How to optimize the construction of a histogram in cupy on the GPU?
Best insight from top research papers
To optimize the construction of a histogram in CuPy on the GPU, several strategies can be employed based on research findings. Techniques such as utilizing multi-core CPU and many-core GPU computing, like CUDA, can significantly enhance performance by achieving up to 7x speedup compared to CPU implementations . Implementing histogram replication, padding, and interleaved read access can effectively reduce collisions among threads, improving parallelism and performance on GPUs . Additionally, automating the optimization process through tools like an OpenACC optimizer can enhance performance portability by automatically rewriting code blocks for accelerated histogram computation on GPUs . By incorporating these strategies, CuPy can efficiently construct histograms on the GPU, achieving high performance and scalability for various applications.
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| Papers (5) | Insight |
|---|---|
| Optimize histogram construction in CuPy on GPU by using an improved parallel prefix sum algorithm with exact indexing to prevent shared memory leakage and efficiently handle big images. | |
01 Feb 2016 | An OpenACC optimizer automates histogram computation optimization on GPUs, enhancing performance portability by distributing atomic operations over multiple local histograms for accelerated construction. |
| Optimize histogram construction in CuPy on GPU by leveraging CUDA for parallel implementation, achieving significant speedup compared to CPU-based methods. | |
| Optimize histogram construction on GPU in CuPy by using histogram replication to eliminate conflicts, padding to reduce bank conflicts, and interleaved read access for improved performance. | |
| Optimize histogram construction in CuPy on GPU by implementing work-efficient techniques, supporting various operators, and utilizing hardware atomic operations efficiently, as demonstrated in the paper. |
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