Instructor Notes

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IntroductionGraphics Processing UnitParallel by DesignSpeed Benefits

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Using your GPU with CuPyIntroduction to CuPyConvolution in PythonConvolution on the CPU Using SciPyConvolution on the GPU Using CuPyMeasuring performanceValidationA shortcut: performing NumPy routines on the GPUA real world example: image processing for radio astronomySource measurements

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Accelerate your Python code with NumbaUsing Numba to execute Python code on the GPU

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A Better Look at the GPUThe GPU, a High Level View at the HardwareHow Programs are ExecutedDifferent MemoriesAdditional Material

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Your First GPU KernelSumming Two Vectors in PythonSumming Two Vectors in CUDARunning Code on the GPU with CuPyUnderstanding the CUDA CodeComputing Hierarchy in CUDAVectors of Arbitrary Size

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Registers, Global, and Local MemoryRegistersGlobal MemoryLocal Memory

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Shared Memory and SynchronizationShared MemoryThread Synchronization

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Constant MemoryConstant Memory

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Concurrent access to the GPUConcurrently execute two kernels on the same GPUStream synchronizationMeasure execution time using streams and events

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