TBBench: A Micro-Benchmark Suite for Intel Threading Building Blocks

Abstract

Abstract-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.

Implications of shallower memory controller transaction queues in scalable memory systems

“…From the results achieved in this paper, the evaluation of other scientific benchmarks such as database and bag-of-tasks [31] benchmarks to multicore architectures will also be considered.…”

Language-based vectorization and parallelization using intrinsics, OpenMP, TBB and Cilk Plus

“…is a C++ template library supporting task parallelism on Intel multicore platforms [5,12]. It provides a rich collection of components for parallel programming, and a scheduler which manages and schedules threads to execute parallel tasks.…”

“…OpenMP [3,18] is the most popular, but one can consider using Threading Building Blocks (TBB for short) [6,12] or Cilk Plus [5,13]. More sophisticated language-based optimization can be done by using intrinsics, which allow to utilize Intel Advanced Vector Extensions (i.e., SIMD extensions) explicitly [6].…”

Simulación en tiempo real de vehículos industriales con modelos multicuerpo de gran complejidad

“…Las herramientas de paralelización más adecuadas dependen fuertemente del problema en cuestión, como así también de las características del hardware disponible. Esto se puede ver por ejemplo en los trabajos de Abdel-Qader and Walker [5] y Marowka [126][127][128]. Hay que señalar que, por tamaño y el tipo de dependencia de los datos, la paralelización de la simulación dinámica de sistemas multicuerpo puede ser considerada como un problema atípico, muy diferente probablemente de los que han sido tenidos en mente por los creadores de las herramientas informáticas.…”