High-Performance Computing Capabilities and Allocations
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NVIDIA Tesla Personal Supercomputer, Please Visit
NVIDIA TESLA PERSONAL SUPERCOMPUTER TESLA DATASHEET Get your own supercomputer. Experience cluster level computing performance—up to 250 times faster than standard PCs and workstations—right at your desk. The NVIDIA® Tesla™ Personal Supercomputer AccessiBLE to Everyone TESLA C1060 COMPUTING ™ PROCESSORS ARE THE CORE is based on the revolutionary NVIDIA CUDA Available from OEMs and resellers parallel computing architecture and powered OF THE TESLA PERSONAL worldwide, the Tesla Personal Supercomputer SUPERCOMPUTER by up to 960 parallel processing cores. operates quietly and plugs into a standard power strip so you can take advantage YOUR OWN SUPERCOMPUTER of cluster level performance anytime Get nearly 4 teraflops of compute capability you want, right from your desk. and the ability to perform computations 250 times faster than a multi-CPU core PC or workstation. NVIDIA CUDA UnlocKS THE POWER OF GPU parallel COMPUTING The CUDA™ parallel computing architecture enables developers to utilize C programming with NVIDIA GPUs to run the most complex computationally-intensive applications. CUDA is easy to learn and has become widely adopted by thousands of application developers worldwide to accelerate the most performance demanding applications. TESLA PERSONAL SUPERCOMPUTER | DATASHEET | MAR09 | FINAL FEATURES AND BENEFITS Your own Supercomputer Dedicated computing resource for every computational researcher and technical professional. Cluster Performance The performance of a cluster in a desktop system. Four Tesla computing on your DesKtop processors deliver nearly 4 teraflops of performance. DESIGNED for OFFICE USE Plugs into a standard office power socket and quiet enough for use at your desk. Massively Parallel Many Core 240 parallel processor cores per GPU that can execute thousands of GPU Architecture concurrent threads. -
The Sunway Taihulight Supercomputer: System and Applications
SCIENCE CHINA Information Sciences . RESEARCH PAPER . July 2016, Vol. 59 072001:1–072001:16 doi: 10.1007/s11432-016-5588-7 The Sunway TaihuLight supercomputer: system and applications Haohuan FU1,3 , Junfeng LIAO1,2,3 , Jinzhe YANG2, Lanning WANG4 , Zhenya SONG6 , Xiaomeng HUANG1,3 , Chao YANG5, Wei XUE1,2,3 , Fangfang LIU5 , Fangli QIAO6 , Wei ZHAO6 , Xunqiang YIN6 , Chaofeng HOU7 , Chenglong ZHANG7, Wei GE7 , Jian ZHANG8, Yangang WANG8, Chunbo ZHOU8 & Guangwen YANG1,2,3* 1Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing 100084, China; 2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; 3National Supercomputing Center in Wuxi, Wuxi 214072, China; 4College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; 5Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; 6First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China; 7Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; 8Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China Received May 27, 2016; accepted June 11, 2016; published online June 21, 2016 Abstract The Sunway TaihuLight supercomputer is the world’s first system with a peak performance greater than 100 PFlops. In this paper, we provide a detailed introduction to the TaihuLight system. In contrast with other existing heterogeneous supercomputers, which include both CPU processors and PCIe-connected many-core accelerators (NVIDIA GPU or Intel Xeon Phi), the computing power of TaihuLight is provided by a homegrown many-core SW26010 CPU that includes both the management processing elements (MPEs) and computing processing elements (CPEs) in one chip. -
Scheduling Many-Task Workloads on Supercomputers: Dealing with Trailing Tasks
Scheduling Many-Task Workloads on Supercomputers: Dealing with Trailing Tasks Timothy G. Armstrong, Zhao Zhang Daniel S. Katz, Michael Wilde, Ian T. Foster Department of Computer Science Computation Institute University of Chicago University of Chicago & Argonne National Laboratory [email protected], [email protected] [email protected], [email protected], [email protected] Abstract—In order for many-task applications to be attrac- as a worker and allocate one task per node. If tasks are tive candidates for running on high-end supercomputers, they single-threaded, each core or virtual thread can be treated must be able to benefit from the additional compute, I/O, as a worker. and communication performance provided by high-end HPC hardware relative to clusters, grids, or