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The hardware accelerator debate: A financial risk case study using many-core computing

COMPUTERS & ELECTRICAL ENGINEERING 2015年 46卷 157-175页

作者： Varghese, Blesson Univ St Andrews Sch Comp Sci St Andrews KY16 9AJ Fife Scotland

The risk of reinsurance portfolios covering globally occurring natural catastrophes, such as earthquakes and hurricanes, is quantified by employing simulations. These simulations are computationally intensive and require large amounts of data to be processed. The use of many-core hardware accelerators, such as the Intel Xeon Phi and the NVIDIA Graphics Processing Unit (GPU), are desirable for achieving high-performance risk analytics. In this paper, we set out to investigate how accelerators can be employed in risk analytics, focusing on developing parallel algorithms for Aggregate Risk Analysis, a simulation which computes the Probable Maximum Loss of a portfolio taking both primary and secondary uncertainties into account. The key result is that both hardware accelerators are useful in different contexts;without taking data transfer times into account the Phi had lowest execution times when used independently and the GPU along with a host in a hybrid platform yielded best performance. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Hardware accelerators many-core computing Secondary uncertainty Financial risk Catastrophic risk Risk analysis

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Multi-level Load Balancing Strategies for Massively Parallel Smoothed Particle Hydrodynamics Simulation 24

Multi-level Load Balancing Strategies for Massively Parallel...

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53rd International Conference on Parallel Processing (ICPP)

作者： Zhang, Yi Chen, Junshi Zhang, Ziyu An, Hong Chen, Longkui Zhao, Yang Wang, Zhanming Univ Sci & Technol China Hefei Peoples R China Laoshan Lab Qingdao Peoples R China

ISBN: (纸本)9798400717932

In the field of computational fluid dynamics, Smoothed Particle Hydrodynamics (SPH) serves as a powerful tool for investigating complex fluid interactions and instabilities. For the practical SPH simulation of large-scale fluid phenomena such as tsunamis, volcanic eruptions, and planetary collisions, it typically requires billions of particles, as the numerical resolution increases proportionally with the number of particles. To efficiently conduct large-scale SPH simulations on modern supercomputers with massive many-core processors, we propose a novel SPH implementation leveraging multi-level parallelism and a corresponding three-level load balancing strategy. Our load balancing approach comprises: (1) a process-level domain decomposition algorithm based on an improved 1D partitioning exact algorithm;(2) an adaptive recursive cell subdivision method;(3) a fine-grained dynamic thread-level task scheduling strategy. Our experiment uses 1 billion particles to simulate converging Richtmyer-Meshkov instability and verifies the effect of load balancing on new Sunway supercomputer. As the shockwave converges on the central interface area, our load balancing strategy breaks the bottleneck constraints on the slowest node, increases the balance of computational loads between nodes from 30.01% to 91.48%, and achieves a 2.8x improvement in computational performance. Finally, our implementation enables each CPU to handle 10 million particles and scale from 1 CPU to 100,000 CPUs (in total 39 million cores with 1 trillion particles) with a performance of 80.4% parallel efficiency.

关键词： Load balance Smoothed particle hydrodynamics many-core computing

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High-performance simulations of turbulent boundary layer flow using Intel Xeon Phi many-core processors

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JOURNAL OF SUPERcomputing 2021年第9期77卷 9597-9614页

作者： Kang, Ji-Hoon Hwang, Jinyul Sung, Hyung Jin Ryu, Hoon Korea Inst Sci & Technol Informat Div Natl Supercomp Daejeon 34141 South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also complement and extend experimental results. DNS of high Reynolds numbers, however, require huge computing cost so high-performance computing has been strongly pursued. In this study, we examine the feasibility of cost-efficient DNS on Intel Xeon Phi many-core processors that are currently adopted by 10% of the 100 largest supercomputers in the world as listed in the Top500 site. For this purpose, we port and optimize our in-house turbulent flow solver named as DNS-TBL (direct numerical simulation-turbulent boundary layer) on Xeon Phi Knights Landing (KNL) many-core processors and conduct benchmark tests on KNL and conventional multicore processors. The key architectural features of KNL processors and strategies to exploit them for performance enhancement are discussed. The optimized code is validated by conducting numerical simulations of zero-pressure gradient turbulent boundary layers at high Reynolds numbers and by comparing simulated turbulent statistics to those reported in previous studies. With the details of optimization strategies and validation processes, this work can serve as a practical guideline for acceleration of large-scale and precise DNS with many-core computing.

