Earticle

Multi-core processors have become the dominant market trend because they provide a great opportunity in increasing processing performance by exploiting various parallelisms. In JPEG (Joint Photographic Experts Group) compression, color space conversion is one of the major kernels known as a computationally expensive module. This paper presents a fast solution for color space conversion with multi-core parallel computation. For this purpose, we utilize Threading Building Blocks (TBB), a runtime library based on C++, and OpenMP (Open Multi-processing), a shared programming language. A RGB image is transformed into a luminance-chrominance color space such as YCbCr. The implementation results show that parallel implementations achieve greater performance improvement regarding processing speed compared with the serial implementation.

Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.