clouds. Typically this The second feature of many-task applications is an empha- means that the application should use the HPC resource in sis on high performance. The many tasks that make up the such a way that it can reduce time to solution beyond what application effectively collaborate to produce some result, is possible otherwise. Furthermore, it is necessary to make and in many cases it is important to get the results quickly. efficient use of the computational resources, achieving high This feature motivates the development of techniques to levels of utilization. Satisfying these twin goals is not trivial, because while the efficiently run many-task applications on HPC hardware. It parallelism in many task computations can vary over time, allows people to design and develop performance-critical on many large machines the allocation policy requires that applications in a many-task style and enables the scaling worker CPUs be provisioned and also relinquished in large up of existing many-task applications to run on much larger blocks rather than individually. -
Supercomputer Fugaku
Supercomputer Fugaku Toshiyuki Shimizu Feb. 18th, 2020 FUJITSU LIMITED Copyright 2020 FUJITSU LIMITED Outline ◼ Fugaku project overview ◼ Co-design ◼ Approach ◼ Design results ◼ Performance & energy consumption evaluation ◼ Green500 ◼ OSS apps ◼ Fugaku priority issues ◼ Summary 1 Copyright 2020 FUJITSU LIMITED Supercomputer “Fugaku”, formerly known as Post-K Focus Approach Application performance Co-design w/ application developers and Fujitsu-designed CPU core w/ high memory bandwidth utilizing HBM2 Leading-edge Si-technology, Fujitsu's proven low power & high Power efficiency performance logic design, and power-controlling knobs Arm®v8-A ISA with Scalable Vector Extension (“SVE”), and Arm standard Usability Linux 2 Copyright 2020 FUJITSU LIMITED Fugaku project schedule 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Fugaku development & delivery Manufacturing, Apps Basic Detailed design & General Feasibility study Installation review design Implementation operation and Tuning Select Architecture & Co-Design w/ apps groups apps sizing 3 Copyright 2020 FUJITSU LIMITED Fugaku co-design ◼ Co-design goals ◼ Obtain the best performance, 100x apps performance than K computer, within power budget, 30-40MW • Design applications, compilers, libraries, and hardware ◼ Approach ◼ Estimate perf & power using apps info, performance counts of Fujitsu FX100, and cycle base simulator • Computation time: brief & precise estimation • Communication time: bandwidth and latency for communication w/ some attributes for communication patterns • I/O time: ◼ Then, optimize apps/compilers etc. and resolve bottlenecks ◼ Estimation of performance and power ◼ Precise performance estimation for primary kernels • Make & run Fugaku objects on the Fugaku cycle base simulator ◼ Brief performance estimation for other sections • Replace performance counts of FX100 w/ Fugaku params: # of inst. commit/cycle, wait cycles of barrier, inst. -
E-Commerce Marketplace
E-COMMERCE MARKETPLACE NIMBIS, AWESIM “Nimbis is providing, essentially, the e-commerce infrastructure that allows suppliers and OEMs DEVELOP COLLABORATIVE together, to connect together in a collaborative HPC ENVIRONMENT form … AweSim represents a big, giant step forward in that direction.” Nimbis, founded in 2008, acts as a clearinghouse for buyers and sellers of technical computing — Bob Graybill, Nimbis president and CEO services and provides pre-negotiated access to high performance computing services, software, and expertise from the leading compute time vendors, independent software vendors, and domain experts. Partnering with the leading computing service companies, Nimbis provides users with a choice growing menu of pre-qualified, pre-negotiated services from HPC cycle providers, independent software vendors, domain experts, and regional solution providers, delivered on a “pay-as-you- go” basis. Nimbis makes it easier and more affordable for desktop users to exploit technical computing for faster results and superior products and solutions. VIRTUAL DESIGNS. REAL BENEFITS. Nimbis Services Inc., a founding associate of the AweSim industrial engagement initiative led by the Ohio Supercomputer Center, has been delving into access complexities and producing, through innovative e-commerce solutions, an easy approach to modeling and simulation resources for small and medium-sized businesses. INFORMATION TECHNOLOGY INFORMATION TECHNOLOGY 2016 THE CHALLENGE Nimbis and the AweSim program, along with its predecessor program Blue Collar Computing, have identified several obstacles that impede widespread adoption of modeling and simulation powered by high performance computing: expensive hardware, complex software and extensive training. In response, the public/private partnership is developing and promoting use of customized applications (apps) linked to OSC’s powerful supercomputer systems. -
