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ORNL Debuts Titan Supercomputer Table of Contents
ReporterRetiree Newsletter December 2012/January 2013 SCIENCE ORNL debuts Titan supercomputer ORNL has completed the installation of Titan, a supercomputer capable of churning through more than 20,000 trillion calculations each second—or 20 petaflops—by employing a family of processors called graphic processing units first created for computer gaming. Titan will be 10 times more powerful than ORNL’s last world-leading system, Jaguar, while overcoming power and space limitations inherent in the previous generation of high- performance computers. ORNL is now home to Titan, the world’s most powerful supercomputer for open science Titan, which is supported by the DOE, with a theoretical peak performance exceeding 20 petaflops (quadrillion calculations per second). (Image: Jason Richards) will provide unprecedented computing power for research in energy, climate change, efficient engines, materials and other disciplines and pave the way for a wide range of achievements in “Titan will provide science and technology. unprecedented computing Table of Contents The Cray XK7 system contains 18,688 nodes, power for research in energy, with each holding a 16-core AMD Opteron ORNL debuts Titan climate change, materials 6274 processor and an NVIDIA Tesla K20 supercomputer ............1 graphics processing unit (GPU) accelerator. and other disciplines to Titan also has more than 700 terabytes of enable scientific leadership.” Betty Matthews memory. The combination of central processing loves to travel .............2 units, the traditional foundation of high- performance computers, and more recent GPUs will allow Titan to occupy the same space as Service anniversaries ......3 its Jaguar predecessor while using only marginally more electricity. “One challenge in supercomputers today is power consumption,” said Jeff Nichols, Benefits ..................4 associate laboratory director for computing and computational sciences. -
FUNDAMENTALS of COMPUTING (2019-20) COURSE CODE: 5023 502800CH (Grade 7 for ½ High School Credit) 502900CH (Grade 8 for ½ High School Credit)
EXPLORING COMPUTER SCIENCE NEW NAME: FUNDAMENTALS OF COMPUTING (2019-20) COURSE CODE: 5023 502800CH (grade 7 for ½ high school credit) 502900CH (grade 8 for ½ high school credit) COURSE DESCRIPTION: Fundamentals of Computing is designed to introduce students to the field of computer science through an exploration of engaging and accessible topics. Through creativity and innovation, students will use critical thinking and problem solving skills to implement projects that are relevant to students’ lives. They will create a variety of computing artifacts while collaborating in teams. Students will gain a fundamental understanding of the history and operation of computers, programming, and web design. Students will also be introduced to computing careers and will examine societal and ethical issues of computing. OBJECTIVE: Given the necessary equipment, software, supplies, and facilities, the student will be able to successfully complete the following core standards for courses that grant one unit of credit. RECOMMENDED GRADE LEVELS: 9-12 (Preference 9-10) COURSE CREDIT: 1 unit (120 hours) COMPUTER REQUIREMENTS: One computer per student with Internet access RESOURCES: See attached Resource List A. SAFETY Effective professionals know the academic subject matter, including safety as required for proficiency within their area. They will use this knowledge as needed in their role. The following accountability criteria are considered essential for students in any program of study. 1. Review school safety policies and procedures. 2. Review classroom safety rules and procedures. 3. Review safety procedures for using equipment in the classroom. 4. Identify major causes of work-related accidents in office environments. 5. Demonstrate safety skills in an office/work environment. -
Florida Course Code Directory Computer Science Course Information 2019-2020
