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Posted on 2025-2-21 17:31:57
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Now technology is changing with each passing day, and updates and iterations are too fast! I can't understand some professional terms and terms, and I don't know what they do. Recently, I read some articles ROCm and HIP and looked confused.
What is ROCm?
ROCm is an open-source software platform optimized to extract HPC and AI workload performance from AMD Instinct accelerators and AMD Radeon GPUs while maintaining compatibility with industry software frameworks. ROCm is a software stack consisting primarily of open-source software that provides tools for programming AMD graphics processing units (GPUs), from low-level cores to advanced end-user applications. Specifically, ROCm provides HIP (Heterogeneous Computing Portable Interface), OpenCL, and OpenMP tools, including compilers, high-level libraries, debuggers, analyzers, and runtimes.
ROCm was originally only supported on Linux systems, but now support for Windows systems has been added.
Documentation:The hyperlink login is visible.
After reading this sentence, you may still not know what it does, so let's compare it with NVIDIA's CUDA, as shown in the figure below:
Isn't this understandable, CUDA calls NVIDIA's GPU, and ROCm calls AMD's GPU.
Component support under different systems
Compatibility List:The hyperlink login is visible.
ROCm actually contains many libraries, tools, and runtimes, see the documentation or GitHub repository for details. GitHub address:The hyperlink login is visible.
GPU support list under Windows system:The hyperlink login is visible.
GPU support list under Linux system:The hyperlink login is visible.
What is HIP?
The Heterogeneous Compute Portability Interface (HIP) API is a C++ runtime API and kernel language that allows developers to create portable applications that run on heterogeneous systems using CPUs and AMD GPUs or NVIDIA GPUs in a single source code. HIP provides a simple marshalling language to access the AMD ROCM backend or NVIDIA CUDA backend to build and run application kernels.
As you can see from the figure, HIP unifies the interface, making it convenient for developers to develop code only once, and HIP calls the underlying libraries of different products to call different hardware. The advantage of this is to attract more developers to join, otherwise, it is impossible to compete with the NVIDIA ecosystem.
Documentation:The hyperlink login is visible.
GitHub address:The hyperlink login is visible.
AMD HIP SDK download for Windows:The hyperlink login is visible.
A simple comparison of CUDA vs. ROCm
| characteristic | CUDA | ROCm | | Developers | NVIDIA | AMD | | open source | not | be | | Programming model | CUDA C/C++ | HIP、OpenCL、OpenMP | | Hardware support | NVIDIA GPU | AMD GPU | | ecosystem | Mature, supporting a wide range of libraries and frameworks | It is rapidly evolving to support mainstream AI frameworks | | cost | Higher, NVIDIA GPUs are more expensive | lower, AMD GPUs are more cost-effective | | flexibility | lower, proprietary platform | Higher, open source platform |
How to choose?
Recommended cases where ROCm is selected:
Limited budget: AMD GPUs are more cost-effective and suitable for organizations on a tight budget.
Customization Required: ROCm's open-source nature allows for deep customization and integration with existing infrastructure.
Planned Migration: ROCm's compatibility with CUDA code simplifies the process of migrating from NVIDIA hardware.
Open-Source Focus: ROCm's open-source nature aligns with the needs of organizations that prioritize transparency and flexibility.
It is recommended to choose CUDA:
Performance-Prioritized: NVIDIA GPUs excel in compute-intensive tasks, making them suitable for performance-critical applications.
Pursuit of ease of use: CUDA's mature ecosystem and easy deployment make it a reliable choice for developers.
Enterprise-Grade Support: CUDA's extensive framework support and optimizations make it a preferred choice for enterprise-level AI and HPC applications.
Already with NVIDIA Investment: Organizations that are already deeply dependent on the NVIDIA ecosystem may be better suited to continue using CUDA.
Reference:
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