Wearable Technology for Enhanced Security
Communications on Applied Electronics (CAE) – ISSN : 2394-4714 Foundation of Computer Science FCS, New York, USA Volume 5 – No.10, September 2016 – www.caeaccess.org Wearable Technology for Enhanced Security Agbaje M. Olugbenga, PhD Babcock University Department of Computer Science Ogun State, Nigeria ABSTRACT Sproutling. Watches like the Apple Watch, and jewelry such Wearable's comprise of sensors and have computational as Cuff and Ringly. ability. Gadgets such as wristwatches, pens, and glasses with Taking a look at the history of computer generations up to the installed cameras are now available at cheap prices for user to present, we could divide it into three main types: mainframe purchase to monitor or securing themselves. Nigerian faced computing, personal computing, and ubiquitous or pervasive with several kidnapping in schools, homes and abduction for computing[4]. The various divisions is based on the number ransomed collection and other unlawful acts necessitate these of computers per users. The mainframe computing describes reviews. The success of the wearable technology in medical one large computer connected to many users and the second, uses prompted the research into application into security uses. personal computing, as one computer per person while the The method of research is the use of case studies and literature term ubiquitous computing however, was used in 1991 by search. This paper takes a look at the possible applications of Paul Weiser. Weiser depicted a world full of embedded the wearable technology to combat the cases of abduction and sensing technologies to streamline and improve life [5]. kidnapping in Nigeria. Nigeria faced with several kidnapping in schools and homes General Terms are in dire need for solution. -
Defining and Measuring Supercomputer Reliability
Defining and Measuring Supercomputer Reliability, Availability, and Serviceability (RAS) Jon Stearley <[email protected]> Sandia National Laboratories?? Albuquerque, New Mexico Abstract. The absence of agreed definitions and metrics for supercomputer RAS obscures meaningful discussion of the issues involved and hinders their solution. This paper seeks to foster a common basis for communication about supercom- puter RAS, by proposing a system state model, definitions, and measurements. These are modeled after the SEMI-E10 [1] specification which is widely used in the semiconductor manufacturing industry. 1 Impetus The needs for standardized terminology and metrics for supercomputer RAS begins with the procurement process, as the below quotation excellently summarizes: “prevailing procurement practices are ... a lengthy and expensive undertak- ing both for the government and for participating vendors. Thus any technically valid methodologies that can standardize or streamline this process will result in greater value to the federally-funded centers, and greater opportunity to fo- cus on the real problems involved in deploying and utilizing one of these large systems.” [2] Appendix A provides several examples of “numerous general hardware and software specifications” [2] from the procurements of several modern systems. While there are clearly common issues being communicated, the language used is far from consistent. Sites struggle to describe their reliability needs, and vendors strive to translate these descriptions into capabilities they can deliver to multiple customers. Another example is provided by this excerpt: “The system must be reliable... It is important to define what we mean by reliable. We do not mean high availability... Reliability in this context means that a large parallel job running for many hours has a high probability of suc- cessfully completing. -
Computer Architectures an Overview