FLORIDA COURSE CODE DIRECTORY COMPUTER SCIENCE COURSE INFORMATION 2019-2020 Section 1007.2616, Florida Statutes, was amended by the Florida Legislature to include the definition of computer science and a requirement that computer science courses be identified in the Course Code Directory published on the Department of Education’s website. DEFINITION: The study of computers and algorithmic processes, including their principles, hardware and software designs, applications, and their impact on society, and includes computer coding and computer programming. SECONDARY COMPUTER SCIENCE COURSES Middle and high schools in each district, including combination schools in which any of grades 6-12 are taught, must provide an opportunity for students to enroll in a computer science course. If a school district does not offer an identified computer science course the district must provide students access to the course through the Florida Virtual School (FLVS) or through other means. COURSE NUMBER COURSE TITLE 0200000 M/J Computer Science Discoveries 0200010 M/J Computer Science Discoveries 1 0200020 M/J Computer Science Discoveries 2 0200305 Computer Science Discoveries 0200315 Computer Science Principles 0200320 AP Computer Science A 0200325 AP Computer Science A Innovation 0200335 AP Computer Science Principles 0200435 PRE-AICE Computer Studies IGCSE Level 0200480 AICE Computer Science 1 AS Level 0200485 AICE Computer Science 2 A Level 0200490 AICE Information Technology 1 AS Level 0200495 AICE Information Technology 2 A Level 0200800 IB Computer -
2017 HPC Annual Report Team Would Like to Acknowledge the Invaluable Assistance Provided by John Noe
sandia national laboratories 2017 HIGH PERformance computing The 2017 High Performance Computing Annual Report is dedicated to John Noe and Dino Pavlakos. Building a foundational framework Editor in high performance computing Yasmin Dennig Contributing Writers Megan Davidson Sandia National Laboratories has a long history of significant contributions to the high performance computing Mattie Hensley community and industry. Our innovative computer architectures allowed the United States to become the first to break the teraflop barrier—propelling us to the international spotlight. Our advanced simulation and modeling capabilities have been integral in high consequence US operations such as Operation Burnt Frost. Strong partnerships with industry leaders, such as Cray, Inc. and Goodyear, have enabled them to leverage our high performance computing capabilities to gain a tremendous competitive edge in the marketplace. Contributing Editor Laura Sowko As part of our continuing commitment to provide modern computing infrastructure and systems in support of Sandia’s missions, we made a major investment in expanding Building 725 to serve as the new home of high performance computer (HPC) systems at Sandia. Work is expected to be completed in 2018 and will result in a modern facility of approximately 15,000 square feet of computer center space. The facility will be ready to house the newest National Nuclear Security Administration/Advanced Simulation and Computing (NNSA/ASC) prototype Design platform being acquired by Sandia, with delivery in late 2019 or early 2020. This new system will enable continuing Stacey Long advances by Sandia science and engineering staff in the areas of operating system R&D, operation cost effectiveness (power and innovative cooling technologies), user environment, and application code performance. -
Titan: a New Leadership Computer for Science
Titan: A New Leadership Computer for Science Presented to: DOE Advanced Scientific Computing Advisory Committee November 1, 2011 Arthur S. Bland OLCF Project Director Office of Science Statement of Mission Need • Increase the computational resources of the Leadership Computing Facilities by 20-40 petaflops • INCITE program is oversubscribed • Programmatic requirements for leadership computing continue to grow • Needed to avoid an unacceptable gap between the needs of the science programs and the available resources • Approved: Raymond Orbach January 9, 2009 • The OLCF-3 project comes out of this requirement 2 ASCAC – Nov. 1, 2011 Arthur Bland INCITE is 2.5 to 3.5 times oversubscribed 2007 2008 2009 2010 2011 2012 3 ASCAC – Nov. 1, 2011 Arthur Bland What is OLCF-3 • The next phase of the Leadership Computing Facility program at ORNL • An upgrade of Jaguar from 2.3 Petaflops (peak) today to between 10 and 20 PF by the end of 2012 with operations in 2013 • Built with Cray’s newest XK6 compute blades • When completed, the new system will be called Titan 4 ASCAC – Nov. 1, 2011 Arthur Bland Cray XK6 Compute Node XK6 Compute Node Characteristics AMD Opteron 6200 “Interlagos” 16 core processor @ 2.2GHz Tesla M2090 “Fermi” @ 665 GF with 6GB GDDR5 memory Host Memory 32GB 1600 MHz DDR3 Gemini High Speed Interconnect Upgradeable to NVIDIA’s next generation “Kepler” processor in 2012 Four compute nodes per XK6 blade. 24 blades per rack 5 ASCAC – Nov. 1, 2011 Arthur Bland ORNL’s “Titan” System • Upgrade of existing Jaguar Cray XT5 • Cray Linux Environment -
