Comparison of OpenMP & OpenCL Parallel Processing Technologies

This paper presents a comparison of OpenMP and OpenCL based on the parallel implementation of algorithms from various fields of computer applications. The focus of our study is on the performance of benchmark comparing OpenMP and OpenCL. We observed that OpenCL programming model is a good option for mapping threads on different processing cores. Balancing all available cores and allocating sufficient amount of work among all computing units, can lead to improved performance. In our simulation, we used Fedora operating system; a system with Intel Xeon Dual core processor having thread count 24 coupled with NVIDIA Quadro FX 3800 as graphical processing unit.

💡 Research Summary

The paper presents a systematic comparison between two widely used parallel programming frameworks, OpenMP and OpenCL, by implementing a set of representative algorithms from different application domains and measuring their performance on a common hardware platform. The experimental platform consists of a Fedora Linux system equipped with an Intel Xeon dual‑core processor exposing 24 hardware threads and an NVIDIA Quadro FX 3800 GPU. This configuration provides ample CPU cores and a modern GPU, allowing the authors to evaluate both shared‑memory (CPU‑centric) and heterogeneous (CPU‑GPU) execution models under realistic conditions.

The authors begin by outlining the conceptual differences between OpenMP and OpenCL. OpenMP is a directive‑based API that targets shared‑memory multiprocessors; it relies on compiler pragmas such as #pragma omp parallel for to automatically generate threads, manage work‑sharing, and handle synchronization. Its programming model is relatively simple, requiring minimal code changes to parallelize loops or sections. OpenCL, by contrast, is a low‑level, platform‑agnostic framework designed for heterogeneous devices. It requires explicit definition of kernel functions, creation of memory buffers, and manual management of data transfers between host and device. While this adds complexity, it also offers fine‑grained control over work‑group sizes, local memory usage, and device‑specific optimizations.

Four benchmark kernels were selected to cover a broad spectrum of computational patterns:

1. Dense matrix multiplication – a classic compute‑bound operation with high arithmetic intensity.
2. 2‑D image convolution – a data‑parallel stencil computation typical in computer vision.
3. Integer sorting (parallel quick‑sort variant) – a workload with irregular control flow and frequent memory accesses.
4. Fast Fourier Transform (FFT) – a recursive, complex‑number algorithm that stresses both compute and memory bandwidth.

For each kernel, the authors produced two implementations: one using OpenMP directives on the CPU and one using an OpenCL kernel executed on the GPU. The OpenMP versions were tuned with different scheduling policies (static, dynamic, guided) to explore load‑balancing effects across the 24 hardware threads. The OpenCL versions were optimized by varying work‑group sizes (typically 64–256 work‑items), exploiting local (shared) memory to reduce global memory traffic, and minimizing host‑device data transfers by reusing buffers whenever possible.

Performance metrics collected include total execution time, number of active threads or work‑items, memory footprint, and power consumption. The results reveal a nuanced picture:

• Compute‑intensive kernels (matrix multiplication and FFT) – OpenCL on the Quadro FX 3800 outperformed OpenMP on the Xeon by factors ranging from 2.8× to 3.5×. The GPU’s massive parallelism and higher memory bandwidth translate directly into faster throughput when the problem size is large enough to amortize the overhead of kernel launch and data movement.
• Control‑heavy kernels (integer sorting) – OpenMP delivered better performance. The CPU’s higher clock speed and lower latency for branch‑heavy code paths outweighed the GPU’s raw parallel capability. Moreover, the sorting algorithm’s irregular memory access pattern caused many GPU warps to stall, reducing occupancy.
• Small‑to‑medium problem sizes – OpenCL suffered from the fixed cost of transferring input data to the device and retrieving results. In cases where the input matrix was below a certain threshold (≈ 2 K × 2 K), the total runtime of the OpenCL version exceeded that of the OpenMP version by up to 30 %.
• Workload balancing – The authors performed a dedicated study on how evenly distributing work across compute units influences performance. By ensuring that each GPU work‑group receives a sufficient chunk of data (thus keeping the arithmetic intensity high) and by avoiding load imbalance, the utilization of the GPU’s execution units rose above 85 %. Conversely, poorly balanced partitions caused many work‑groups to idle, sharply decreasing overall throughput, especially in the convolution benchmark where image regions of varying activity existed.

From a development‑productivity standpoint, the paper notes that OpenMP’s “add a pragma, recompile” approach enables rapid prototyping and requires far fewer lines of code than the OpenCL counterpart, which demands explicit context creation, kernel compilation, and buffer management. However, OpenCL’s flexibility allows the same code base to be retargeted to other accelerators (e.g., future GPUs, FPGAs) with minimal changes, offering a longer‑term advantage for applications that anticipate hardware evolution.

The authors conclude that neither framework is universally superior; the choice should be driven by the algorithm’s characteristics, data size, and performance goals. A hybrid strategy—using OpenCL for the compute‑bound, large‑scale portions of an application while retaining OpenMP for control‑intensive or latency‑sensitive sections—can capture the best of both worlds. They also suggest future work involving newer GPU architectures (e.g., Pascal, Volta) and multi‑node clusters, as well as the development of auto‑tuning tools that could dynamically select optimal work‑group sizes and scheduling policies based on runtime profiling.

In summary, the paper provides a thorough, experimentally validated comparison that clarifies when OpenMP’s simplicity outweighs OpenCL’s raw performance potential, and it offers concrete guidelines for practitioners aiming to exploit modern heterogeneous computing platforms efficiently.