Computer Architectures An Overview PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 25 Feb 2012 22:35:32 UTC Contents Articles Microarchitecture 1 x86 7 PowerPC 23 IBM POWER 33 MIPS architecture 39 SPARC 57 ARM architecture 65 DEC Alpha 80 AlphaStation 92 AlphaServer 95 Very long instruction word 103 Instruction-level parallelism 107 Explicitly parallel instruction computing 108 References Article Sources and Contributors 111 Image Sources, Licenses and Contributors 113 Article Licenses License 114 Microarchitecture 1 Microarchitecture In computer engineering, microarchitecture (sometimes abbreviated to µarch or uarch), also called computer organization, is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures.[1] Implementations might vary due to different goals of a given design or due to shifts in technology.[2] Computer architecture is the combination of microarchitecture and instruction set design. Relation to instruction set architecture The ISA is roughly the same as the programming model of a processor as seen by an assembly language programmer or compiler writer. The ISA includes the execution model, processor registers, address and data formats among other things. The Intel Core microarchitecture microarchitecture includes the constituent parts of the processor and how these interconnect and interoperate to implement the ISA. The microarchitecture of a machine is usually represented as (more or less detailed) diagrams that describe the interconnections of the various microarchitectural elements of the machine, which may be everything from single gates and registers, to complete arithmetic logic units (ALU)s and even larger elements. -
Programming on K Computer
Programming on K computer Koh Hotta The Next Generation Technical Computing Fujitsu Limited Copyright 2010 FUJITSU LIMITED System Overview of “K computer” Target Performance : 10PF over 80,000 processors Over 640K cores Over 1 Peta Bytes Memory Cutting-edge technologies CPU : SPARC64 VIIIfx 8 cores, 128GFlops Extension of SPARC V9 Interconnect, “Tofu” : 6-D mesh/torus Parallel programming environment. 1 Copyright 2010 FUJITSU LIMITED I have a dream that one day you just compile your programs and enjoy high performance on your high-end supercomputer. So, we must provide easy hybrid parallel programming method including compiler and run-time system support. 2 Copyright 2010 FUJITSU LIMITED User I/F for Programming for K computer K computer Client System FrontFront End End BackBack End End (80000 (80000 Nodes) Nodes) IDE Interface CCommommandand JobJob C Controlontrol IDE IntInteerfrfaceace debugger DDebugebuggerger App IntInteerfrfaceace App Interactive Debugger GUI Data Data Data CoConvnveerrtteerr Data SamplSampleerr debugging partition official op VisualizedVisualized SSaammpplingling Stage out partition Profiler DaDattaa DaDattaa (InfiniBand) 3 Copyright 2010 FUJITSU LIMITED Parallel Programming 4 Copyright 2010 FUJITSU LIMITED Hybrid Parallelism on over-640K cores Too large # of processes to manipulate To reduce number of processes, hybrid thread-process programming is required But Hybrid parallel programming is annoying for programmers Even for multi-threading, procedure level or outer loop parallelism was desired Little opportunity for such coarse grain parallelism System support for “fine grain” parallelism is required 5 Copyright 2010 FUJITSU LIMITED Targeting inner-most loop parallelization Automatic vectorization technology has become mature, and vector-tuning is easy for programmers. Inner-most loop parallelism, which is fine-grain, should be an important portion for peta-scale parallelization. -
40 Jahre Erfolgsgeschichte BS2000 Vortrag Von 2014
Fujitsu NEXT Arbeitskreis BS2000 40 Jahre BS2000: Eine Erfolgsgeschichte Achim Dewor Fujitsu NEXT AK BS2000 5./6. Mai 2014 0 © FUJITSU LIMITED 2014 Was war vor 40 Jahren eigentlich so los? (1974) 1 © FUJITSU LIMITED 2014 1974 APOLLO-SOJUS-KOPPLUNG Die amtierenden Staatspräsidenten Richard Nixon und Leonid Breschnew unterzeichnen das Projekt. Zwei Jahre arbeitet man an den Vorbereitungen für eine neue gemeinsame Kopplungsmission 2 © FUJITSU LIMITED 2014 1974 Der erste VW Golf wird ausgeliefert 3 © FUJITSU LIMITED 2014 1974 Willy Brandt tritt zurück 6. MAI 1974 TRITT BUNDESKANZLER WILLY BRANDT IM VERLAUF DER SPIONAGE-AFFÄRE UM SEINEN PERSÖNLICHEN REFERENTEN, DEN DDR-SPION GÜNTER GUILLAUME 4 © FUJITSU LIMITED 2014 1974 Muhammad Ali gegen George Foreman „Ich werde schweben wie ein Schmetterling, zustechen wie eine Biene", "Ich bin das Größte, das jemals gelebt hat“ "Ich weiß nicht immer, wovon ich rede. Aber ich weiß, dass ich recht habe“ (Zitate von Ali) 5 © FUJITSU LIMITED 2014 1974 Deutschland wird Fußball-Weltmeister Breitners Elfer zum Ausgleich und das unvergängliche, typische Müller- Siegtor aus der Drehung. Es war der sportliche Höhepunkt der wohl besten deutschen Mannschaft aller Zeiten. 6 © FUJITSU LIMITED 2014 Was ist eigentlich „alt“? These: „Stillstand macht alt …. …. ewig jung bleibt, was laufend weiterentwickelt wird!“ 7 © FUJITSU LIMITED 2014 Was ist „alt“ - was wurde weiterentwickelt ? Wird das Fahrrad bald 200 Jahre alt !? Welches? Das linke oder das rechte? Fahrrad (Anfang 1817) Fahrrad (2014) 8 © FUJITSU LIMITED 2014 Was ist „alt“ - was wurde weiterentwickelt ? Soroban (Japan) Im Osten, vom Balkan bis nach China, wird er hier und da noch als preiswerte Rechenmaschine bei kleineren Geschäften verwendet. -
