Adaptation of MPDATA Heterogeneous Stencil Computation to Intel Xeon Phi Coprocessor

The multidimensional positive definite advection transport algorithm (MPDATA) belongs to the group of nonoscillatory forward-in-time algorithms and performs a sequence of stencil computations. MPDATA is one of the major parts of the dynamic core of the EULAG geophysical model. In this work, we outline an approach to adaptation of the 3D MPDATA algorithm to the Intel MIC architecture. In order to utilize available computing resources, we propose the (3 + 1)D decomposition of MPDATA heterogeneous stencil computations. This approach is based on combination of the loop tiling and fusion techniques. It allows us to ease memory/communication bounds and better exploit the theoretical floating point efficiency of target computing platforms. An important method of improving the efficiency of the (3 + 1)D decomposition is partitioning of available cores/threads into work teams. It permits for reducing inter-cache communication overheads. This method also increases opportunities for the efficient distribution of MPDATA computation onto available resources of the Intel MIC architecture, as well as Intel CPUs. We discuss preliminary performance results obtained on two hybrid platforms, containing two CPUs and Intel Xeon Phi. The top-of-the-line Intel Xeon Phi 7120P gives the best performance results, and executes MPDATA almost 2 times faster than two Intel Xeon E5-2697v2 CPUs.

Download Full-text

Effective SIMD Vectorization for Intel Xeon Phi Coprocessors

Scientific Programming ◽

10.1155/2015/269764 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 8

Author(s):

Xinmin Tian ◽

Hideki Saito ◽

Serguei V. Preis ◽

Eric N. Garcia ◽

Sergey S. Kozhukhov ◽

...

Keyword(s):

High Performance ◽

Xeon Phi ◽

Performance Gain ◽

Intel Xeon Phi ◽

Performance Study ◽

Seamless Integration ◽

Small Matrix ◽

Performance Results ◽

Intel Mic ◽

Intel Xeon

Efficiently exploiting SIMD vector units is one of the most important aspects in achieving high performance of the application code running on Intel Xeon Phi coprocessors. In this paper, we present several effective SIMD vectorization techniques such as less-than-full-vector loop vectorization, Intel MIC specific alignment optimization, and small matrix transpose/multiplication 2D vectorization implemented in the Intel C/C++ and Fortran production compilers for Intel Xeon Phi coprocessors. A set of workloads from several application domains is employed to conduct the performance study of our SIMD vectorization techniques. The performance results show that we achieved up to 12.5x performance gain on the Intel Xeon Phi coprocessor. We also demonstrate a 2000x performance speedup from the seamless integration of SIMD vectorization and parallelization.

Download Full-text

The VOLNA-OP2 tsunami code (version 1.5)

Geoscientific Model Development ◽

10.5194/gmd-11-4621-2018 ◽

2018 ◽

Vol 11 (11) ◽

pp. 4621-4635 ◽

Cited By ~ 7

Author(s):

Istvan Z. Reguly ◽

Daniel Giles ◽

Devaraj Gopinathan ◽

Laure Quivy ◽

Joakim H. Beck ◽

...

Keyword(s):

Graphics Processing Units ◽

High Performance ◽

Shallow Water Equation ◽

Xeon Phi ◽

Intel Xeon Phi ◽

Central Processing ◽

Domain Specific ◽

Computing Platforms ◽

Graphics Processing ◽

Intel Xeon

Abstract. In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite-volume non-linear shallow-water equation (NSWE) solver built on the OP2 domain-specific language (DSL) for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high-performance computing platforms: central processing units (CPUs), the Intel Xeon Phi, and graphics processing units (GPUs). This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years; here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity as well as performance and portability to its users across a number of platforms.

