Using NERSC for Research in High Energy Physics Theory
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An Operational Perspective on a Hybrid and Heterogeneous Cray XC50 System
An Operational Perspective on a Hybrid and Heterogeneous Cray XC50 System Sadaf Alam, Nicola Bianchi, Nicholas Cardo, Matteo Chesi, Miguel Gila, Stefano Gorini, Mark Klein, Colin McMurtrie, Marco Passerini, Carmelo Ponti, Fabio Verzelloni CSCS – Swiss National Supercomputing Centre Lugano, Switzerland Email: {sadaf.alam, nicola.bianchi, nicholas.cardo, matteo.chesi, miguel.gila, stefano.gorini, mark.klein, colin.mcmurtrie, marco.passerini, carmelo.ponti, fabio.verzelloni}@cscs.ch Abstract—The Swiss National Supercomputing Centre added depth provides the necessary space for full-sized PCI- (CSCS) upgraded its flagship system called Piz Daint in Q4 e daughter cards to be used in the compute nodes. The use 2016 in order to support a wider range of services. The of a standard PCI-e interface was done to provide additional upgraded system is a heterogeneous Cray XC50 and XC40 system with Nvidia GPU accelerated (Pascal) devices as well as choice and allow the systems to evolve over time[1]. multi-core nodes with diverse memory configurations. Despite Figure 1 clearly shows the visible increase in length of the state-of-the-art hardware and the design complexity, the 37cm between an XC40 compute module (front) and an system was built in a matter of weeks and was returned to XC50 compute module (rear). fully operational service for CSCS user communities in less than two months, while at the same time providing significant improvements in energy efficiency. This paper focuses on the innovative features of the Piz Daint system that not only resulted in an adaptive, scalable and stable platform but also offers a very high level of operational robustness for a complex ecosystem. -
A Scheduling Policy to Improve 10% of Communication Time in Parallel FFT
A scheduling policy to improve 10% of communication time in parallel FFT Samar A. Aseeri Anando Gopal Chatterjee Mahendra K. Verma David E. Keyes ECRC, KAUST CC, IIT Kanpur Dept. of Physics, IIT Kanpur ECRC, KAUST Thuwal, Saudi Arabia Kanpur, India Kanpur, India Thuwal, Saudi Arabia [email protected] [email protected] [email protected] [email protected] Abstract—The fast Fourier transform (FFT) has applications A. FFT Algorithm in almost every frequency related studies, e.g. in image and signal Forward Fourier transform is given by the following equa- processing, and radio astronomy. It is also used to solve partial differential equations used in fluid flows, density functional tion. 3 theory, many-body theory, and others. Three-dimensional N X (−ikxx) (−iky y) (−ikz z) 3 f^(k) = f(x; y; z)e e e (1) FFT has large time complexity O(N log2 N). Hence, parallel algorithms are made to compute such FFTs. Popular libraries kx;ky ;kz 3 perform slab division or pencil decomposition of N data. None The beauty of this sum is that k ; k ; and k can be summed of the existing libraries have achieved perfect inverse scaling x y z of time with (T −1 ≈ n) cores because FFT requires all-to-all independent of each other. These sums are done at different communication and clusters hitherto do not have physical all- to-all connections. With advances in switches and topologies, we now have Dragonfly topology, which physically connects various units in an all-to-all fashion. -
インテル® Parallel Studio XE 2020 リリースノート
インテル® Parallel Studio XE 2020 2019 年 12 月 5 日 内容 1 概要 ..................................................................................................................................................... 2 2 製品の内容 ........................................................................................................................................ 3 2.1 インテルが提供するデバッグ・ソリューションの追加情報 ..................................................... 5 2.2 Microsoft* Visual Studio* Shell の提供終了 ........................................................................ 5 2.3 インテル® Software Manager .................................................................................................... 5 2.4 サポートするバージョンとサポートしないバージョン ............................................................ 5 3 新機能 ................................................................................................................................................ 6 3.1 インテル® Xeon Phi™ 製品ファミリーのアップデート ......................................................... 12 4 動作環境 .......................................................................................................................................... 13 4.1 プロセッサーの要件 ...................................................................................................................... 13 4.2 ディスク空き容量の要件 .............................................................................................................. 13 4.3 オペレーティング・システムの要件 ............................................................................................. 13 -
Introduction to High Performance Computing
