Outline The Tutorials Plenary Talks Papers & Panels The Top500 list The Exhibition http://sc10.supercomputing.org/
Day 0 - Arrival US Airways entertainment system is running Linux!
A lecture by Prof. Rubin Landau Computational Physics at the Educational Track 3:30PM - 5:00PM Communities, Education Physics: Examples in Computational Physics, Part 2 Physics: Examples in Computational Physics, Part 2 Rubin Landau 297 Although physics faculty are incorporating computers to enhance physics education, computation is often viewed as a black box whose inner workings need not be understood. We propose to open up the computational black box by providing Computational Physics (CP) curricula materials based on a problem-solving paradigm that can be incorporated into existing physics classes, or used in stand-alone CP classes. The curricula materials assume a computational science point of view, where understanding of the applied math and the CS is also important, and usually involve a compiled language in order for the students to get closer to the algorithms. The materials derive from a new CP eTextbook available from Compadre that includes video-based lectures, programs, applets, visualizations and animations.
Eclipse PTP At last FORTRAN has an advanced, free, IDE !!! PTP - Parallel Tools Platform http://www.eclipse.org/ptp/
Python for Scientific Computing
Amazon Cluster GPU Instances provide 22 GB of memory, 33.5 EC2 Compute Units, and utilize the Amazon EC2 Cluster network, which provides high throughput and low latency for High Performance Computing (HPC) and data intensive applications. Each GPU instance features two NVIDIA Tesla® M2050 GPUs, delivering peak performance of more than one trillion double-precision FLOPS. Many workloads can be greatly accelerated by taking advantage of the parallel processing power of hundreds of cores in the new GPU instances. Many industries including oil and gas exploration, graphics rendering and engineering design are using GPU processors to improve the performance of their critical applications. Amazon Cluster GPU Instances extend the options for running HPC workloads in the AWS cloud. Cluster Compute Instances, launched earlier this year, provide the ability to create clusters of instances connected by a low latency, high throughput network. Cluster GPU Instances give customers with HPC workloads an additional option to further customize their high performance clusters in the cloud. For those customers who have applications that can benefit from the parallel computing power of GPUs, Amazon Cluster GPU Instances can often lead to even further efficiency gains over what can be achieved with traditional processors. By leveraging both instance types, HPC customers can tailor their compute cluster to best meet the performance needs of their workloads. For more information on HPC capabilities provided by Amazon EC2, visit aws.amazon.com/ec2/hpc-applications. Amazon Cluster GPU Instances Not SC10 Related
World’s #1 China's National University of Defense Technology's Tianhe-1A supercomputer has taken the top ranking from Oak Ridge National Laboratory's Jaguar supercomputer on the latest Top500 ranking of the world's fastest supercomputers. The Tianhe-1A achieved a performance level of 2.67 petaflopsper second, while Jaguar achieved 1.75 petaflops per second. The Nebulae, another Chinese-built supercomputer, came in third with a performance of 1.27 petaflops per second. "What the Chinese have done is they're exploiting the power of [graphics processing units], which are...awfully close to being uniquely suited to this particular benchmark," says University of Illinois Urbana-Champagne professor Bill Gropp. Tianhe-1A is a Linux computer built from components from Intel and NVIDIA. "What we should be focusing on is not losing our leadership and being able to apply computing to a broad range of science and engineering problems," Gropp says. Overall, China had five supercomputers ranked in the top 100, while 42 of the top 100 computers were U.S. systems.
