High-level Support for Hybrid Parallel Execution of C++ Applications Targeting Intel® Xeon Phi™ Coprocessors

Dokulil, Jiri; Bajrovic, Enes; Benkner, Siegfried; Pllana, Sabri; Sandrieser, Martin; Bachmayer, Beverly

doi:10.1016/j.procs.2013.05.430

Cited by 16 publications

(16 citation statements)

References 1 publication

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“…It may be useful to compare the performance that we achieved using all available cores of the host Intel Xeon processors with the performance achieved when all available cores of the Intel Xeon Phi coprocessor are used [22]. Furthermore, software technologies, such as [13], enable the use of all cores of homogeneous processors of the host and all available cores of the coprocessor.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Memeti

Pllana

2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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Abstract-Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speedups of up to 17.6×. Our approach is up to 3× faster than a patternbased parallel approach that uses the RE2 library.

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Section: Discussionmentioning

confidence: 99%

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Memeti

Pllana

2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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“…These powerful models for scheduling on heterogeneous systems are queue-based that basically split the workload into smaller tasks and queuing these tasks across the available resources. A similar approach based on priority queues is proposed by Dokulili et al [16].…”

Section: Related Workmentioning

confidence: 99%

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

Memeti

Pllana

2016

2016 45th International Conference on Parallel Processing Workshops (ICPPW)

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We describe an approach that uses combinatorial optimization and machine learning to share the work between the host and device of heterogeneous computing systems such that the overall application execution time is minimized. We propose to use combinatorial optimization to search for the optimal system configuration in the given parameter space (such as, the number of threads, thread affinity, work distribution for the host and device). For each system configuration that is suggested by combinatorial optimization, we use machine learning for evaluation of the system performance. We evaluate our approach experimentally using a heterogeneous platform that comprises two 12-core Intel Xeon E5 CPUs and an Intel Xeon Phi 7120P co-processor with 61 cores. Using our approach we are able to find a near-optimal system configuration by performing only about 5% of all possible experiments.

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“…The HyPHI library leverages the similarity of the Xeon Phi architecture (compared to the standard x86-64 architecture) to allow hybrid execution with a single source code utilizing all cores of the host processor(s) and coprocessor(s) simultaneously. Our previous experiments have shown that such hybrid execution allows us to better exploit systems equipped with coprocessors and get closer to their full potential [4], [6]. The outer for-loop is a time step loop, which is a common pattern for many simulation codes.…”

Section: Hyphi Librarymentioning

confidence: 99%

“…As an example of a more complex kernel, we have implemented a smoothed particle hydrodynamics (SPH) code using HyPHI [6]. The implementation makes two calls to hybrid foreach patterns.…”

Section: B Smoothed Particle Hydrodynamicsmentioning

confidence: 99%

“…One example is the Intel Xeon Phi coprocessor, which is a PCI express card that comes with its own GDDR5 memory (6,8,or 16GB, depending on the model) and large number of cores (57, 60, or 61). Each core is equipped with a powerful vector unit, can support four hardware threads (providing around 240 threads per card), and provides fully cache-coherent memory access.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Tuning of a Parallel Pattern Library for Heterogeneous Systems with Intel Xeon Phi

Dokulil

Benkner

2014

2014 IEEE International Symposium on Parallel and Distributed Processing With Applications

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Pattern libraries are important tools for high productivity application development. Their struggle for best performance is complicated by the fact that they are used to execute user-provided code, which is not known during their creation. This makes pattern libraries good candidate for automatic software tuning. In this paper, we deal with automatic online parameter tuning of the HyPHI hybrid pattern library for heterogeneous systems equipped with the Intel Xeon Phi coprocessors. We propose a framework that can be used to combine a pattern library with an existing tuning library in a practical and efficient way. Our experiments show that tuning can noticeably improve the performance of the library and it introduces very little overhead.

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High-level Support for Hybrid Parallel Execution of C++ Applications Targeting Intel® Xeon Phi™ Coprocessors

Cited by 16 publications

References 1 publication

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Combinatorial Optimization of Work Distribution on Heterogeneous Systems

Automatic Tuning of a Parallel Pattern Library for Heterogeneous Systems with Intel Xeon Phi

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