Performance Of OpenMP Research Articles

Optimizing Computational Performance with OpenMP Parallel Programming Techniques

The article presents a study of parallel computing, specifically comparing the performance of OpenMP in C++ and Python. Furthermore, the technologies of OpenMP and TPL (C++, C#) are contrasted. Performance indicators were established that showcase the advantages and disadvantages of each methodology. In addition to the numerical data, the research provides insights into the peculiarities of each parallel computing model, which can assist developers in choosing the right tool.

Performance analysis of CUDA, OpenACC and OpenMP programming models on TESLA V100 GPU

Graphics processors are widely utilized in modern supercomputers as accelerators. Ability to perform efficient parallelization and low-level allow scientists to greatly boost performance of their codes. Modern Nvidia GPUs feature low-level approaches, such as CUDA, along with high-level approaches: OpenACC and OpenMP. While the low-level approach aims to explore all possible abilities of SIMT GPU architecture by writing low-level C/C++ code, it takes significant effort from programmer. OpenACC and OpenMP programming models are opposite to CUDA. Using these models the programmer only have to identify the blocks of code to be parallelized using pragmas. We compare the performance of CUDA, OpenMP and OpenACC on state-of-the-art Nvidia Tesla V100 GPU in various typical scenarios that arise in scientific programming, such as matrix multiplication, regular memory access patterns and evaluate performance of physical simulation codes implemented using these programming models. Moreover, we study the performance matrix multiplication implemented in vendor-optimized BLAS libraries for Nvidia Tesla V100 GPU and modern Intel Xeon processor.

Производительность OpenMP и реализация MPI на системе ultrasparc

Производительность OpenMP и реализация MPI на системе ultrasparc

Fast 2-D ultrasound strain imaging: the benefits of using a GPU

Deformation of tissue can be accurately estimated from radio-frequency ultrasound data using a 2-dimensional normalized cross correlation (NCC)-based algorithm. This procedure, however, is very computationally time-consuming. A major time reduction can be achieved by parallelizing the numerous computations of NCC. In this paper, two approaches for parallelization have been investigated: the OpenMP interface on a multi-CPU system and Compute Unified Device Architecture (CUDA) on a graphics processing unit (GPU). The performance of the OpenMP and GPU approaches were compared with a conventional Matlab implementation of NCC. The OpenMP approach with 8 threads achieved a maximum speed-up factor of 132 on the computing of NCC, whereas the GPU approach on an Nvidia Tesla K20 achieved a maximum speed-up factor of 376. Neither parallelization approach resulted in a significant loss in image quality of the elastograms. Parallelization of the NCC computations using the GPU, therefore, significantly reduces the computation time and increases the frame rate for motion estimation.

Performance modeling of hybrid MPI/OpenMP scientific applications on large-scale multicore supercomputers

In this paper, we present a performance modeling framework based on memory bandwidth contention time and a parameterized communication model to predict the performance of OpenMP, MPI and hybrid applications with weak scaling on three large-scale multicore supercomputers: IBM POWER4, POWER5+ and BlueGene/P, and analyze the performance of these MPI, OpenMP and hybrid applications. We use STREAM memory benchmarks and Intelʼs MPI benchmarks to provide initial performance analysis and model validation of MPI and OpenMP applications on these multicore supercomputers because the measured sustained memory bandwidth can provide insight into the memory bandwidth that a system should sustain on scientific applications with the same amount of workload per core. In addition to using these benchmarks, we also use a weak-scaling hybrid MPI/OpenMP large-scale scientific application: Gyrokinetic Toroidal Code (GTC) in magnetic fusion to validate our performance model of the hybrid application on these multicore supercomputers. The validation results for our performance modeling method show less than 7.77% error rate in predicting the performance of hybrid MPI/OpenMP GTC on up to 512 cores on these multicore supercomputers.

Overcoming performance bottlenecks in using OpenMP on SMP clusters

This paper presents a new parallel programming environment called ParADE to enable easy, portable, and high-performance computing for SMP clusters. Different from the prior studies, ParADE separates the programming model from the execution model: it enables shared-address-space programming while it realizes hybrid execution of message-passing and shared-address-space. To overcome the poor performance of conventional OpenMP on SDSM (Software Distributed Shared Memory), ParADE implements an intelligent OpenMP translator supporting efficient mutual exclusion and efficient page transmission. The experimental results on a Linux cluster demonstrate that ParADE reduces mutual exclusion overhead and overall execution time.

Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.

Scalability and performance of OpenMP and MPI on a 128‐processor SGI Origin 2000

AbstractThe purpose of this paper is to investigate the scalability and performance of seven, simple OpenMP test programs and to compare their performance with equivalent MPI programs on an SGI Origin 2000. Data distribution directives were used to make sure that the OpenMP implementation had the same data distribution as the MPI implementation. For the matrix‐times‐vector (test 5) and the matrix‐times‐matrix (test 7) tests, the syntax allowed in OpenMP 1.1 does not allow OpenMP compilers to be able to generate efficient code since the reduction clause is not currently allowed for arrays. (This problem is corrected in OpenMP 2.0.) For the remaining five tests, the OpenMP version performed and scaled significantly better than the corresponding MPI implementation, except for the right shift test (test 2) for a small message. Copyright © 2001 John Wiley & Sons, Ltd.

Evaluting Performance of OpenMP and MPI on the SGI Origin 2000 with Benchmarks of Realistic Problem Sizes

Six applications benchmarks, including four NAS codes, provided by H. Jin and J. Wu, previously parallelized using OpenMP, and message-passing-interface (MPI), were run on a 128-processor SGI Origin 2000. Detailed profile data was collected to understand the factors causing imperfect scalability. Our results show that load imbalance and cost of remote accesses are the main factors in limited speed-up of the OpenMP versions, whereas communication costs are the single major factor in the performance of the MPI versions.

Ab initio quantum chemistry on a ccNUMA architecture using openMP. III

In this study we report the implementation of Gaussian 98 using OpenMP. OpenMP is a standard for parallel programming on shared memory computers. We compare the performance of OpenMP with methods such as Hartree–Fock (HF) and density functional theory (DFT), including first and second derivatives of the energy. In addition we also look at CI-singles (CIS). Performance was investigated with up to 32 processors and compared against the standard version of Gaussian 98.