Dynamic Teams in OpenMP

Schönherr, Jan Hendrik; Richling, Jan; Heiß, Hans-Ulrich

doi:10.1109/sbac-pad.2010.36

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…Schönherr et al [23] use dynamic teams that change the number of active threads during parallel regions to reduce over-subscription with co-executing OpenMP programs. They show that equipartitioning hardware threads between co-executing, parallel programs improves performance compared to over-subscription.…”

Section: Multiprogram Workload Thread Allocatorsmentioning

confidence: 99%

Scalo

Georgakoudis

Vandierendonck

Thoman

et al. 2017

ACM Trans. Archit. Code Optim.

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Shared memory machines continue to increase in scale by adding more parallelism through additional cores and complex memory hierarchies. Often, executing multiple applications concurrently, dividing among them hardware threads, provides greater efficiency rather than executing a single application with large thread counts. However, contention for shared resources can limit the improvement of concurrent application execution: orchestrating the number of threads used by each application and is essential.In this article, we contribute SCALO, a solution to orchestrate concurrent application execution to increase throughput. SCALO monitors co-executing applications at runtime to evaluate their scalability. Its optimizing thread allocator analyzes these scalability estimates to adapt the parallelism of each program. Unlike previous approaches, SCALO differs by including dynamic contention effects on scalability and by controlling the parallelism during the execution of parallel regions. Thus, it improves throughput when other state-of-theart approaches fail and outperforms them by up to 40% when they succeed.

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Section: Multiprogram Workload Thread Allocatorsmentioning

confidence: 99%

Scalo

Georgakoudis

Vandierendonck

Thoman

et al. 2017

ACM Trans. Archit. Code Optim.

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“…Some solutions gather information on efficiency by making use of pre-execution profiling [2,3], while others do not require profiling and do not account for program efficiency [4][5][6][7]. However, it is nontrivial to measure and account for program efficiency without profiling.…”

Section: Contributionsmentioning

confidence: 99%

“…In the interest of preserving existing standards and interfaces, Schonherr et al [5] modified GCC's implementation of OpenMP in order to prevent oversubscription.…”

Section: Shared Memory Systemsmentioning

confidence: 99%

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Efficient multiprogramming for multicores with SCAF

Creech

Kotha

Barua

2013

Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture

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As hardware becomes increasingly parallel and the availability of scalable parallel software improves, the problem of managing multiple multithreaded applications (processes) becomes important. Malleable processes, which can vary the number of threads used as they run [1], enable sophisticated and flexible resource management.Although many existing applications parallelized for SMPs with parallel runtimes are in fact already malleable, deployed run-time environments provide no interface nor any strategy for intelligently allocating hardware threads or even preventing oversubscription. Prior research methods either depend upon profiling applications ahead of time in order to make good decisions about allocations, or do not account for process efficiency at all, leading to poor performance. None of these prior methods have been adapted widely in practice. This paper presents the Scheduling and Allocation with Feedback (SCAF) system: a drop-in runtime solution which supports existing malleable applications in making intelligent allocation decisions based on observed efficiency without any changes to semantics, program modification, offline profiling, or even recompilation. Our existing implementation can control most unmodified OpenMP applications. Other malleable threading libraries can also easily be supported with small modifications, without requiring application modification or recompilation.In this work, we present the SCAF daemon and a SCAF-aware port of the GNU OpenMP runtime. We present a new technique for estimating process efficiency purely at runtime using available hardware counters, and demonstrate its effectiveness in aiding allocation decisions.We evaluated SCAF using NAS NPB parallel benchmarks on five commodity parallel platforms, enumerating architectural features and their effects on our scheme.We measured the benefit of SCAF in terms of sum of speedups improvement (a common metric for multiprogrammed environments) when running all benchmark pairs concurrently compared to equipartitioning -the best existing competing scheme in the literature. If the sum of speedups with SCAF is within 5% of equipartitioning (i.e., improvement factor is 0.95X < improvement factor in sum of speedups < 1.05X), then we deem SCAF to break even. Less than 0.95X is considered a slowdown; greater than 1.05X is an improvement. We found that SCAF improves on equipartitioning on 4 out of 5 machines, breaking even or improving in 80-89% of pairs and showing a mean improvement of 1.11-1.22X for benchmark pairs for which it shows an improvement, depending on the machine.Since we are not aware of any widely available tool for equipartitioning, we also compare SCAF against multiprogramming using unmodified OpenMP, which is the only environment available to end-users today. SCAF improves or breaks even on the unmodified OpenMP runtimes for all 5 machines in 72-100% of pairs, with a mean improvement of 1.27-1.7X, depending on the machine.

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“…Our coscheduling design [15] enriches the process placement interface and adds a needed feature non-intrusively. In an initial work on software interfaces, we allow to change the parallelism degree of applications on the fly [16].…”

Section: Visionmentioning

confidence: 99%

Analyzing resource interdependencies in multi-core architectures to improve scheduling decisions

Busse

Schönherr

Diener

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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Since the advent of multi-core processors, different multi-core system and in particular processor architectures have emerged exhibiting individual advantages and disadvantages. One of the main distinguishing factors among these architectures is their varying degree and type of resource sharing among individual cores. On the one hand, resource sharing is necessary for the cores to communicate, while on the other hand resource sharing is often used for economic reasons. Depending on the degree and type of resource sharing, the impact on performance depends on the workload applied and can vary to a large extend.In this paper, we investigate the impact of different kinds of resource interdependencies found in current processors on the performance of scheduling strategies using a set of benchmarks. Our results show that the architecture has a major impact on the performance of a process placement strategy. However, they also point out that simple strategies taking only a few basic architectural characteristics into account fall short. Thus, new holistic scheduling strategies are needed that take more characteristics into account.

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Dynamic Teams in OpenMP

Cited by 7 publications

References 4 publications

Scalo

Scalo

Efficient multiprogramming for multicores with SCAF

Analyzing resource interdependencies in multi-core architectures to improve scheduling decisions

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