Introduction to High Performance Computing Shaohao Chen Research Computing Services (RCS) Boston University Outline • What is HPC? Why computer cluster? • Basic structure of a computer cluster • Computer performance and the top 500 list • HPC for scientific research and parallel computing • National-wide HPC resources: XSEDE • BU SCC and RCS tutorials What is HPC? • High Performance Computing (HPC) refers to the practice of aggregating computing power in order to solve large problems in science, engineering, or business. • Purpose of HPC: accelerates computer programs, and thus accelerates work process. • Computer cluster: A set of connected computers that work together. They can be viewed as a single system. • Similar terminologies: supercomputing, parallel computing. • Parallel computing: many computations are carried out simultaneously, typically computed on a computer cluster. • Related terminologies: grid computing, cloud computing. Computing power of a single CPU chip • Moore‘s law is the observation that the computing power of CPU doubles approximately every two years. • Nowadays the multi-core technique is the key to keep up with Moore's law. Why computer cluster? • Drawbacks of increasing CPU clock frequency: --- Electric power consumption is proportional to the cubic of CPU clock frequency (ν3). --- Generates more heat. • A drawback of increasing the number of cores within one CPU chip: --- Difficult for heat dissipation. • Computer cluster: connect many computers with high- speed networks. • Currently computer cluster is the best solution to scale up computer power. • Consequently software/programs need to be designed in the manner of parallel computing. Basic structure of a computer cluster • Cluster – a collection of many computers/nodes. • Rack – a closet to hold a bunch of nodes. -
View Annual Report
Fellow Shareholders, Headlined by strong growth in revenue and profitability, we had one of our best years ever in 2015, executing across each of our major focus areas and positioning our company for continued growth into the future. We achieved another year of record revenue, growing by nearly 30 percent compared to 2014. In fact, our revenue in 2015 was more than three times higher than just four years prior — driven by growth in both our addressable market and market share. Over the last five years, we have transformed from a company solely focused on the high-end of the supercomputing market — where we are now the clear market leader — to a company with multiple product lines serving multiple markets. We provide our customers with powerful computing, storage and analytics solutions that give them the tools to advance their businesses in ways never before possible. During 2015, we installed supercomputing and storage solutions at a number of customers around the world. In the U.S., we completed the first phase of the massive new “Trinity” system at Los Alamos National Laboratory. This Cray XC40 supercomputer with Sonexion storage serves as the National Nuclear Security Administration’s flagship supercomputer, supporting all three of the NNSA’s national laboratories. We installed the first petaflop supercomputer in India at the Indian Institute of Science. In Europe, we installed numerous XC and storage solutions, including a significant expansion of the existing XC40 supercomputer at the University of Stuttgart in Germany. This new system nearly doubles their computing capacity and is currently the fastest supercomputer in Germany. -
Applying the Roofline Performance Model to the Intel Xeon Phi Knights Landing Processor
Applying the Roofline Performance Model to the Intel Xeon Phi Knights Landing Processor Douglas Doerfler, Jack Deslippe, Samuel Williams, Leonid Oliker, Brandon Cook, Thorsten Kurth, Mathieu Lobet, Tareq Malas, Jean-Luc Vay, and Henri Vincenti Lawrence Berkeley National Laboratory {dwdoerf, jrdeslippe, swwilliams, loliker, bgcook, tkurth, mlobet, tmalas, jlvay, hvincenti}@lbl.gov Abstract The Roofline Performance Model is a visually intuitive method used to bound the sustained peak floating-point performance of any given arithmetic kernel on any given processor architecture. In the Roofline, performance is nominally measured in floating-point operations per second as a function of arithmetic intensity (operations per byte of data). In this study we determine the Roofline for the Intel Knights Landing (KNL) processor, determining the sustained peak memory bandwidth and floating-point performance for all levels of the memory hierarchy, in all the different KNL cluster modes. We then determine arithmetic intensity and performance for a suite of application kernels being targeted for the KNL based supercomputer Cori, and make comparisons to current Intel Xeon processors. Cori is the National Energy Research Scientific Computing Center’s (NERSC) next generation supercomputer. Scheduled for deployment mid-2016, it will be one of the earliest and largest KNL deployments in the world. 1 Introduction Moving an application to a new architecture is a challenge, not only in porting of the code, but in tuning and extracting maximum performance. This is especially true with the introduction of the latest manycore and GPU-accelerated architec- tures, as they expose much finer levels of parallelism that can be a challenge for applications to exploit in their current form. -
TECHNICAL GUIDELINES for APPLICANTS to PRACE 17Th CALL