<Computer Science, Software Engineering >
<Computer Science, Software Engineering > The following is a hypothetical schedule, based on the 2018-2019 catalog. It assumes no transferred credits, no requirements waived by placement tests, and no courses taken in the summer. UW-Eau Claire cannot guarantee all courses will be offered as shown, but will provide a range of courses that will enable prepared students to fulfill their requirements in a timely period. This is just a guide. Please consult your advisor, the catalog, and your degree audit for specific requirements. FIRST YEAR FIRST SEMESTER SECOND SEMESTER Subj/Area/Course Title Crs Subj/Area/Course Title Crs CS 145 Intro to OO Prog 4 CS 245 Adv. Prog. Data Structures 4 CS 146 Big Picture in CS 1 WRIT 114/116 Blugold Seminar 5 MATH 112 or Precalculus 4 MATH 114 Calculus I 4 MATH 109 & 113 College Algebra and Trig (Winterim) (MATH 113 – prereq or concurrent enrollment for CS 245 LE Core Electives 6 LE Core Electives 3 TOTAL 15 TOTAL 16 SECOND YEAR FIRST SEMESTER SECOND SEMESTER Subj/Area/Course Title Crs Subj/Area/Course Title Crs CS 260 Database Systems (prereq for CS355) 4 CS 252 Intro. Computer Systems (prereq for CS 4 CS352, 452) MINOR Courses for Minor/Certificate 6 CS 268 Web Systems 3 LE Core Electives 6 LE Core Electives 3-6 MINOR Courses for Minor/Certificate 3 TOTAL 16 TOTAL 15 THIRD YEAR FIRST SEMESTER – Apply for Admission to Major SECOND SEMESTER Subj/Area/Course Title Crs Subj/Area/Course Title Crs CS 335 Algorithms (prereq for CS 452) 3 CS 355 Software Engineering I 3 (Fall only) (Spring only) CS 352 ComputeOrg. -
EVGA Geforce GTX TITAN X Superclocked
EVGA GeForce GTX TITAN X Superclocked Part Number: 12G-P4-2992-KR The EVGA GeForce GTX TITAN X combines the technologies and performance of the new NVIDIA Maxwell architecture in the fastest and most advanced graphics card on the planet. This incredible GPU delivers unrivaled graphics, acoustic, thermal and power-efficient performance. The most demanding enthusiast can now experience extreme resolutions up to 4K-and beyond. Enjoy hyper-realistic, real-time lighting with advanced NVIDIA VXGI, as well as NVIDIA G-SYNC display technology for smooth, tear-free gaming. Plus, you get DSR technology that delivers a brilliant 4K experience, even on a 1080p display. SPECIFICATIONS KEY FEATURES RESOLUTION & REFRESH Base Clock: 1127 MHZ NVIDIA Dynamic Super Resolution Technology Max Monitors Supported: 4 Boost Clock: 1216 MHz NVIDIA MFAA Technology 240Hz Max Refresh Rate Memory Clock: 7010 MHz Effective NVIDIA GameWorks Technology Max Analog: 2048x1536 CUDA Cores: 3072 NVIDIA GameStream Technology Max Digital: 4096x2160 Bus Type: PCI-E 3.0 NVIDIA G-SYNC Ready Memory Detail: 12288MB GDDR5 Microsoft DirectX 12 REQUIREMENTS Memory Bit Width: 384 Bit NVIDIA GPU Boost 2.0 600 Watt or greater power supply.**** Memory Speed: 0.28ns NVIDIA Adaptive Vertical Sync PCI Express, PCI Express 2.0 or PCI Express Memory Bandwidth: 336.5 GB/s NVIDIA Surround Technology 3.0 compliant motherboard with one graphics NVIDIA SLI Ready slot. DIMENSIONS NVIDIA CUDA Technology An available 6-pin PCI-E power connector and Height: 4.376in - 111.15mm OpenGL 4.4 Support an available 8 pin PCI-E power connector Length: 10.5in - 266.7mm OpenCL Support Windows 8 32/64bit, Windows 7 32/64bit, Windows Vista 32/64bit Width: Dual Slot HDMI 2.0, DisplayPort 1.2 and Dual-link DVI PCI Express 3.0 **Support for HDMI includes GPU-accelerated Blu-ray 3D support (Blu-ray 3D playback requires the purchase of a compatible software player from CyberLink, ArcSoft, Corel, or Sonic), x.v.Color, HDMI Deep Color, and 7.1 digital surround sound. -
Artificial Intelligence Program 1
