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Use for CUDA\u002FGPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I\u002FO workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1554,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Foptimize-for-gpu\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"for","optimize for gpu","gpu","optimize","use","profiling","vector-search","numpy","scipy","scikit-learn","data-science","scientific","processing","CPU","Raft","GPU Acceleration","plausible","image","option","parallel","access","simulation","warp","NVIDIA","file","pandas","vector","search","learn","performance","memory","python","2026-11-10 10:12:30",false,"---\nname: optimize-for-gpu\ndescription: GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA\u002FGPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I\u002FO workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.\nlicense: MIT\ncompatibility: Requires an NVIDIA CUDA-capable GPU for GPU execution. RAPIDS 26.06 requires Python 3.11+ on Linux or WSL2 and matching CUDA 12 or 13 wheels. Package installation needs network access.\nmetadata:\n  version: '1.4'\n  category: data-science-and-ml\n  maintainer: Kalaris Labs\n  tags: GPU acceleration, CUDA, performance optimization, scientific Python\n---\n\n# GPU Optimization for Python with NVIDIA\n\nTreat GPU acceleration as an evidence-driven optimization, not an automatic rewrite. Preserve the\nuser's numerical and algorithmic contract, measure with representative data, and keep the GPU\nversion only when synchronized end-to-end benchmarks show a useful improvement.\n\n## When This Skill Applies\n\n- User wants to speed up numerical\u002Fscientific Python code\n- User is working with large arrays, matrices, or dataframes\n- User mentions CUDA, GPU, NVIDIA, or parallel computing\n- User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets\n- User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication)\n- User is doing machine learning (training, inference, hyperparameter tuning, preprocessing)\n- User is doing graph analytics (centrality, community detection, shortest paths, PageRank, etc.)\n- User is doing vector search, nearest neighbor search, similarity search, or building a RAG pipeline\n- User has Faiss, Annoy, ScaNN, or sklearn NearestNeighbors code that could be GPU-accelerated\n- User wants GPU-accelerated interactive dashboards, cross-filtering, or exploratory data analysis on large datasets\n- User is doing geospatial analysis (point-in-polygon, spatial joins, trajectory analysis, distance calculations) with GeoPandas or shapely\n- User is doing image processing, computer vision, or medical imaging (filtering, segmentation, morphology, feature detection) with scikit-image or OpenCV\n- User is working with whole-slide images (WSI), digital pathology, microscopy, or remote sensing imagery\n- User is loading large binary data files into GPU memory (numpy.fromfile → cupy, or Python open() → GPU array)\n- User needs to read files from S3, HTTP, or WebHDFS directly into GPU memory\n- User mentions GPUDirect Storage (GDS) or wants to bypass CPU-memory staging for file IO\n- User is doing physics simulation (particles, cloth, fluids, rigid bodies) or differentiable simulation\n- User needs mesh operations (ray casting, closest-point queries, signed distance fields) or geometry processing on GPU\n- User is doing robotics (kinematics, dynamics, control) with transforms and quaternions\n- User has Python simulation loops that could be JIT-compiled to GPU kernels\n- User mentions NVIDIA Warp or wants differentiable GPU simulation integrated with PyTorch\u002FJAX\n- User is doing simulations, signal processing, financial modeling, bioinformatics, physics, or any compute-intensive work\n- User wants to optimize existing code and GPU acceleration is the right answer\n\n## Choose the Smallest Suitable Layer\n\nPrefer a maintained library implementation over a custom kernel:\n\n| Existing workload | Preferred path | Use for |\n| --- | --- | --- |\n| NumPy \u002F SciPy | **CuPy** | arrays, sparse matrices, linear algebra, FFTs, signal processing |\n| pandas | **cudf.pandas**, then **cuDF** | accelerator mode first; native API for more control |\n| scikit-learn | **cuml.accel**, then **cuML** | accelerator mode first; native estimators as needed |\n| NetworkX | **nx-cugraph**, then **cuGraph** | backend dispatch first; native graph API at scale |\n| scikit-image | **cuCIM** | GPU image processing and whole-slide imaging |\n| Faiss \u002F Annoy \u002F k-NN | **cuVS** | exact and approximate vector search |\n| Raw or remote file I\u002FO | **KvikIO** | GPU buffers and GPUDirect Storage |\n| Custom array kernels | **Numba-CUDA-MLIR** for new work; **Numba-CUDA** for existing code | explicit SIMT kernels and shared memory |\n| Spatial or differentiable kernels | **Warp** | geometry, simulation kernels, robotics, autodiff |\n| High-level physics simulation | **Newton** | maintained engine that succeeds the removed `warp.sim` module |\n| Low-level RAPIDS primitives | **RAFT** (`pylibraft`) | sparse eigensolvers, resources, multi-GPU building blocks |\n\nDo not move code out of PyTorch, JAX, TensorFlow, or another GPU-native framework merely to use\none of these libraries. First remove CPU round trips and use the framework's compiler, profiler,\nmixed-precision, and batching facilities.\n\nTreat these as legacy-only:\n\n| Project | Status | Guidance |\n| --- | --- | --- |\n| **cuxfilter** | Final release 26.06 | Maintain existing dashboards only. For new work, combine cuDF with HoloViews\u002FhvPlot\u002FDatashader and serve with Panel, Dash, Streamlit, or Bokeh. |\n| **cuSpatial** | Archived at 25.04 | Use only in an isolated legacy environment. For new work, keep geometry in GeoPandas\u002FShapely and accelerate compatible tabular stages with cuDF. |\n\nFull per-library guidance, including when each is the *wrong* choice and how to combine\nthem, is in [references\u002Fdecision_framework.md](references\u002Fdecision_framework.md).\nInstall commands and CUDA version selection are in\n[references\u002Finstallation.md](references\u002Finstallation.md). Before\u002Fafter conversions for\nevery library are in\n[references\u002Fcode_transformation_patterns.md](references\u002Fcode_transformation_patterns.md).\n\n## Optimization Workflow\n\n### 1. Define the contract and baseline\n\n- Capture a representative input, expected output, and acceptable numerical tolerance.\n- Measure the current end-to-end path, including input, transfers, compute, and output.\n- Profile before changing code. Use CPU profilers for CPU code and identify whether the real limit\n  is compute, memory bandwidth, allocation, transfer, synchronization, or storage.\n- Record hardware, package versions, dtypes, shapes, batch size, and warm-up policy with results.\n\n### 2. Check suitability before porting\n\nGPU execution is promising when the hot path exposes substantial independent work, runs often\nenough to amortize initialization and transfer, and has a working set that fits available device\nmemory with room for temporaries. Keep a CPU path when the workload is small, mostly sequential,\ndominated by unsupported operations, or requires frequent host-device round trips.\n\nDo not use fixed row-count thresholds as proof. Benchmark the user's actual shapes and hardware.\nFor out-of-core data, estimate peak working memory and choose chunking, Dask, or a streaming design\nbefore allocating.\n\n### 3. Try the least disruptive implementation\n\n1. If the code already uses a GPU-native framework, optimize within that framework.\n2. Try accelerator or backend modes (`cudf.pandas`, `cuml.accel`, `nx-cugraph`).\n3. Move to a native GPU API only where accelerator coverage or performance is insufficient.\n4. Write a custom kernel only when profiling shows an operation without a suitable library\n   implementation.\n\nRead the relevant library reference before writing code; compatible names can still differ in\ndefaults, dtypes, output types, and supported arguments.