/region time and realistic end-to-end latency; include transfer and conversion costs when production pays them.

7. Keep, revise, or reject the port

Retain the GPU path only when it passes correctness checks and improves the metric the user cares about on representative data. If it does not, explain whether the limiting factor is problem size, transfers, unsupported fallback, memory pressure, launch granularity, or the algorithm itself.

Important Notes

• Provide a CPU fallback when the application requires portability; otherwise fail early with a clear hardware and dependency error.
• Test numerical correctness against CPU results (GPU floating point may differ slightly due to operation ordering)
• GPU memory is limited — for datasets larger than GPU memory, consider chunking or using RAPIDS Dask for multi-GPU
• Prefer the CUDA Array Interface or DLPack for supported zero-copy interchange, but verify device, dtype, contiguity, ownership, and stream semantics rather than assuming every conversion is free.

Reference Files

Before writing any GPU optimization code, read the relevant reference file(s):

Read the specific reference before writing code — they contain detailed API patterns, optimization techniques, and pitfalls specific to each library.