Artificial Intelligence Program 1 Artificial Intelligence Program Reid Simmons, Director of the BSAI program (NSH 3213) 15-122 Principles of Imperative Computation 10 (students without credit or a waiver for 15-112, Jean Harpley, Program Coordinator (NSH 1517) Fundamentals of Programming and Computer www.cs.cmu.edu/bs-in-artificial-intelligence (http://www.cs.cmu.edu/bs-in- Science, must take 15-112 before 15-122) artificial-intelligence/) 15-150 Principles of Functional Programming 10 15-210 Parallel and Sequential Data Structures and 12 Overview Algorithms 15-213 Introduction to Computer Systems 12 Carnegie Mellon University has led the world in artificial intelligence 15-251 Great Ideas in Theoretical Computer Science 12 education and innovation since the field was created. It's only natural, then, that the School of Computer Science would offer the nation's first bachelor's degree in Artificial Intelligence, which started in Fall 2018. Artificial Intelligence The new BSAI program gives students the in-depth knowledge needed to transform large amounts of data into actionable decisions. The program All of the following three AI core courses: Units and its curriculum focus on how complex inputs — such as vision, language 07-180 Concepts in Artificial Intelligence 5 and huge databases — can be used to make decisions or enhance human 15-281 Artificial Intelligence: Representation and 12 capabilities. The curriculum includes coursework in computer science, Problem Solving math, statistics, computational modeling, machine learning and symbolic computation. Because Carnegie Mellon is devoted to AI for social good, 10-315 Introduction to Machine Learning (SCS Majors) 12 students will also take courses in ethics and social responsibility, with the plus one of the following AI core courses: option to participate in independent study projects that change the world for 16-385 Computer Vision 12 the better — in areas like healthcare, transportation and education. -
The Artisanal Nuke, 2014
The Artisanal Nuke Mary C. Dixon US Air Force Center for Unconventional Weapons Studies Maxwell Air Force Base, Alabama THE ARTISANAL NUKE by Mary C. Dixon USAF Center for Unconventional Weapons Studies 125 Chennault Circle Maxwell Air Force Base, Alabama 36112-6427 July 2014 Disclaimer The opinions, conclusions, and recommendations expressed or implied in this publication are those of the author and do not necessarily reflect the views of the Air University, Air Force, or Department of Defense. ii Table of Contents Chapter Page Disclaimer ................................................................................................... ii Table of Contents ....................................................................................... iii About the Author ......................................................................................... v Acknowledgements ..................................................................................... vi Abstract ....................................................................................................... ix 1 Introduction .............................................................................................. 1 2 Background ............................................................................................ 19 3 Stockpile Stewardship ........................................................................... 27 4 Opposition & Problems ......................................................................... 71 5 Milestones & Accomplishments .......................................................... -
A Comprehensive Performance Comparison of Dedicated and Embedded GPU Systems
DUJE (Dicle University Journal of Engineering) 11:3 (2020) Page 1011-1020 Research Article A Comprehensive Performance Comparison of Dedicated and Embedded GPU Systems Adnan Ozsoy 1,* 1 Department of Computer Engineering, Hacettepe University, [email protected] (ORCID: https://orcid.org/0000-0002-0302-3721) ARTICLE INFO ABSTRACT Article history: General purpose usage of graphics processing units (GPGPU) is becoming increasingly important as Received 26 May 2020 Received in revised form 29 June 2020 graphics processing units (GPUs) get more powerful and their widespread usage in performance-oriented Accepted 9 July 2020 computing. GPGPUs are mainstream performance hardware in workstation and cluster environments and Available online 30 September 2020 their behavior in such setups are highly analyzed. Recently, NVIDIA, the leader hardware and software vendor in GPGPU computing, started to produce more energy efficient embedded GPGPU systems, Jetson