The Top 10
SC10 Keynote LectureClayton M. Christensen - Harvard Business School
How to Create New Growth in a Risk-Minimizing Environment Disruption is the mechanism by which great companies continue to succeed and new entrants displace the market leaders. Disruptive innovations either create new markets or reshape existing markets by delivering relatively simple, convenient, low cost innovations to a set of customers who are ignored by industry leaders. One of the bedrock principles of Christensen's disruptive innovation theory is that companies innovate faster than customers' lives change. Because of this, most organizations end up producing products that are too good, too expensive, and too inconvenient for many customers. By only pursuing these "sustaining" innovations, companies unwittingly open the door to "disruptive" innovations, be it "low-end disruption" targeting overshot-less-demanding customers or "new-market disruption", targeting non-consumers. 1. Many of today’s markets that appear to have little growth remaining, actually have great growth potential through disruptive innovations that transform complicated, expensive products into simple, affordable ones. 2. Successful innovation seems unpredictable because innovators rely excessively on data, which is only available about the past. They have not been equipped with sound theories that do not allow them to see the future perceptively. This problem has been solved. 3. Understanding the customer is the wrong unit of analysis for successful innovation. Understanding the job that the customer is trying to do is the key. 4. Many innovations that have extraordinary growth potential fail, not because of the product or service itself, but because the company forced it into an inappropriate business model instead of creating a new optimal one. 5. Companies with disruptive products and business models are the ones whose share prices increase faster than the market over sustained periods
SC10 Keynote Speaker
High-End Computing and Climate Modeling: Future Trends and Prospects SESSION: Big Science, Big Data II Presenter(s):Phillip Colella ABSTRACT:Over the past few years, there has been considerable discussion of the change in high-end computing, due to the change in the way increased processor performance will be obtained: heterogeneous processors with more cores per chip, deeper and more complex memory and communications hierarchies, and fewer bytes per flop. At the same time, the aggregate floating-point performance at the high end will continue to increase, to the point that we can expect exascale machines by the end of the decade. In this talk, we will discuss some of the consequences of these trends for scientific applications from a mathematical algorithm and software standpoint. We will use the specific example of climate modeling as a focus, based on discussions that have been going on in that community for the past two years. Chair/Presenter Details: Patricia Kovatch (Chair) - University of Tennessee, Knoxville Phillip Colella - Lawrence Berkeley National Laboratory
Prediction of Earthquake Ground Motions Using Large-Scale Numerical Simulations SESSION: Big Science, Big Data II Presenter(s):Tom Jordan ABSTRACT:Realistic earthquake simulations can now predict strong ground motions from the largest anticipated fault ruptures. Olsen et al. (this meeting) have simulated a M8 “wall-to-wall” earthquake on southern San Andreas fault up to 2-Hz, sustaining 220 teraflops for 24 hours on 223K cores of NCCS Jaguar. Large simulation ensembles (~10^6) have been combined with probabilistic rupture forecasts to create CyberShake, a physics-based hazard model for Southern California. In the highly-populated sedimentary basins, CyberShake predicts long-period shaking intensities substantially higher than empirical models, primarily due to the strong coupling between rupture directivity and basin excitation. Simulations are improving operational earthquake forecasting, which provides short-term earthquake probabilities using seismic triggering models, and earthquake early warning, which attempts to predict imminent shaking during an event. These applications offer new and urgent computational challenges, including requirements for robust, on-demand supercomputing and rapid access to very large data sets.
Exascale Computing Will (Won't) Be Used by Scientists by the End of This Decade EVENT TYPE: Panel Panelists:Marc Snir, William Gropp, Peter Kogge, Burton Smith, Horst Simon, Bob Lucas, Allan Snavely, Steve Wallach ABSTRACT:DOE has set a goal of Exascale performance by 2018. While not impossible, this will require radical innovations. A contrarian view may hold that technical obstacles, cost, limited need, and inadequate policies will delay exascale well beyond 2018. The magnitude of the required investments will lead to a public discussion for which we need to be well prepared. We propose to have a public debate on the proposition "Exascale computing will be used by the end of the decade", with one team arguing in favor and another team arguing against. The arguments should consider technical and non-technical obstacles and use cases. The proposed format is: (a) introductory statements by each team (b) Q&A's where each team can put questions to other team (c) Q&A's from the public to either teams. We shall push to have a lively debate that is not only informative, but also entertaining.