关键词： Computational fluid dynamics Direct numerical simulations of turbulent flows many-core computing Xeon Phi knights landing processors

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Solving a trillion unknowns per second with HPGMG on Sunway TaihuLight

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第2期23卷 493-507页

作者： Ma, Wenjing Ao, Yulong Yang, Chao Williams, Samuel Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Comp Sci Beijing 100190 Peoples R China Peking Univ CAPT Sch Math Sci Beijing 100871 Peoples R China Peking Univ CCSE Sch Math Sci Beijing 100871 Peoples R China Peking Univ Ctr Data Sci Beijing 100871 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

Benchmarks for supercomputers are important tools, not only for evaluating and ranking modern supercomputers, but also for providing hints for future architecture design. As a new benchmark, HPGMG (high performance geometric multigrid) solves a linear equation set with a full geometric multi-grid algorithm. It involves computation on different scales, data movement with various volumes, global communication and neighbor communication with both large and small messages, etc., and is more correlated to real world applications than traditional benchmarks such as LINPACK. Therefore, it is desirable to examine how well HPGMG can perform on leadership supercomputers such as Sunway Taihulight. Sunway Taihulight, the No. 1 supercomputer in the Top 500 list from June 2016 to June 2018, which uses a specially designed many-core architecture SW26010, is of great interest to the community of high performance computing. With careful analysis and code design, we came up with an efficient implementation of HPGMG on SW26010 processors. We not only employed traditional optimization techniques such as 2.5D partitioning, double buffering, and collective data load, but also introduced a micro-benchmark to help with the choice of optimization direction and parameter tuning. Another contribution is that we proposed a new procedure for the major operations, by granulating and reordering the smooth function and the ghost exchange operation, leading to reduced memory copy and accelerated communication process. Our optimized implementation of HPGMG on Sunway TaihuLight achieved a ground-breaking performance of1.036x1012 Degrees of Freedom per second at the finest level, which is No. 1 on the HPGMG list of Nov 2017.

关键词： HPGMG Sunway TaihuLight Performance benchmark and optimization many-core computing

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computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第3期28卷 836-847页

作者： Burke, Neil Rau-Chaplin, Andrew Varghese, Blesson Dalhousie Univ Fac Comp Sci Halifax NS Canada Univ St Andrews Sch Comp Sci North Haugh St Andrews Fife Scotland

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable. In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： risk analysis secondary uncertainty many-core computing hardware accelerators

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Extreme-Scale High-Order WENO Simulations of 3-D Detonation Wave with 10 Million cores

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2018年第2期15卷 26-26页

作者： Cai, Ying Ao, Yulong Yang, Chao Ma, Wenjing Zhao, Haitao Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Peking Univ Sch Math Sci CAPT Beijing 100871 Peoples R China Peking Univ Sch Math Sci CCSE Beijing 100871 Peoples R China Peking Univ NELVT Beijing 100871 Peoples R China Chinese Acad Sci State Key Lab Comp Sci Beijing 100190 Peoples R China

High-order stencil computations, frequently found in many applications, pose severe challenges to emerging many-core platforms due to the complexities of hardware architectures as well as the sophisticated computing and data movement patterns. In this article, we tackle the challenges of high-order WENO computations in extreme-scale simulations of 3D gaseous waves on Sunway TaihuLight. We design efficient parallelizalion algorithms and present effective optimization techniques to fully exploit various parallelisms with reduced memory footprints, enhanced data reuse, and balanced computation load. Test results show the optimized code can scale to 9.98 million cores, solving 12.74 trillion unknowns with 23.12 Pflops double-precision performance.

关键词： WENO method Detonation waves Sunway TaihuLight many-core computing computation reuse

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Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 72卷 51-60页

作者： Huang, Fang Tao, Jian Xiang, Yang Liu, Peng Dong, Lei Wang, Lizhe Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Inst Remote Sensing Big Data Big Data Res Ctr Chengdu 611731 Peoples R China Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Deakin Univ Sch Informat Technol Geelong Vic 3216 Australia China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 10094 Peoples R China

Compressive sensing (CS) is a new signal processing method, which was developed recent years. CS can sample signals with a frequency far below the Nyquist frequency. CS can also compress the signals while sampling, which can reduce the usage of resources for signal transmission and storage. However, the reconstruction algorithm used in the corresponding decoder is highly complex and computationally expensive. Thus, in some specific applications, e.g., remote sensing image processing for disaster monitoring, the CS algorithm usually cannot satisfy the time requirements on traditional computing platforms. Various studies have shown that many-core computing piatforms such as OpenCL are among the most promising platforms that are available for real-time processing because of their powerful floating-point computing capabilities. In this study, we present the design and implementation of parallel compressive sampling matching pursuit (CoSaMP), which is an OpenCL-based parallel CS reconstruction algorithm, as well as some optimization strategies, such as access efficiency, numerical merge, and instruction optimization. Based on experiments using remote sensing images with different sizes, we demonstrated that the proposed parallel algorithm can achieve speedups of about 41 times and 58 times on AMD HD7350 and NVIDIA K20Xm platforms, respectively, without modifying the application code. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Compressive sensing CoSaMP Heterogeneous computing many-core computing OpenCL

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26 PFLOPS Stencil Computations for Atmospheric Modeling on Sunway TaihuLight 31

