A Performance Comparison of CUDA and OpenCL (1005.2581v3)

Abstract: 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.

• The paper compares CUDA and OpenCL by analyzing kernel execution and data transfer times on NVIDIA GPUs using a quantum Monte Carlo simulation.
• It employs the AQUA application with Suzuki-Trotter decomposition to simulate quantum spin systems across 8 to 128 qubits.
• Numerical results show that OpenCL lags 13% to 63% in kernel execution and 16% to 67% overall, underscoring CUDA's efficiency.

Performance Analysis of CUDA vs OpenCL on NVIDIA GPUs

The paper undertaken by Karimi, Dickson, and Hamze provides a comprehensive analysis of the performance characteristics of CUDA and OpenCL, two prevalent interfaces for GPU programming. The paper endeavors to assess these frameworks in the context of a computationally-intensive scientific application, specifically focusing on data transfer times, kernel execution times, and the overall application execution times on NVIDIA GPUs.

CUDA, developed by NVIDIA, is an API limited to NVIDIA hardware, offering potentially greater performance due to its tailored design. Conversely, OpenCL is a portable open standard enabling code to run on various parallel processing devices, including CPUs, GPUs, and DSPs, across different vendors. This generality in OpenCL, however, may introduce cross-platform performance variability as well as a performance overhead when compared to hardware-specific interfaces like CUDA.

Methodology Overview

The application utilized for this comparative paper, called Adiabatic Quantum Algorithms (AQUA), serves as a Monte Carlo simulation of a quantum spin system. By approximating the quantum spin configuration through a Suzuki-Trotter decomposition technique, the researchers simulate systems ranging from 8 to 128 qubits. These applications involve substantial numerical operations, which are well-suited for the parallel processing capabilities of modern GPUs.

The experimental setup involved porting a CUDA kernel into OpenCL, predominantly using NVIDIA's development tools. When performing cross-platform kernel testing, significant code modification was required to adapt for ATI GPUs due to OpenCL's stateless nature within that ecosystem.

Results and Discussion

Through a series of performance experiments conducted on an NVIDIA GeForce GTX-260, the findings highlight that CUDA consistently outperforms OpenCL in both kernel execution and data transfer tasks. The CUDA implementations not only yielded faster kernel execution but also improved data transfer performance. The results notably indicate that OpenCL's runtime compiled kernel execution might contribute to its slower performance when compared to CUDA's pre-compiled approach.

Numerical results from the experiments demonstrate that the OpenCL kernel's execution time ranged between 13% and 63% slower than their CUDA counterparts across different problem sizes. Similarly, the end-to-end application performance exhibited a 16% to 67% slowdown for OpenCL compared to CUDA. These results underscore CUDA's advantage in optimizing performance for applications with high computational demand and extensive data transfer.

Implications and Future Directions

The implications of this research are significant for developers and researchers focusing on high-performance computing applications. While OpenCL provides the advantage of platform versatility, CUDA's optimization for NVIDIA hardware offers substantial performance gains in applications requiring the utmost efficiency.

In a theoretical context, these findings may prompt further exploration of the trade-offs between portability and performance in heterogeneous computing environments. Practically, the choice between CUDA and OpenCL for development should not only be guided by performance metrics but also consider the development environment, target hardware, and long-term maintenance and support scenarios.

Looking forward, the evolution of parallel computing will likely continue to inform this balance between portability and efficiency. Advances in compiler optimizations and GPU architectural developments may enhance OpenCL's performance characteristics. Additionally, the introduction of standardized machine learning and AI workloads driving GPU utilization may also influence the choice of computing frameworks.

Karimi, Dickson, and Hamze's substantive examination contributes to the wealth of literature evaluating the performance nuances across GPU programming frameworks, offering valuable insights for high-performance application developers and researchers.