TECHNICAL GUIDELINES FOR APPLICANTS TO PRACE 17th CALL (T ier-0) The contributing sites and the corresponding computer systems for this call are: System Architecture Site (Country) Core Hours Minimum (node hours) request Joliot Curie - Bull Sequana X1000 GENCI@CEA 134 million 15 million core SKL (FR) (2.8 million) hours Joliot Curie - BULL Sequana GENCI@CEA 72 million 15 million core KNL X1000 (FR) (1,1 million) hours Hazel Hen Cray XC40 System GCS@HLRS 70 million 35 million core (DE) (2.9 million) hours JUWELS Multicore cluster GCS@JSC (DE) 70 million 35 million core (1.5 million) hours Marconi- Lenovo System CINECA (IT) 36 million 15 million core Broadwell (1 million) hours Marconi-KNL Lenovo System CINECA (IT) 612 million 30 million core (9 million) hours MareNostrum Lenovo System BSC (ES) 240 million 15 million core (5 million) hours Piz Daint Cray XC50 System CSCS (CH) 510 million 68 million core (7.5 million) hours Use of GPUs SuperMUC Lenovo NextScale/ GCS@LRZ (DE) 105 million 35 million core SuperMUC-NG Lenovo ThinkSystem (3.8 million) hours The site selection is done together with the specification of the requested computing time by the two sections at the beginning of the online form. The applicant can choose one or several machines as execution system, as long as proper benchmarks and resource request justification are provided on each of the requested systems. The parameters are listed in tables. The first column describes the field in the web online form to be filled in by the applicant. The remaining columns specify the range limits for each system. -
Introduction to Intel Xeon Phi (“Knights Landing”) on Cori
Introduction to Intel Xeon Phi (“Knights Landing”) on Cori" Brandon Cook! Brian Friesen" 2017 June 9 - 1 - Knights Landing is here!" • KNL nodes installed in Cori in 2016 • “Pre-produc=on” for ~ 1 year – No charge for using KNL nodes – Limited access (un7l now!) – Intermi=ent down7me – Frequent so@ware changes • KNL nodes on Cori will soon enter produc=on – Charging Begins 2017 July 1 - 2 - Knights Landing overview" Knights Landing: Next Intel® Xeon Phi™ Processor Intel® Many-Core Processor targeted for HPC and Supercomputing First self-boot Intel® Xeon Phi™ processor that is binary compatible with main line IA. Boots standard OS. Significant improvement in scalar and vector performance Integration of Memory on package: innovative memory architecture for high bandwidth and high capacity Integration of Fabric on package Three products KNL Self-Boot KNL Self-Boot w/ Fabric KNL Card (Baseline) (Fabric Integrated) (PCIe-Card) Potential future options subject to change without notice. All timeframes, features, products and dates are preliminary forecasts and subject to change without further notification. - 3 - Knights Landing overview TILE CHA 2 VPU 2 VPU Knights Landing Overview 1MB L2 Core Core 2 x16 X4 MCDRAM MCDRAM 1 x4 DMI MCDRAM MCDRAM Chip: 36 Tiles interconnected by 2D Mesh Tile: 2 Cores + 2 VPU/core + 1 MB L2 EDC EDC PCIe D EDC EDC M Gen 3 3 I 3 Memory: MCDRAM: 16 GB on-package; High BW D Tile D D D DDR4: 6 channels @ 2400 up to 384GB R R 4 36 Tiles 4 IO: 36 lanes PCIe Gen3. 4 lanes of DMI for chipset C DDR MC connected by DDR MC C Node: 1-Socket only H H A 2D Mesh A Fabric: Omni-Path on-package (not shown) N Interconnect N N N E E L L Vector Peak Perf: 3+TF DP and 6+TF SP Flops S S Scalar Perf: ~3x over Knights Corner EDC EDC misc EDC EDC Streams Triad (GB/s): MCDRAM : 400+; DDR: 90+ Source Intel: All products, computer systems, dates and figures specified are preliminary based on current expectations, and are subject to change without notice. -
Cray XC40 Power Monitoring and Control for Knights Landing
Cray XC40 Power Monitoring and Control for Knights Landing Steven J. Martin, David Rush Matthew Kappel, Michael Sandstedt, Joshua Williams Cray Inc. Cray Inc. Chippewa Falls, WI USA St. Paul, MN USA {stevem,rushd}@cray.com {mkappel,msandste,jw}@cray.com Abstract—This paper details the Cray XC40 power monitor- This paper is organized as follows: In section II, we ing and control capabilities for Intel Knights Landing (KNL) detail blade-level Hardware Supervisory System (HSS) ar- based systems. The Cray XC40 hardware blade design for Intel chitecture changes introduced to enable improved telemetry KNL processors is the first in the XC family to incorporate enhancements directly related to power monitoring feedback gathering capabilities on Cray XC40 blades with Intel KNL driven by customers and the HPC community. This paper fo- processors. Section III provides information on updates to cuses on power monitoring and control directly related to Cray interfaces available for power monitoring and control that blades with Intel KNL processors and the interfaces available are new for Cray XC40 systems and the software released to users, system administrators, and workload managers to to support blades with Intel KNL processors. Section IV access power management features. shows monitoring and control examples. Keywords-Power monitoring; power capping; RAPL; energy efficiency; power measurement; Cray XC40; Intel Knights II. ENHANCED HSS BLADE-LEVEL MONITORING Landing The XC40 KNL blade incorporates a number of en- hancements over previous XC blade types that enable the I. INTRODUCTION collection of power telemetry in greater detail and with Cray has provided advanced power monitoring and control improved accuracy. -