\n\n### 4. Keep a coherent GPU data path\n\n- Transfer inputs once and keep intermediates device-resident.\n- Reuse allocations and prefer `out=` or in-place forms when semantics allow.\n- Batch small operations; fuse elementwise work when it removes intermediate arrays.\n- Use pinned host memory and non-default streams only after profiling shows transfer overlap matters.\n- Choose `float32`, mixed precision, or reduced-precision storage only when the contract permits it.\n\n### 5. Validate semantics before speed\n\n- Compare CPU and GPU outputs on small deterministic fixtures and representative data.\n- Use explicit tolerances for floating-point results and test edge cases, NaNs, ordering, and dtypes.\n- For approximate nearest-neighbor indexes, report recall@k against exact search; do not compare an\n  exact CPU algorithm with an approximate GPU algorithm as if they were equivalent.\n- Check accelerator warnings and logs for CPU fallback.\n\n### 6. Benchmark GPU code correctly\n\nGPU work is asynchronous, so a CPU timer around an unsynchronized call measures enqueue time.\nWarm up context creation and JIT compilation, then use CUDA events or a library-aware timer:\n\n```python\nfrom cupyx.profiler import benchmark\n\nprint(benchmark(gpu_function, (arg1, arg2), n_warmup=10, n_repeat=100))\n```\n\nUse `%gpu_timeit` in notebooks, Nsight Systems (`nsys`) for end-to-end timelines, and Nsight\nCompute (`ncu`) for kernel analysis. Report both synchronized kernel\u002Fregion time and realistic\nend-to-end latency; include transfer and conversion costs when production pays them.\n\n### 7. Keep, revise, or reject the port\n\nRetain the GPU path only when it passes correctness checks and improves the metric the user cares\nabout on representative data. If it does not, explain whether the limiting factor is problem size,\ntransfers, unsupported fallback, memory pressure, launch granularity, or the algorithm itself.\n\n## Important Notes\n\n- Provide a CPU fallback when the application requires portability; otherwise fail early with a\n  clear hardware and dependency error.\n- Test numerical correctness against CPU results (GPU floating point may differ slightly due to operation ordering)\n- GPU memory is limited — for datasets larger than GPU memory, consider chunking or using RAPIDS Dask for multi-GPU\n- Prefer the CUDA Array Interface or DLPack for supported zero-copy interchange, but verify device,\n  dtype, contiguity, ownership, and stream semantics rather than assuming every conversion is free.\n\n## Reference Files\n\nBefore writing any GPU optimization code, read the relevant reference file(s):\n\n| File | When to Read |\n|------|-------------|\n| `references\u002Fcupy.md` | User has NumPy\u002FSciPy code, or needs array operations on GPU |\n| `references\u002Fnumba.md` | User has existing Numba-CUDA code or needs explicit SIMT kernels; note the migration path to Numba-CUDA-MLIR |\n| `references\u002Fcudf.md` | User has pandas code, or needs dataframe operations on GPU |\n| `references\u002Fcuml.md` | User has scikit-learn code, or needs ML training\u002Finference\u002Fpreprocessing on GPU |\n| `references\u002Fcugraph.md` | User has NetworkX code, or needs graph analytics on GPU |\n| `references\u002Fwarp.md` | User needs GPU kernels for simulation, spatial computing, mesh\u002Fvolume queries, differentiable programming, or robotics; use Newton for a high-level physics engine |\n| `references\u002Fkvikio.md` | User needs high-performance file IO to\u002Ffrom GPU, GPUDirect Storage, reading S3\u002FHTTP to GPU, or Zarr on GPU |\n| `references\u002Fcuxfilter.md` | User maintains or explicitly requests cuxfilter (sunset — 26.06 is the final release) |\n| `references\u002Fcucim.md` | User has scikit-image code, or needs image processing, digital pathology, or WSI reading on GPU |\n| `references\u002Fcuvs.md` | User needs vector search, nearest neighbors, similarity search, or RAG retrieval on GPU |\n| `references\u002Fcuspatial.md` | User maintains or explicitly requests cuSpatial (archived — frozen at 25.04 and isolated from current RAPIDS) |\n| `references\u002Fraft.md` | User needs sparse eigensolvers, device memory management, or multi-GPU primitives |\n\nRead the specific reference before writing code — they contain detailed API patterns, optimization techniques, and pitfalls specific to each library.