AMD’s HIP SDK, an official component of the ROCm ecosystem, now offers CUDA support for both professional and consumer GPUs. AMD recognizes the challenges faced by developers of GPU-accelerated applications who often have to manage two separate code bases: one for NVIDIA’s proprietary CUDA API and another for non-NVIDIA GPUs. This task is particularly arduous for smaller teams.
Leveraging the open-source ROCm solution, AMD utilizes the HIP SDK to facilitate the execution of CUDA applications on various GPUs, enabling consumers to run these applications seamlessly.
Through its open-source runtime API, kernel language, and AMD-specific enhancements, the HIP SDK transforms CUDA applications into simplified C++ code. This simplification makes compiling and running these applications on AMD and NVIDIA GPUs much easier. Furthermore, AMD has extended its HIP SDK support to professional GPUs and consumer hardware, including Radeon (e.g., APU).
It’s important to note that while HIP is an integral part of the AMD ROCm computing open-source platform, the AMD ROCm platform primarily targets the HPC and AI domains, mainly focusing on server solutions. In contrast, HIP is explicitly designed for desktop applications.
The benefits of converting existing CUDA applications to run on AMD GPUs are evident. Developers can undertake this conversion without requiring support from AMD.
Additionally, AMD ensures the portability of GPU-accelerated graphics and emulation tools to their hardware with the HIP SDK.
The AMD HIP SDK supports a wide range of GPUs, encompassing professional workstations, gaming GPUs, and APUs. The supported list includes the following:
- Radeon VII
- Radeon RX Vega Series
- Radeon RX 5000 series
- Radeon RX 6000 series
- Radeon RX 7000 series
- Radeon Pro WX 9100
- Radeon Pro W6000 series
It’s worth mentioning that while AMD’s HIP SDK optimizes code for developers, achieving its full software potential may require some fine-tuning by the developers themselves. Additionally, knowing that the HIP SDK is incompatible with machine learning or artificial intelligence tools such as PyTorch or TensorFlow is essential.
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