Performance Characteristics of OpenMP Language Constructs on a Many-core-on-a-chip Architecture

Zhu, Weirong; Cuvillo, Juan del; Gao, Guang R.

doi:10.1007/978-3-540-68555-5_19

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…Our critical thread identification is not suitable for this scenario. However, dynamic scheduling has large runtime overheads and static scheduling is recommended as the first scheduling option, especially when the number of threads is increased [Zhu et al 2006]. The decision whether to use static or dynamic scheduling in a parallel is out of the scope of this article.…”

Section: Identification Of Critical Threadsmentioning

confidence: 99%

Thread-management techniques to maximize efficiency in multicore and simultaneous multithreaded microprocessors

Rakvic

Cai²,

González³

et al. 2010

ACM Trans. Archit. Code Optim.

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We provide an analysis of thread-management techniques that increase performance or reduce energy in multicore and Simultaneous Multithreaded (SMT) cores. Thread delaying reduces energy consumption by running the core containing the critical thread at maximum frequency while scaling down the frequency and voltage of the cores containing noncritical threads. In this article, we provide an insightful breakdown of thread delaying on a simulated multi-core microprocessor. Thread balancing improves overall performance by giving higher priority to the critical thread in the issue queue of an SMT core. We provide a detailed breakdown of performance results for thread-balancing, identifying performance benefits and limitations. For those benchmarks where a performance benefit is not possible, we introduce a novel thread-balancing mechanism on an SMT core that can reduce energy consumption. We have performed a detailed study on an Intel microprocessor simulator running parallel applications. Thread delaying can reduce energy consumption by 4% to 44% with negligible performance loss. Thread balancing can increase performance by 20% or can reduce energy consumption by 23%.

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Section: Identification Of Critical Threadsmentioning

confidence: 99%

Thread-management techniques to maximize efficiency in multicore and simultaneous multithreaded microprocessors

Rakvic

Cai²,

González³

et al. 2010

ACM Trans. Archit. Code Optim.

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“…For all other uses, contact the owner/author(s). reasonably well-understood [7], but little is know about its energy efficiency.…”

Section: The Research Problem and Motivationmentioning

confidence: 99%

“…In order to understand the complexities of this approach, we specifically look at multi-threaded applications. The performance of the existing constructs for concurrent execution is reasonably well-understood [4,7]. Furthermore, since parallel programming enables programmers to run their applications faster, it makes sense to assume that an application that finish earlier will also consume less energy [3].…”

mentioning

confidence: 99%

Do language constructs for concurrent execution have impact on energy efficiency?

Pinto

2013

Proceedings of the 2013 Companion Publication for Conference on Systems, Programming, &Amp; Applications: Software for Humanity

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This study analyzed the performance and energy consumption of multicore applications, using three techniques to manage concurrent execution in a set of benchmarks. We conclude that these constructs can heavily impact on energy consumption. Nonetheless, the trade-off between performance and energy consumption in multicore applications is not so obvious. The research problem and motivationMeasuring the energy consumption of an application and understanding where the energy usage lies provides new opportunities for energy savings. In order to understand the complexities of this approach, we specifically look at multi-threaded applications. The performance of the existing constructs for concurrent execution is reasonably well-understood [4,7]. Furthermore, since parallel programming enables programmers to run their applications faster, it makes sense to assume that an application that finish earlier will also consume less energy [3]. This idea is known as the "Race to idle" principle [1]. In summary: faster programs will theoretically consume less energy because they will have the machine idle fast, and modern processors consume very little energy when idle. Nevertheless, parallelism and the overheads inherent to concurrent and parallel programming constructs might impact energy consumption in ways that are hard to predict.This paper presents an empirical study consisting of the evaluation of the performance and energy consumption of four applications that use three programming concurrent constructs plus a sequential implementation with the goal of demonstrating that it is hard to establish a trade-offs between energy-efficiency and performance. This study is relevant because it is known that concurrent construct is often used in high-level applications [5]. Moreover, the performance of the existing constructs for concurrent execution is Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).reasonably well-understood [7], but little is know about its energy efficiency. Related workTo the best of our knowledge, only two studies have dealt with the topic of understanding the impact of concurrent constructs on the energy efficiency of applications [2, 6]. Gautham et al.[2] analyzed synchronization primitives and explore the following synchronization techniques to find an ideal solution for synchronizationintensive workloads: i) spin lock ii) mutexes iii) software transactional memory. They show that Software Transactional Memory (STM) systems can perform better than locks for workloads where a significant portion of the execution time is spent in the critical sections. Trefethen [6] studies the behavior of the NAS Benchmark suite for its energy and runtime performance. The benchmark ...

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“…In order to understand the complexities of this approach, we specifically look at multi-threaded applications, since multicore processors have become ubiquitous. The performance of the existing constructs for concurrenty 1 execution is reasonably well-understood [10,14], but little is known about its energy efficiency. Furthermore, since concurrent/parallel programming enables programmers to run their applications faster, a common belief is that this application will also consume less energy [4].…”

Section: Motivationmentioning

confidence: 99%

Refactoring multicore applications towards energy efficiency

Pinto

2013

Proceedings of the 2013 Companion Publication for Conference on Systems, Programming, &Amp; Applications: Software for Humanity

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Great strides have been made to increase the energy efficiency of hardware, data center facilities, and network infrastructure. However, in any computer system, it is software that directs much of the activity of the hardware. Moreover, multicore processors have become ubiquitous, mainly because their multiple benefits, especially enhanced performance for multi-threaded and computeintensive applications. Nonetheless, there are few studies addressing the topic of restructuring multicore applications to consume less energy and even fewer that leverage developer expertise to achieve that goal. In this thesis we present a brief background study for refactoring multicore applications in order to improve both performance and energy consumption. The idea consists in proposing a catalog of refactorings targeting some languages of the JVM platform.

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Performance Characteristics of OpenMP Language Constructs on a Many-core-on-a-chip Architecture

Cited by 12 publications

References 12 publications

Thread-management techniques to maximize efficiency in multicore and simultaneous multithreaded microprocessors

Thread-management techniques to maximize efficiency in multicore and simultaneous multithreaded microprocessors

Do language constructs for concurrent execution have impact on energy efficiency?

Refactoring multicore applications towards energy efficiency

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