\n\n## Related skills\n\n- `dask`: Distributed computing for larger-than-RAM pandas\u002FNumPy workflows.\n","# GPU Optimization for Python with NVIDIA\n\nTreat GPU acceleration as an evidence-driven optimization, not an automatic rewrite. Preserve the\nuser's numerical and algorithmic contract, measure with representative data, and keep the GPU\nversion only when synchronized end-to-end benchmarks show a useful improvement.\n\n## When This Skill Applies\n\n- User wants to speed up numerical\u002Fscientific Python code\n- User is working with large arrays, matrices, or dataframes\n- User mentions CUDA, GPU, NVIDIA, or parallel computing\n- User has NumPy, pandas, SciPy, scikit-learn, NetworkX, or scipy.sparse.linalg code that processes large datasets\n- User needs low-level GPU primitives (sparse eigensolvers, device memory management, multi-GPU communication)\n- User is doing machine learning (training, inference, hyperparameter tuning, preprocessing)\n- User is doing graph analytics (centrality, community detection, shortest paths, PageRank, etc.)\n- User is doing vector search, nearest neighbor search, similarity search, or building a RAG pipeline\n- User has Faiss, Annoy, ScaNN, or sklearn NearestNeighbors code that could be GPU-accelerated\n- User wants GPU-accelerated interactive dashboards, cross-filtering, or exploratory data analysis on large datasets\n- User is doing geospatial analysis (point-in-polygon, spatial joins, trajectory analysis, distance calculations) with GeoPandas or shapely\n- User is doing image processing, computer vision, or medical imaging (filtering, segmentation, morphology, feature detection) with scikit-image or OpenCV\n- User is working with whole-slide images (WSI), digital pathology, microscopy, or remote sensing imagery\n- User is loading large binary data files into GPU memory (numpy.fromfile → cupy, or Python open() → GPU array)\n- User needs to read files from S3, HTTP, or WebHDFS directly into GPU memory\n- User mentions GPUDirect Storage (GDS) or wants to bypass CPU-memory staging for file IO\n- User is doing physics simulation (particles, cloth, fluids, rigid bodies) or differentiable simulation\n- User needs mesh operations (ray casting, closest-point queries, signed distance fields) or geometry processing on GPU\n- User is doing robotics (kinematics, dynamics, control) with transforms and quaternions\n- User has Python simulation loops that could be JIT-compiled to GPU kernels\n- User mentions NVIDIA Warp or wants differentiable GPU simulation integrated with PyTorch\u002FJAX\n- User is doing simulations, signal processing, financial modeling, bioinformatics, physics, or any compute-intensive work\n- User wants to optimize existing code and GPU acceleration is the right answer\n\n## Choose the Smallest Suitable Layer\n\nPrefer a maintained library implementation over a custom kernel:\n\n| Existing workload | Preferred path | Use for |\n| --- | --- | --- |\n| NumPy \u002F SciPy | **CuPy** | arrays, sparse matrices, linear algebra, FFTs, signal processing |\n| pandas | **cudf.pandas**, then **cuDF** | accelerator mode first; native API for more control |\n| scikit-learn | **cuml.accel**, then **cuML** | accelerator mode first; native estimators as needed |\n| NetworkX | **nx-cugraph**, then **cuGraph** | backend dispatch first; native graph API at scale |\n| scikit-image | **cuCIM** | GPU image processing and whole-slide imaging |\n| Faiss \u002F Annoy \u002F k-NN | **cuVS** | exact and approximate vector search |\n| Raw or remote file I\u002FO | **KvikIO** | GPU buffers and GPUDirect Storage |\n| Custom array kernels | **Numba-CUDA-MLIR** for new work; **Numba-CUDA** for existing code | explicit SIMT kernels and shared memory |\n| Spatial or differentiable kernels | **Warp** | geometry, simulation kernels, robotics, autodiff |\n| High-level physics simulation | **Newton** | maintained engine that succeeds the removed `warp.sim` module |\n| Low-level RAPIDS primitives | **RAFT** (`pylibraft`) | sparse eigensolvers, resources, multi-GPU building blocks |\n\nDo not move code out of PyTorch, JAX, TensorFlow, or another GPU-native framework merely to use\none of these libraries. First remove CPU round trips and use the framework's compiler, profiler,\nmixed-precision, and batching facilities.