Ushering in a New Era: Argonne National Laboratory & Aurora
Ushering in a New Era Argonne National Laboratory’s Aurora System April 2015 ANL Selects Intel for World’s Biggest Supercomputer 2-system CORAL award extends IA leadership in extreme scale HPC Aurora Argonne National Laboratory >180PF Trinity NNSA† April ‘15 Cori >40PF NERSC‡ >30PF July ’14 + Theta Argonne National Laboratory April ’14 >8.5PF >$200M ‡ Cray* XC* Series at National Energy Research Scientific Computing Center (NERSC). † Cray XC Series at National Nuclear Security Administration (NNSA). 2 The Most Advanced Supercomputer Ever Built An Intel-led collaboration with ANL and Cray to accelerate discovery & innovation >180 PFLOPS (option to increase up to 450 PF) 18X higher performance† >50,000 nodes Prime Contractor 13MW >6X more energy efficient† 2018 delivery Subcontractor Source: Argonne National Laboratory and Intel. †Comparison of theoretical peak double precision FLOPS and power consumption to ANL’s largest current system, MIRA (10PFs and 4.8MW) 3 Aurora | Science From Day One! Extreme performance for a broad range of compute and data-centric workloads Transportation Biological Science Renewable Energy Training Argonne Training Program on Extreme- Scale Computing Aerodynamics Biofuels / Disease Control Wind Turbine Design / Placement Materials Science Computer Science Public Access Focus Areas Focus US Industry and International Co-array Fortran Batteries / Solar Panels New Programming Models 4 Aurora | Built on a Powerful Foundation Breakthrough technologies that deliver massive benefits Compute Interconnect File System 3rd Generation 2nd Generation Intel® Xeon Phi™ Intel® Omni-Path Intel® Lustre* Architecture Software >17X performance† >20X faster† >3X faster† FLOPS per node >500 TB/s bi-section bandwidth >1 TB/s file system throughput >12X memory bandwidth† >2.5 PB/s aggregate node link >5X capacity† bandwidth >30PB/s aggregate >150TB file system capacity in-package memory bandwidth Integrated Intel® Omni-Path Architecture Processor code name: Knights Hill Source: Argonne National Laboratory and Intel. -
Toward Automated Cache Partitioning for the K Computer
IPSJ SIG Technical Report Toward Automated Cache Partitioning for the K Computer Swann Perarnau1 Mitsuhisa Sato1,2 Abstract: The processor architecture available on the K computer (SPARC64VIIIfx) features an hardware cache par- titioning mechanism called sector cache. This facility enables software to split the memory cache in two independent sectors and to select which one will receive a line when it is retrieved from memory. Such control over the cache by an application enables significant performance optimization opportunities for memory intensive programs, that several studies on software-controlled cache partitioning environments already demonstrated. Most of these previous studies share the same implementation idea: the use of page coloring and an overriding of the operating system virtual memory manager. However, the sector cache differs in several key points over these imple- mentations, making the use of the existing analysis or optimization strategies impractical, while enabling new ones. For example, while most studies overlooked the issue of phase changes in an application because of the prohibitive cost of a repartitioning, the sector cache provides this feature without any costs, allowing multiple cache distribution strategies to alternate during an execution, providing the best allocation of the current locality pattern. Unfortunately, for most application programmers, this hardware cache partitioning facility is not easy to take advantage of. It requires intricate knowledge of the memory access patterns of a code and the ability to identify data structures that would benefit from being isolated in cache. This optimization process can be tedious, with multiple code modifications and countless application runs necessary to achieve a good optimization scheme.