Download Full-text

The use of MPI and OpenMP technologies for subsequence similarity search in very long time series on a computer cluster system with nodes based on the Intel Xeon Phi Knights Landing many-core processor

Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie) ◽

10.26089/nummet.v20r104 ◽

2019 ◽

pp. 29-44

Author(s):

Я.А. Краева ◽

М.Л. Цымблер

Keyword(s):

Time Series ◽

Similarity Search ◽

Xeon Phi ◽

Intel Xeon Phi ◽

Time Warping ◽

Knights Landing ◽

Dynamic Time ◽

Many Core ◽

Intel Mic ◽

Intel Xeon

В настоящее время поиск похожих подпоследовательностей требуется в широком спектре приложений интеллектуального анализа временных рядов: моделирование климата, финансовые прогнозы, медицинские исследования и др. В большинстве указанных приложений при поиске используется мера схожести Dynamic Time Warping (DTW), поскольку на сегодняшний день научное сообщество признает меру DTW одной из лучших для большинства предметных областей. Мера DTW имеет квадратичную вычислительную сложность относительно длины искомой подпоследовательности, в силу чего разработан ряд параллельных алгоритмов ее вычисления на устройствах FPGA и многоядерных ускорителях с архитектурами GPU и Intel MIC. В настоящей статье предлагается новый параллельный алгоритм для поиска похожих подпоследовательностей в сверхбольших временных рядах на кластерных системах с узлами на базе многоядерных процессоров Intel Xeon Phi поколения Knights Landing (KNL). Вычисления распараллеливаются на двух уровнях: на уровне всех узлов кластера - с помощью технологии MPI и в рамках одного узла кластера - с помощью технологии OpenMP. Алгоритм предполагает использование дополнительных структур данных и избыточных вычислений, позволяющих эффективно задействовать возможности векторизации вычислений на процессорных системах Phi KNL. Эксперименты, проведенные на синтетических и реальных наборах данных, показали хорошую масштабируемость алгоритма. Nowadays, the subsequence similarity search is required in a wide range of time series mining applications: climate modeling, financial forecasts, medical research, etc. In most of these applications, the Dynamic Time Warping (DTW) similarity measure is used, since DTW is empirically confirmed as one of the best similarity measures for the majority of subject domains. Since the DTW measure has a quadratic computational complexity with respect to the length of query subsequence, a number of parallel algorithms for various many-core architectures are developed, namely FPGA, GPU, and Intel MIC. In this paper we propose a new parallel algorithm for subsequence similarity search in very large time series on computer cluster systems with nodes based on Intel Xeon Phi Knights Landing (KNL) many-core processors. Computations are parallelized on two levels as follows: by MPI at the level of all cluster nodes and by OpenMP within a single cluster node. The algorithm involves additional data structures and redundant computations, which make it possible to efficiently use the capabilities of vector computations on Phi KNL. Experimental evaluation of the algorithm on real-world and synthetic datasets shows that the proposed algorithm is highly scalable.

Download Full-text

The VOLNA-OP2 Tsunami Code (Version 1.0)

10.5194/gmd-2018-18 ◽

2018 ◽

Author(s):

Istvan Z. Reguly ◽

Devaraj Gopinathan ◽

Joakim H. Beck ◽

Michael B. Giles ◽

Serge Guillas ◽

...

Keyword(s):

High Performance ◽

Domain Specific Language ◽

Xeon Phi ◽

Intel Xeon Phi ◽

Domain Specific ◽

Computing Platforms ◽

Tsunami Model ◽

Performance Computing ◽

Code Version ◽

Intel Xeon

Abstract. In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite volume non-linear shallow water equations (NSWE) solver built on the OP2 domain specific language for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high performance computing platforms: CPUs, the Intel Xeon Phi, and GPUs. This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years, here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity to its users, as well as performance and portability on a number of platforms.

Download Full-text

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

EPJ Web of Conferences ◽

10.1051/epjconf/201817502009 ◽

2018 ◽

Vol 175 ◽

pp. 02009

Author(s):

Carleton DeTar ◽

Steven Gottlieb ◽

Ruizi Li ◽

Doug Toussaint

Keyword(s):

Conjugate Gradient ◽

Memory Hierarchy ◽

Xeon Phi ◽

Intel Xeon Phi ◽

Code Performance ◽

Recent Developments ◽

Knights Landing ◽

Many Core ◽

Intel Xeon

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

Download Full-text