Intel® Parallel Studio XE 2020 Update 2 Release Notes
Intel® Parallel StudIo Xe 2020 uPdate 2 15 July 2020 Contents 1 Introduction ................................................................................................................................................... 2 2 Product Contents ......................................................................................................................................... 3 2.1 Additional Information for Intel-provided Debug Solutions ..................................................... 4 2.2 Microsoft Visual Studio Shell Deprecation ....................................................................................... 4 2.3 Intel® Software Manager ........................................................................................................................... 5 2.4 Supported and Unsupported Versions .............................................................................................. 5 3 What’s New ..................................................................................................................................................... 5 3.1 Intel® Xeon Phi™ Product Family Updates ...................................................................................... 12 4 System Requirements ............................................................................................................................. 13 4.1 Processor Requirements........................................................................................................................ 13 4.2 Disk Space Requirements ..................................................................................................................... -
Effectively Using NERSC
Effectively Using NERSC Richard Gerber Senior Science Advisor HPC Department Head (Acting) NERSC: the Mission HPC Facility for DOE Office of Science Research Largest funder of physical science research in U.S. Bio Energy, Environment Computing Materials, Chemistry, Geophysics Particle Physics, Astrophysics Nuclear Physics Fusion Energy, Plasma Physics 6,000 users, 48 states, 40 countries, universities & national labs Current Production Systems Edison 5,560 Ivy Bridge Nodes / 24 cores/node 133 K cores, 64 GB memory/node Cray XC30 / Aries Dragonfly interconnect 6 PB Lustre Cray Sonexion scratch FS Cori Phase 1 1,630 Haswell Nodes / 32 cores/node 52 K cores, 128 GB memory/node Cray XC40 / Aries Dragonfly interconnect 24 PB Lustre Cray Sonexion scratch FS 1.5 PB Burst Buffer 3 Cori Phase 2 – Being installed now! Cray XC40 system with 9,300 Intel Data Intensive Science Support Knights Landing compute nodes 10 Haswell processor cabinets (Phase 1) 68 cores / 96 GB DRAM / 16 GB HBM NVRAM Burst Buffer 1.5 PB, 1.5 TB/sec Support the entire Office of Science 30 PB of disk, >700 GB/sec I/O bandwidth research community Integrate with Cori Phase 1 on Aries Begin to transition workload to energy network for data / simulation / analysis on one system efficient architectures 4 NERSC Allocation of Computing Time NERSC hours in 300 millions DOE Mission Science 80% 300 Distributed by DOE SC program managers ALCC 10% Competitive awards run by DOE ASCR 2,400 Directors Discretionary 10% Strategic awards from NERSC 5 NERSC has ~100% utilization Important to get support PI Allocation (Hrs) Program and allocation from DOE program manager (L. -
Intel® Xeon Phi™ Processor: Your Path to Deeper Insight
PRODUCT BRIEF Intel® Xeon Phi™ Processor: Your Path to Deeper Insight Intel Inside®. Breakthroughs Outside. Unlock deeper insights to solve your biggest challenges faster with the Intel® Xeon Phi™ processor, a foundational element of Intel® Scalable System Framework (Intel® SSF) What if you could gain deeper insight to pursue new discoveries, drive business innovation or shape the future using advanced analytics? Breakthroughs in computational capability, data accessibility and algorithmic innovation are driving amazing new possibilities. One key to unlocking these deeper insights is the new Intel® Xeon Phi™ processor – Intel’s first processor to deliver the performance of an accelerator with the benefits of a standard host CPU. Designed to help solve your biggest challenges faster and with greater efficiency, the Intel® Xeon Phi™ processor enables machines to rapidly learn without being explicitly programmed, in addition to helping drive new breakthroughs using high performance modeling and simulation, visualization and data analytics. And beyond computation, as a foundational element of Intel® Scalable System Framework (Intel® SSF), the Intel® Xeon Phi™ processor is part of a solution that brings together key technologies for easy-to-deploy and high performance clusters. Solve Your Biggest Challenges Faster1 Designed from the ground up to eliminate bottlenecks, the Intel® Xeon Phi™ processor is Intel’s first bootable host processor specifically designed for highly parallel workloads, and the first to integrate both memory and fabric technologies. Featuring up to 72 powerful and efficient cores with ultra-wide vector capabilities (Intel® Advanced Vector Extensions or AVX-512), the Intel® Xeon Phi™ processor raises the bar for highly parallel computing.