\n\nTreat these as legacy-only:\n\n| Project | Status | Guidance |\n| --- | --- | --- |\n| **cuxfilter** | Final release 26.06 | Maintain existing dashboards only. For new work, combine cuDF with HoloViews\u002FhvPlot\u002FDatashader and serve with Panel, Dash, Streamlit, or Bokeh. |\n| **cuSpatial** | Archived at 25.04 | Use only in an isolated legacy environment. For new work, keep geometry in GeoPandas\u002FShapely and accelerate compatible tabular stages with cuDF. |\n\nFull per-library guidance, including when each is the *wrong* choice and how to combine\nthem, is in [references\u002Fdecision_framework.md](references\u002Fdecision_framework.md).\nInstall commands and CUDA version selection are in\n[references\u002Finstallation.md](references\u002Finstallation.md). Before\u002Fafter conversions for\nevery library are in\n[references\u002Fcode_transformation_patterns.md](references\u002Fcode_transformation_patterns.md).\n\n## Optimization Workflow\n\n### 1. Define the contract and baseline\n\n- Capture a representative input, expected output, and acceptable numerical tolerance.\n- Measure the current end-to-end path, including input, transfers, compute, and output.\n- Profile before changing code. Use CPU profilers for CPU code and identify whether the real limit\n  is compute, memory bandwidth, allocation, transfer, synchronization, or storage.\n- Record hardware, package versions, dtypes, shapes, batch size, and warm-up policy with results.\n\n### 2. Check suitability before porting\n\nGPU execution is promising when the hot path exposes substantial independent work, runs often\nenough to amortize initialization and transfer, and has a working set that fits available device\nmemory with room for temporaries. Keep a CPU path when the workload is small, mostly sequential,\ndominated by unsupported operations, or requires frequent host-device round trips.\n\nDo not use fixed row-count thresholds as proof. Benchmark the user's actual shapes and hardware.\nFor out-of-core data, estimate peak working memory and choose chunking, Dask, or a streaming design\nbefore allocating.\n\n### 3. Try the least disruptive implementation\n\n1. If the code already uses a GPU-native framework, optimize within that framework.\n2. Try accelerator or backend modes (`cudf.pandas`, `cuml.accel`, `nx-cugraph`).\n3. Move to a native GPU API only where accelerator coverage or performance is insufficient.\n4. Write a custom kernel only when profiling shows an operation without a suitable library\n   implementation.\n\nRead the relevant library reference before writing code; compatible names can still differ in\ndefaults, dtypes, output types, and supported arguments.\n\n### 4. Keep a coherent GPU data path\n\n- Transfer inputs once and keep intermediates device-resident.\n- Reuse allocations and prefer `out=` or in-place forms when semantics allow.\n- Batch small operations; fuse elementwise work when it removes intermediate arrays.\n- Use pinned host memory and non-default streams only after profiling shows transfer overlap matters.\n- Choose `float32`, mixed precision, or reduced-precision storage only when the contract permits it.\n\n### 5. Validate semantics before speed\n\n- Compare CPU and GPU outputs on small deterministic fixtures and representative data.\n- Use explicit tolerances for floating-point results and test edge cases, NaNs, ordering, and dtypes.