Comparing programming models for medical imaging on multi‐core systems

Abstract

SUMMARYMulti-core processors offer a huge potential of parallelism but pose a challenge of program development for achieving high performance in real applications. We compare three popular parallel programming models-POSIX threads (Pthreads), OpenMP, and Threading Building Blocks (TBB)-regarding their use for multi-core systems. We analyze how these models can be employed for implementing various parallelizations of a real-world application from the area of medical imaging, and we conduct extensive runtime experiments to measure performance. Our main contribution is a comprehensive comparison of Pthreads, OpenMP, and TBB with respect to the following criteria: program development effort, programming style, level of abstraction, and runtime performance on multi-cores.

Performance Gaps between OpenMP and OpenCL for Multi-core CPUs

“…There are a lot of comparative studies between OpenMP and other CPU specific programming models [11], [12], and also some relevant work on the comparison between CUDA and OpenCL [13], [14]. However, detailed studies on OpenMP and OpenCL are rarely seen.…”

An application-centric evaluation of OpenCL on multi-core CPUs

“…There are multiple studies on OpenMP and other CPU specific programming models [35,36], and also some relevant work on studying OpenCL on GPUs [6,37]. However, very little research is available on evaluating OpenCL performance on multi-core CPUs.…”

A generic parallel pattern interface for stream and data processing

“…Basically, these patterns instantiate parallelism while hide away the complexity of concurrency mechanisms, eg, thread management, synchronizations, or data sharing. Examples of applications coming from multiple domains (eg, financial, medical, and mathematical) and improving their performance through parallel programming design patterns, can be widely found in the literature . Nevertheless, although all these skeletons aim to simplify the development of parallel applications, there is not a unified standard .…”

“…Examples of applications coming from multiple domains (eg, financial, medical, and mathematical) and improving their performance through parallel programming design patterns, can be widely found in the literature. [5][6][7] Nevertheless, although all these skeletons aim to simplify the development of parallel applications, there is not a unified standard. 8 Therefore, users require understanding different frameworks, not only to decide which fits best for their purposes, but also to properly use them.…”