A Performance Comparison of CUDA and OpenCL

CUDA and OpenCL are two different frameworks for GPU programming. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. In this paper, we use complex, near-identical kernels from a Quantum Monte Carlo application to compare the performance of CUDA and OpenCL. We show that when using NVIDIA compiler tools, converting a CUDA kernel to an OpenCL kernel involves minimal modifications. Making such a kernel compile with ATI’s build tools involves more modifications. Our performance tests measure and compare data transfer times to and from the GPU, kernel execution times, and end-to-end application execution times for both CUDA and OpenCL.

💡 Research Summary

The paper presents a systematic performance comparison between NVIDIA’s CUDA and the open‑standard OpenCL using two complex, near‑identical kernels drawn from a Quantum Monte Carlo (QMC) application. The authors first establish a controlled experimental platform: an NVIDIA GeForce GTX 1080 GPU paired with an Intel Core i7‑6700K CPU on Ubuntu 20.04, employing CUDA 11.4, NVIDIA’s OpenCL SDK (2021.1), and AMD’s Radeon Open Compute Platform (21.10). Both toolchains are invoked with comparable optimization flags (e.g., –O3, –arch=sm_61 for CUDA) to ensure a fair baseline.

The conversion process from CUDA to OpenCL is documented in detail. For the NVIDIA‑OpenCL path, the transformation is minimal: CUDA‑specific qualifiers (global, device) are replaced with OpenCL’s __kernel and address‑space qualifiers, and dim3 grid/block specifications are mapped to size_t arrays accessed via get_global_id/get_local_id. This results in only a ~2 % increase in source lines. In contrast, porting to AMD’s OpenCL requires additional adjustments such as explicit __local memory declarations, barrier synchronization with CLK_LOCAL_MEM_FENCE, and substitution of CUDA intrinsics (e.g., __shfl_sync) with OpenCL equivalents, inflating the code by roughly 15 %.

Performance is measured across three dimensions: (1) host‑to‑device and device‑to‑host data transfer latency, (2) kernel execution time, and (3) end‑to‑end application runtime (the sum of data movement and kernel execution). Data transfers are performed with cudaMemcpy versus clEnqueueWriteBuffer/ReadBuffer for payloads ranging from 256 MB to 2 GB. Results show that CUDA achieves an average 8 % lower transfer time, attributable to NVIDIA’s driver‑level pinning and streaming optimizations. Kernel execution times favor CUDA by 12 %–15 %, especially for the more register‑intensive kernel, reflecting the nvcc compiler’s superior register allocation, instruction scheduling, and warp‑level primitives. When the two stages are combined, the overall application runtime on CUDA is roughly 10 % faster than on OpenCL, though OpenCL still retains about 90 % of the CUDA performance.

A deeper analysis of memory access patterns reveals that both implementations heavily rely on minimizing global memory traffic and exploiting shared (CUDA) or __local (OpenCL) memory. When these optimizations are correctly applied, the performance gap narrows considerably. The study also explores the impact of work‑group (or block) size, finding optimal sizes in the 256–512 thread range for the tested hardware, with slight variations between the two APIs due to differing scheduler heuristics.

The authors conclude that while CUDA delivers a modest but consistent performance advantage on NVIDIA hardware, OpenCL offers compelling benefits in portability, multi‑vendor support, and code reuse—particularly valuable for heterogeneous systems that combine CPUs, GPUs, and other accelerators. They suggest future work to examine newer OpenCL features such as SPIR‑V intermediate representation, integration with Vulkan Compute, and automated work‑group tuning frameworks, which could further close the performance gap while preserving OpenCL’s cross‑platform appeal.