What Is CUDA? How It Works and How It Compares to ROCm, ZLUDA

What Is CUDA

CUDA is a parallel computing platform released by NVIDIA in 2006. Its official name is Compute Unified Device Architecture. It made practical the use of GPUs for large-scale computation beyond graphics rendering. Its defining feature is providing hardware capabilities and a development environment as an integrated package. The NVIDIA official documentation (https://docs.nvidia.com/cuda/) defines its execution model and memory hierarchy. It is now established as the de facto standard for AI training and scientific computing. It positions the GPU as an accelerator that complements sequential processing by the CPU. Its strength is the ability to drive thousands of compute units simultaneously. The term refers not to a language alone, but to the entire ecosystem.

How It Works

The basic model is a division of labor in which the CPU controls overall processing and the GPU handles heavy computation. The CPU side is called the host, and the GPU side is called the device. The workflow consists of memory allocation and transfer, kernel launch, and result retrieval. A kernel is a function that runs on the GPU, executed in parallel by many threads. Threads are grouped into blocks, and a collection of blocks is called a grid. This hierarchy corresponds to the configuration of compute units on the GPU. The memory hierarchy is also layered. Registers and shared memory are fast but small in capacity. Global memory is large in capacity but has high latency. Constant memory and texture memory are high-speed areas for limited uses. Success or failure of computation often depends on the design of memory transfers. PCI Express and NVLink bandwidth determine overall performance. Overlapping asynchronous transfers with computation is the key point of optimization.

Development Environment and Key Components

The core of development is the CUDA Toolkit. It includes the nvcc compiler, the runtime environment, and various libraries. The main included components are as follows.

• cuBLAS: Basic library for matrix operations
• cuDNN: Primitives for deep learning
• cuFFT: Fast Fourier Transform
• NCCL: Collective communication across multiple GPUs
• Thrust: Collection of parallel algorithms
• Nsight Systems, Nsight Compute: Performance analysis tools Compute Capability, which indicates computing capability, is also important. Available instructions and precision formats differ by generation. Matching driver and Toolkit versions is also essential. When using containers, nvidia-container-toolkit serves as the bridge. Comparison with the AMD ROCm documentation (https://rocm.docs.amd.com/) deepens understanding.

Real-World Use Cases

The first use is training and inference for deep learning. PyTorch and TensorFlow have been optimized on the assumption of CUDA. The combination of cuDNN and NCCL forms the basis for distributed training. For inference infrastructure, optimization with TensorRT is widely used. The second use is high-performance computing. It is used in fluid analysis, molecular dynamics, and weather forecasting. The third use is image and video processing. NVDEC and NPP are used for decoding, encoding, and real-time analysis. The fourth use is finance and search processing. High parallelism is leveraged in Monte Carlo methods and cryptanalysis. In practice, exclusive use of machines with 8 GPUs is more common than a single GPU. In the cloud, different models are used for training and inference. H100 and A100 are often adopted for training, and L40S and T4 for inference.

How It Differs from ROCm

ROCm is an open computing platform maintained by AMD. Supported GPUs are the Radeon and Instinct series. HIP provides a programming model equivalent to CUDA. The HIPIFY tool can convert code derived from CUDA. The differences can be summarized in the following points.

• Supported hardware: CUDA is NVIDIA-only, ROCm is AMD-centered
• Openness: CUDA has many closed parts, ROCm has a wider open scope
• Supported OS: CUDA is well supported on Windows and Linux, ROCm is Linux-centered
• Libraries: Correspondence of cuDNN to MIOpen, and NCCL to RCCL
• Adoption record: CUDA has more trained assets and documentation Availability of existing assets, rather than performance differences, determines the choice. If existing code depends on cuDNN optimizations, migration effort increases. New builds on Instinct are common in scientific computing and public institutions. AMD machines in the cloud sometimes have lower unit prices and are chosen for cost-effectiveness.

How It Differs from ZLUDA

ZLUDA is a compatibility layer for running code written for CUDA on other vendors’ GPUs. It was initially developed for Intel, with support for AMD added later. Its implementation can be confirmed in the ZLUDA public repository (https://github.com/vosen/ZLUDA). It works by remapping CUDA API calls to HIP or other methods. It aims to run existing binaries without recompilation. Its positioning differs from ROCm. ROCm is an alternative platform, while ZLUDA is a bridging layer. Attention to the legal background is also necessary. In 2024, developments surrounding development support were reported, and there was a period when development stalled. Its maintenance as an open project was later announced. Immature aspects remain in terms of performance guarantees and long-term maintenance. Its use is centered on validation and as a buffer during the migration period.

Key Points for Migration and Operations

In migration, pinning versions and ensuring reproducibility are key points. Items to check are as follows.

• Correspondence between Compute Capability and instructions used
• Combination of driver, Toolkit, and communication library
• Container infrastructure and GPU allocation settings
• Power limits and cooling, and ECC enable/disable
• Error accumulation and checkpoint recovery during long runs Manual adjustments remain after automatic conversion with HIPIFY. Performance varies with shared memory usage and synchronization granularity. Differences in random numbers and floating-point rounding are also subject to verification. In large-scale distributed setups, collective communication differences between NCCL and RCCL become apparent. Proficiency with tools equivalent to Nsight is required for failure analysis. In operations, unifying monitoring metrics is a challenge. A system for regular monitoring of temperature, power, utilization, and memory pressure is needed.

Cost and Selection Guide

Costs must be viewed as the total of hardware costs, usage fees, and migration costs. NVIDIA machines have an advantage in availability and information. AMD machines sometimes have lower hardware unit prices or cloud usage fees. However, comparison should be based on the total including migration and validation effort. Selection guidelines are as follows.

• Short-term generative AI development: Adopting CUDA is stable
• Long-term in-house infrastructure: Use ROCm together to diversify suppliers
• Extending the life of existing assets: Try ZLUDA in limited validation
• Education and prototyping: Decide based on available GPU and OS support In our editorial independent evaluation, availability and reproducibility are more important than cost differences. Infrastructure with documented recovery procedures lowers total costs. If dependence on a single vendor is a concern, coexistence with open specifications provides a buffer.

Editorial Opinion

We evaluate that selection should prioritize asset continuity over peak speed. This is because when existing models and operating procedures assume CUDA, migration costs outweigh performance differences. For new projects, we see prioritizing ease of hiring and troubleshooting as a reasonable decision. We see the pitfall in the field in successful small-scale validation. This is because procedures that worked on a few GPUs often fail in large-scale distributed or long-running execution. We evaluate that management that thoroughly pins the versions of drivers, toolkits, and communication libraries is essential. We expect the struggle between compatibility layers and open specifications to continue. This is because even as AMD and Intel advance their open approaches, accumulated training assets will not collapse in a short time. It seems likely that coexistence of CUDA-centered and alternative approaches will continue over the next 1 to 3 years.

References

• CUDA Toolkit Documentation - NVIDIA
• ROCm Documentation - AMD
• ZLUDA - GitHub
• CUDA Zone - NVIDIA