Intel's Array Building Blocks: A retargetable, dynamic compiler and embedded language

Newburn, Chris J.; So, Byoungro; Liu, Zhenying; McCool, Michael; Ghuloum, Anwar; Toit, Stefanus Du; Wang, Zhi Gang; Du, Zhao Hui; Chen, Yongjian; Wu, Gangxiong; Guo, Pengcheng; Liu, Zhanglin; Zhang, Dan

doi:10.1109/cgo.2011.5764690

Cited by 44 publications

(34 citation statements)

References 18 publications

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“…The recently-developed Julia programming language [11] attempts to address such an efficiency weakness of MATLAB through aggressive just-intime compilation and optimization but is still in prerelease status. Among special-purpose programming tools, Intel's Array Building Blocks (ArBB) [12] appear the closest to our approach. It repeats elemental computation specified in C++ on elements taken from different data structures.…”

Section: B Sixth-order Implicit Compact Difference Schemementioning

confidence: 99%

Automated Rapid Prototyping of Regular Grid-Based Numerical Applications Using Generalized Elemental Subroutines

Situ

Wang

2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Computational scientists and engineers commonly rely on established software libraries to achieve high performance and reliability in their numerical applications. Unfortunately, this approach does not work well if the desired functionality is absent in existing libraries or if the integration is difficult. In such scenarios, one is often forced to explore alternative algorithms and in-house implementations. Such exploration can be a challenging task for computational scientists and engineers without sufficient computer science background. To address this issue, we design and build an automated rapid prototyping tool for regular grid-based numerical applications. This new tool allows programmers to specify algorithms as composition of familiar computation patterns such as those easily found in open literature expressed as generalized elemental subroutines. The tool then automatically transforms such subroutines into code which adapts to the prescribed data structures and delivers performance expected from the underlying algorithms. We demonstrate the tool in use cases including a productiongrade computational fluid dynamic application.

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Section: B Sixth-order Implicit Compact Difference Schemementioning

confidence: 99%

Automated Rapid Prototyping of Regular Grid-Based Numerical Applications Using Generalized Elemental Subroutines

Situ

Wang

2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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“…It is designed to produce scalable and portable programs that can harvest data and thread parallelism on both multi-core and heterogeneous many-core architectures [142]. Intel ArBB is a combination of Intel Ct, a former research project Intel started in 2007, and a multi-core development platform initially developed at the University of Waterloo [19] and then continued by RapidMind Inc. [18].…”

Section: Intel Array Building Blocksmentioning

confidence: 99%

Tuning the victim selection policy of Intel TBB

Iordan

Jahre

Natvig

2015

Journal of Systems Architecture

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In the early 2000s, the superscalar CPU paradigm reached the point of diminishing returns mainly due to power requirements and overheating concerns. Faced with a constant demand for performance, hardware developers were in need of new ways to efficiently use the ever increasing transistor count predicted by Moore's law. The Chip MultiProcessors (CMPs) came as a natural solution to the power wall: several less complex and significantly less "power hungry" cores integrated on a single chip. In almost all ICT segments today, from High Performance Computing (HPC) to embedded devices, CMPs have become the architecture of choice. With this wide adoption of CMPs, software developers need to use parallel programming to fully exploit this architecture. Although parallelization can maximize the performance and energy efficiency of applications running on CMPs, it also comes with its own set of challenges. Among these, inherent management overheads that can account for sub-linear speedups and can increase the energy consumption of executions. Because of rising concerns for energy cost and battery life, much research and development today focuses on reducing power requirements and saving energy.In this thesis, we investigate how parallel programming can be used to improve the energy efficiency of applications running on CMP systems. We focus on a programming paradigm called Task Based Programming (TBP). The base concept of the TBP model is that the programmer focuses on identifying and annotating pieces of code (tasks) which can be executed concurrently with other tasks. An important result of our work is an increased understanding of how computations, parallelization and energy consumption relate when executing on CMP systems.Working in this direction, we use a simulation framework to allow for increased flexibility in design space exploration and noninvasive measurements. Unfortunately, the performance overhead of simulation is significant: simulating a parallel application can be 10000x slower than executing it on real hardware. In the first part of our research, we took it upon ourselves to try to solve this issue. We investigate the challenges of employing a sampling based technique to take advantage of the periodic behavior in TBP parallel applications. Our proposal is a simple 3-phase methodology that identifies only a small number of representative execution samples to simulate thus reducing the overall simulation time.In the second part of our work, we look at parallelization as a mean to save energy on CMP platforms. We test and compare two TBP libraries, Wool and Intel TBB, focusing on the behavior of some basic TBP parallelization operations like task spawning, task synchronization and task stealing. We investigate the energy footprint of these parallelization overheads and the effect it has on the energy-efficiency of the executing system. We have identified that failed task steals amount for the largest overhead. To reduce their impact and improve our system's energy efficiency, we devised a new occupa...

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“…As in LLVA, Intel's Array Building Blocks [4] also introduces general vector idioms to support manual vectorization. While these works focus on target-agnostic manual vectorization, our work focuses on a target-agnostic auto-vectorization engine.…”

Section: B Split Vectorization Using Gcc4climentioning

confidence: 99%