\n- For approximate nearest-neighbor indexes, report recall@k against exact search; do not compare an\n  exact CPU algorithm with an approximate GPU algorithm as if they were equivalent.\n- Check accelerator warnings and logs for CPU fallback.\n\n### 6. Benchmark GPU code correctly\n\nGPU work is asynchronous, so a CPU timer around an unsynchronized call measures enqueue time.\nWarm up context creation and JIT compilation, then use CUDA events or a library-aware timer:\n\n```python\nfrom cupyx.profiler import benchmark\n\nprint(benchmark(gpu_function, (arg1, arg2), n_warmup=10, n_repeat=100))\n```\n\nUse `%gpu_timeit` in notebooks, Nsight Systems (`nsys`) for end-to-end timelines, and Nsight\nCompute (`ncu`) for kernel analysis. Report both synchronized kernel\u002Fregion time and realistic\nend-to-end latency; include transfer and conversion costs when production pays them.\n\n### 7. Keep, revise, or reject the port\n\nRetain the GPU path only when it passes correctness checks and improves the metric the user cares\nabout on representative data. If it does not, explain whether the limiting factor is problem size,\ntransfers, unsupported fallback, memory pressure, launch granularity, or the algorithm itself.\n\n## Important Notes\n\n- Provide a CPU fallback when the application requires portability; otherwise fail early with a\n  clear hardware and dependency error.\n- Test numerical correctness against CPU results (GPU floating point may differ slightly due to operation ordering)\n- GPU memory is limited — for datasets larger than GPU memory, consider chunking or using RAPIDS Dask for multi-GPU\n- Prefer the CUDA Array Interface or DLPack for supported zero-copy interchange, but verify device,\n  dtype, contiguity, ownership, and stream semantics rather than assuming every conversion is free.\n\n## Reference Files\n\nBefore writing any GPU optimization code, read the relevant reference file(s):\n\n| File | When to Read |\n|------|-------------|\n| `references\u002Fcupy.md` | User has NumPy\u002FSciPy code, or needs array operations on GPU |\n| `references\u002Fnumba.md` | User has existing Numba-CUDA code or needs explicit SIMT kernels; note the migration path to Numba-CUDA-MLIR |\n| `references\u002Fcudf.md` | User has pandas code, or needs dataframe operations on GPU |\n| `references\u002Fcuml.md` | User has scikit-learn code, or needs ML training\u002Finference\u002Fpreprocessing on GPU |\n| `references\u002Fcugraph.md` | User has NetworkX code, or needs graph analytics on GPU |\n| `references\u002Fwarp.md` | User needs GPU kernels for simulation, spatial computing, mesh\u002Fvolume queries, differentiable programming, or robotics; use Newton for a high-level physics engine |\n| `references\u002Fkvikio.md` | User needs high-performance file IO to\u002Ffrom GPU, GPUDirect Storage, reading S3\u002FHTTP to GPU, or Zarr on GPU |\n| `references\u002Fcuxfilter.md` | User maintains or explicitly requests cuxfilter (sunset — 26.06 is the final release) |\n| `references\u002Fcucim.md` | User has scikit-image code, or needs image processing, digital pathology, or WSI reading on GPU |\n| `references\u002Fcuvs.md` | User needs vector search, nearest neighbors, similarity search, or RAG retrieval on GPU |\n| `references\u002Fcuspatial.md` | User maintains or explicitly requests cuSpatial (archived — frozen at 25.04 and isolated from current RAPIDS) |\n| `references\u002Fraft.md` | User needs sparse eigensolvers, device memory management, or multi-GPU primitives |\n\nRead the specific reference before writing code — they contain detailed API patterns, optimization techniques, and pitfalls specific to each library.\n\n## Related skills\n\n- `dask`: Distributed computing for larger-than-RAM pandas\u002FNumPy workflows.\n",{"name":5,"license":15,"metadata":61,"description":7,"compatibility":66},{"tags":62,"version":63,"category":64,"maintainer":65},"GPU acceleration, CUDA, performance optimization, scientific Python","1.4","data-science-and-ml","Kalaris Labs","Requires an NVIDIA CUDA-capable GPU for GPU execution. 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