Keywords: NVIDIA Jetson, series GPUs, to make GPGPU computing more applicable in domains where energy and space are limited. Embedded GPGPU, CUDA Although, the architecture of the GPUs in Jetson systems is the same as the traditional dedicated desktop graphic cards, the interaction between the GPU and the other components of the system such as main memory, central processing unit (CPU), and hard disk, is a lot different than traditional desktop solutions. To fully understand the capabilities of the Jetson series embedded solutions, in this paper we run several applications from many different domains and compare the performance characteristics of these applications on both embedded and dedicated desktop GPUs. After analyzing the collected data, we have identified certain application domains and program behaviors that Jetson series can deliver performance comparable to dedicated GPU performance. -
Power Efficiency and Performance with ORNL's Cray XK7 Titan
Power Efficiency and Performance with ORNL's Cray XK7 Titan Work by Jim Rogers Presented by Arthur Bland Director of Operations National Center for Computational Sciences Oak Ridge National Laboratory ORNL’s “Titan” Hybrid System: Cray XK7 with AMD Opteron and NVIDIA Tesla processors SYSTEM SPECIFICATIONS: • Peak performance of 27.1 PF • 24.5 GPU + 2.6 CPU • 18,688 Compute Nodes each with: • 16-Core AMD Opteron CPU • NVIDIA Tesla “K20x” GPU • 32 + 6 GB memory • 512 Service and I/O nodes 2 4,352 ft • 200 Cabinets 2 404 m • 710 TB total system memory • Cray Gemini 3D Torus Interconnect • 8.9 MW peak power 2 ORNL ISC’13 ORNL's Cray XK7 Titan | Sample Run: HPL Consumption MW, Instantaneous kW-hours (Cumulative) 10 8,000 7,545.56kW-hr 9 7,000 8 RmaxPower = 8,296.53kW 6,000 7 Instantaneous Measurements 5,000 6 8.93 MW 21.42 PF 5 2,397.14 MF/Watt 4,000 4 3,000 kW, Instantaneous 3 2,000 RmaxPower 2 kW-hours, cumulative 1,000 1 Run Time Duration (hh:mm:ss) - - 3 0:00:53 0:01:53 0:02:53 0:03:53 0:04:53 0:05:53 0:06:53 0:07:53 0:08:53 0:09:53 0:10:53 0:11:53 0:12:53 0:13:53 0:14:53 0:15:53 0:16:53 0:17:53 0:18:53 0:19:53 0:20:53 0:21:53 0:22:53 0:23:53 0:24:53 0:25:53 0:26:53 0:27:53 0:28:53 0:29:53 0:30:53 0:31:53 0:32:53 0:33:53 0:34:53 0:35:53 0:36:53 0:37:53 0:38:53 0:39:53 0:40:53 0:41:53 0:42:53 0:43:53 0:44:53 ORNL0:45:53 0:46:53 0:47:53 ISC’130:48:53 0:49:53 0:50:53 0:51:53 0:52:53 0:53:53 0:54:53 Revised Metering Capabilities for HPC Systems at ORNL • Each of the seven (7) 2.5 MVA or 3.0 MVA Cabinet/rack transformers designated for HPC use is Chassis/crate now metered by a separate Schneider Electric CM4000 PowerLogic Circuit blade Monitor CPU • “F” Position within the EEHPCWG A Aspect 4 Category Power • Highly accurate power quality monitor Conv. -
Harnessing the Power of Titan with the Uintah Computational Framework
Alan Humphrey, Qingyu Meng, Brad Peterson, Martin Berzins Scientific Computing and Imaging Institute, University of Utah I. Uintah Framework – Overview II. Extending Uintah to Leverage GPUs III. Target Application – DOE NNSA PSAAP II Multidisciplinary Simulation Center IV. A Developing GPU-based Radiation Model V. Summary and Questions Central Theme: Shielding developers from complexities inherent in heterogeneous systems like Titan & Keeneland Thanks to: John Schmidt, Todd Harman, Jeremy Thornock, J. Davison de St. Germain Justin Luitjens and Steve Parker, NVIDIA DOE Titan – 20 Petaflops DOE for funding the CSAFE project from 1997-2010, 18,688 GPUs DOE NETL, DOE NNSA, INCITE, ALCC NSF for funding via SDCI and PetaApps, XSEDE NSF Keeneland Keeneland Computing Facility 792 GPUs Oak Ridge Leadership Computing Facility for access to Titan DOE NNSA PSAPP II (March 2014) Parallel, adaptive multi-physics framework Fluid-structure interaction problems Patch-based AMR: Particle system and mesh-based fluid solve Plume Fires Chemical/Gas Mixing Foam Explosions Angiogenesis Compaction Sandstone Compaction Industrial Flares Shaped Charges MD – Multiscale Materials Design Patch-based domain decomposition Asynchronous ALCF Mira task-based paradigm Task - serial code on generic “patch” OLCF Titan Task specifies desired halo region Clear separation of user code from parallelism/runtime Strong Scaling: Uintah infrastructure provides: Fluid-structure interaction problem using MPMICE algorithm w/ AMR • automatic MPI message generation • load balancing •