26 PFLOPS Stencil Computations for Atmospheric Modeling on S...

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31st IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Ao, Yulong Yang, Chao Wang, Xinliang Xue, Wei Fu, Haohuan Liu, Fangfang Gan, Lin Xu, Ping Ma, Wenjing Chinese Acad Sci Inst Software Beijing Peoples R China Chinese Acad Sci State Key Lab Comp Sci Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ MOE Key Lab Earth Syst Modeling Beijing Peoples R China Tsinghua Univ Ctr Earth Syst Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Natl Supercomp Ctr Wuxi Wuxi Peoples R China

ISBN: (纸本)9781538639146

Stencil computation arises from a broad set of scientific and engineering applications and often plays a critical role in the performance of extreme-scale simulations. Due to the memory bound nature, it is a challenging task to optimize stencil computation kernels on modern supercomputers with relatively high computing throughput whilst relatively low data-moving capability. This work serves as a demonstration on the details of the algorithms, implementations and optimizations of a real-world stencil computation in 3D nonhydrostatic atmospheric modeling on the newly announced Sunway TaihuLight supercomputer. At the algorithm level, we present a computation-communication overlapping technique to reduce the inter-process communication overhead, a locality-aware blocking method to fully exploit on-chip parallelism with enhanced data locality, and a collaborative data accessing scheme for sharing data among different threads. In addition, a variety of effective hardware specific implementation and optimization strategies on both the process-and thread-level, from the fine-grained data management to the data layout transformation, are developed to further improve the performance. Our experiments demonstrate that a single-process many-core speedup of as high as 170x can be achieved by using the proposed algorithm and optimization strategies. The code scales well to millions of cores in terms of strong scalability. And for the weak-scaling tests, the code can scale in a nearly ideal way to the full system scale of more than 10 million cores, sustaining 25.96 PFLOPS in double precision, which is 20% of the peak performance.

关键词： stencil computation atmospheric modeling Sunway TaihuLight many-core computing heterogeneous computing

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Time-efficient simulations of tight-binding electronic structures with Intel Xeon Phi™ many-core processors

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COMPUTER PHYSICS COMMUNICATIONS 2016年 209卷 79-87页

作者： Ryu, Hoon Jeong, Yosang Kang, Ji-Hoon Cho, Kyu Nam Korea Inst Sci & Technol Informat Natl Inst Supercomp & Networking Daejeon 34141 South Korea Korea Univ Sch Elect Engn Seoul 02841 South Korea Samsung Elect Software Ctr Seoul 06765 South Korea

Modelling of multi-million atomic semiconductor structures is important as it not only predicts properties of physically realizable novel materials, but can accelerate advanced device designs. This work elaborates a new Technology-Computer-Aided-Design (TCAD) tool for nanoelectronics modelling, which uses a sp(3)d(5)s(*) tight-binding approach to describe multi-million atomic structures, and simulate electronic structures with high performance computing (HPC), including atomic effects such as alloy and dopant disorders. Being named as Quantum simulation tool for Advanced Nanoscale Devices (Q-AND), the tool shows nice scalability on traditional multi-core HPC clusters implying the strong capability of large-scale electronic structure simulations, particularly with remarkable performance enhancement on latest clusters of Intel Xeon Phi (TM) coprocessors. A review of the recent modelling study conducted to understand an experimental work of highly phosphorus-doped silicon nanowires, is presented to demonstrate the utility of Q-AND. Having been developed via Intel Parallel computing Center project, Q-AND will be open to public to establish a sound framework of nanoelectronics modelling with advanced HPC clusters of a many-core base. With details of the development methodology and exemplary study of dopant electronics, this work will present a practical guideline for TCAD development to researchers in the field of computational nanoelectronics. (C) 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： Tight-binding simulation Electronic structure calculation many-core computing Xeon Phi (TM) coprocessor

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An investigation of the performance portability of OpenCL

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2013年第11期73卷 1439-1450页

作者： Pennycook, S. J. Hammond, S. D. Wright, S. A. Herdman, J. A. Miller, I. Jarvis, S. A. Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Sandia Natl Labs Ctr Res Comp Albuquerque NM 87185 USA UK Atom Weapons Estab Supercomp Solut Ctr Aldermaston RG7 4PR Berks England

This paper reports on the development of an MPI/OpenCL implementation of LU, an application-level benchmark from the NAS Parallel Benchmark Suite. An account of the design decisions addressed during the development of this code is presented, demonstrating the importance of memory arrangement and work-item/work-group distribution strategies when applications are deployed on different device types. The resulting platform-agnostic, single source application is benchmarked on a number of different architectures, and is shown to be 1.3-1.5x slower than native FORTRAN 77 or CUDA implementations on a single node and 1.3-3.1x slower on multiple nodes. We also explore the potential performance gains of OpenCL's device fissioning capability, demonstrating up to a 3 x speed-up over our original OpenCL implementation. (C) 2012 Elsevier Inc. All rights reserved.

关键词： many-core computing GPU computing Optimisation OpenCL High performance computing

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