GigaAPI for GPU Parallelization
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912305440096256 |
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| author | Suvarna, M. Tehrani, O. |
| author_facet | Suvarna, M. Tehrani, O. |
| contents | GigaAPI is a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential. The API offers a comprehensive set of functionalities, including fundamental GPU operations, image processing, and complex GPU tasks, abstracting away the intricacies of low-level CUDA and C++ programming. GigaAPI's modular design aims to inspire future NVIDIA researchers to create a generalized, dynamic, extensible, and cross-GPU architecture-compatible API. Through experiments and simulations, we demonstrate the general efficiency gains achieved by leveraging GigaAPI's simplified multi-GPU programming model and showcase our learning experience through setup and other aspects, as we were interested in learning complex CUDA programming and parallelism. We hope that this contributes to the democratization of parallel GPU computing, enabling researchers and practitioners to unlock new possibilities across diverse domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01266 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GigaAPI for GPU Parallelization Suvarna, M. Tehrani, O. Distributed, Parallel, and Cluster Computing Hardware Architecture Performance GigaAPI is a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential. The API offers a comprehensive set of functionalities, including fundamental GPU operations, image processing, and complex GPU tasks, abstracting away the intricacies of low-level CUDA and C++ programming. GigaAPI's modular design aims to inspire future NVIDIA researchers to create a generalized, dynamic, extensible, and cross-GPU architecture-compatible API. Through experiments and simulations, we demonstrate the general efficiency gains achieved by leveraging GigaAPI's simplified multi-GPU programming model and showcase our learning experience through setup and other aspects, as we were interested in learning complex CUDA programming and parallelism. We hope that this contributes to the democratization of parallel GPU computing, enabling researchers and practitioners to unlock new possibilities across diverse domains. |
| title | GigaAPI for GPU Parallelization |
| topic | Distributed, Parallel, and Cluster Computing Hardware Architecture Performance |
| url | https://arxiv.org/abs/2504.01266 |