Data and thread affinity in openmp programs

Terboven, Christian; Mey, Dieter an; Schmidl, Dirk; Jin, Henry; Reichstein, Thomas

doi:10.1145/1366219.1366222

Cited by 77 publications

(66 citation statements)

References 5 publications

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“…The experiment platform has 24 cores which detail configuration can be found in Sect. 4. In the test, the performance speedup per thread continues decreasing as the number of threads grows, as showed in Fig.…”

Section: Introductionmentioning

confidence: 76%

“…Many researches [1,2,3,4,5,6] have been carried out to improve the performance of OpenMP applications on multi-core system. Truong et al [1] seek to improve scalability from implementation aspect.…”

Section: Related Workmentioning

confidence: 99%

“…Noronha et al [2] study the benefits from using large page support for OpenMP applications. Terboven et al [4] improve data and thread affinity of OpenMP programs on multi-core system by binding thread to thread cores and allocate memory with next touch strategy. A series of papers [3,5,6] from Broquedis et al introduce a runtime system that transpose affinities of thread teams into scheduling hints.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

How OpenMP Applications Get More Benefit from Many-Core Era

Yan

Han

et al. 2010

Beyond Loop Level Parallelism in OpenMP: Accelerators, Tasking and More

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Abstract.With the approaching of the many-core era, it becomes more and more difficult for a single OpenMP application to efficiently utilize all the available processor cores. On the other hand, the available cores become more than necessary for some applications. We believe executing multiple OpenMP applications concurrently will be a common usage model in the future. In this model, how threads are scheduled on the cores are important as cores are asymmetric. We have designed and implemented a prototype scheduler, SWOMPS, to help schedule the threads of all the concurrent applications system-widely. The scheduler makes its decision based on underlying hardware configuration as well as the hints of scheduling preference of each application provided by users. Experiment evaluation shows SWOMPS is quite efficient in improving the performance.With the help of SWOMPS, we compared exclusive running one application and concurrent running multiple applications in term of system throughput and individual application performance. In various experimental comparisons, concurrent execution outperforms in throughput, meanwhile the performance slowdown of individual applications in concurrent execution is reasonable.

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Section: Introductionmentioning

confidence: 76%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

How OpenMP Applications Get More Benefit from Many-Core Era

Yan

Han

et al. 2010

Beyond Loop Level Parallelism in OpenMP: Accelerators, Tasking and More

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show abstract

“…In order to add parallel capacity for systems based on multi-core CPU, there are different platforms and libraries available-for example, Intel (Folsom, CA, USA) Thread Building Blocks (TBB) [9,10], Pthreads [10], Message Passing Interface (MPI) [11], OpenMP [11,12], Open Computing Language (OpenCL) [13] and Open Accelerators (OpenACC) [14]. All of them can be used for programming multi-core CPUs, but MPI is more appropriate for distributed shared memory (DSM) systems, such as computational clusters [11].…”

Section: Parallel Processingmentioning

confidence: 99%

Endpoint Security in Networks: An OpenMP Approach for Increasing Malware Detection Speed

et al. 2017

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Increasingly sophisticated antivirus (AV) software and the growing amount and complexity of malware demand more processing power from personal computers, specifically from the central processor unit (CPU). This paper conducted performance tests with Clam AntiVirus (ClamAV) and improved its performance through parallel processing on multiple cores using the Open Multi-Processing (OpenMP) library. All the tests used the same dataset constituted of 1.33 GB of data distributed among 2766 files of different sizes. The new parallel version of ClamAV implemented in our work achieved an execution time around 62% lower than the original software version, reaching a speedup of 2.6 times faster. The main contribution of this work is to propose and implement a new version of the ClamAV antivirus using parallel processing with OpenMP, easily portable to a variety of hardware platforms and operating systems.

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“…This type of thread and data mapping is called sharing-aware mapping. For optimal performance improvements, data and thread mapping should be performed together [23]. For the thread mapping, knowledge about how data is shared between the threads is necessary.…”

Section: Introductionmentioning

confidence: 99%

A Sharing-Aware Memory Management Unit for Online Mapping in Multi-core Architectures

Cruz

Diener

Pilla

et al. 2016

Euro-Par 2016: Parallel Processing

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Abstract. In modern shared-memory architectures, it is important to map threads and data in a way that increases the locality of their memory accesses, thereby improving performance and energy efficiency. Threads that access shared data should be mapped close to each other in the memory hierarchy, while the data they access should be mapped to their NUMA node, which is called sharing-aware mapping. In this paper, we propose SAMMU, which adds sharing-awareness to the memory management unit in current architectures. SAMMU analyzes the memory access behavior in hardware and provides information to the operating system so it can perform an online mapping of threads and data. In the evaluation with a wide range of parallel applications, performance was improved by up to 35.7% (13.1% on average).

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Data and thread affinity in openmp programs

Cited by 77 publications

References 5 publications

How OpenMP Applications Get More Benefit from Many-Core Era

How OpenMP Applications Get More Benefit from Many-Core Era

Endpoint Security in Networks: An OpenMP Approach for Increasing Malware Detection Speed

A Sharing-Aware Memory Management Unit for Online Mapping in Multi-core Architectures

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