GPU Computing: To ExaScale and Beyond Bill Dally - NVIDIA/Stanford University
Dedicated High-End Computing to Revolutionize Climate Modeling: An International Collaboration ABSTRACT:A collaboration of six institutions on three continents is investigating the use of dedicated HPC resources for global climate modeling. Two types of experiments were run using the entire 18,048-core Cray XT-4 at NICS from October 2009 to March 2010: (1) an experimental version of the ECMWF Integrated Forecast System, run at several resolutions down to 10 km grid spacing to evaluate high-impact and extreme events; and (2) the NICAM global atmospheric model from JAMSTEC, run at 7 km grid resolution to simulate the boreal summer climate, over many years. The numerical experiments sought to determine whether increasing weather and climate model resolution to accurately resolve mesoscale phenomena in the atmosphere can improve the model fidelity in simulating the mean climate and the distribution of variances and covariances. Chair/Presenter Details: Robert Jacob (Chair) - Argonne National Laboratory James Kinter - Institute of Global Environment and Society
Using GPUs for Weather and Climate Models Presenter(s):Mark Govett ABSTRACT:With the power, cooling, space, and performance restrictions facing large CPU-based systems, graphics processing units (GPUs) appear poised to become the next-generation super-computers. GPU-based systems already are two of the top ten fastest supercomputers on the Top500 list, with the potential to dominate this list in the future. While the hardware is highly scalable, achieving good parallel performance can be challenging. Language translation, code conversion and adaption, and performance optimization will be required. This presentation will survey existing efforts to use GPUs for weather and climate applications. Two general parallelization approaches will be discussed. The most common approach is to run select routines on the GPU but requires data transfers between CPU and GPU. Another approach is to run everything on the GPU and avoid the data transfers, but this can require significant effort to parallelize and optimize the code.
Global Arrays Global Arrays Roadmap and Future Developments SESSION: Global Arrays: Past, Present & Future EVENT TYPE: Special and Invited Events SESSION CHAIR: Moe Khaleel Speaker(s):Daniel Chavarria ABSTRACT:This talk will describe the current state of the Global Arrays toolkit and its underlying ARMCI communication layer and how we believe they should evolve over the next few years. The research and development agenda is targeting expected architectural features and configurations on emerging extreme-scale and exascale systems. Speaker Details: Moe Khaleel (Chair) - Pacific Northwest National Laboratory Daniel Chavarria - Pacific Northwest National Laboratory
Enabling High Performance Cloud Computing Environments SESSION LEADER(S):Jurrie Van Den Breekel ABSTRACT:The cloud is the new “killer” service to bring service providers and enterprises into the age of network services capable of infinite scale. As an example, 5,000 servers with many cloud services could feasibly serve one billion users or end devices. The idea of services at this scale is now possible with multi-core processing, virtualization and high speed Ethernet, but even today the mix of implementing these technologies requires careful considerations in public and private infrastructure design. While cloud computing offers tremendous possibilities, it is critical to understanding the limitations of this framework across key network attributes such as performance, security, availability and scalability. Real-world testing of a cloud computing environment is a key step toward putting any concerns to rest around performance, security and availability. Spirent will share key findings that are the result of some recent work with the European Advanced Networking Test Center (EANTC) including a close examination of how implementing a cloud approach within a private or private data center affects the firewall, data center bridging, virtualization, and WAN optimization. Session Leader Details: Jurrie Van Den Breekel (Primary Session Leader) - Spirent Communications
Cont’ Speakers: NEOVISE – Paul Burns SPIRENT – Jurrie van den Breekel BROCADE – Steve Smith Paul: Single application – single server Single application – multiple servers (cluster computing) Multiple applications – single sever – virtualization Multiple applications – multiple servers – Cloud computing 3rd dimension : tenants, T1 T2 on the same physical server - security
Elastic Cloud Caches for Accelerating Service-Oriented Computations SESSION CHAIR: David Abramson AUTHOR(S):David Chiu, Gagan Agrawal, Apeksha Shetty ABSTRACT:Computing as a utility, that is, on-demand access to computing and storage infrastructure, has emerged as the Cloud. In this model of computing, elastic resource allocation, i.e., the ability to scale resources, should be optimized to manage cost versus performance. Meanwhile, the wake of the information sharing/mining age is invoking a pervasive sharing of Web services and data sets in the Cloud, and at the same time, many data-intensive scientific applications are being expressed as these services. In this paper, we explore an approach to accelerate service processing in the Cloud. We have developed a cooperative scheme for caching data output from services for reuse. We propose algorithms for scaling our cache system up during peak querying times, and back down to save costs. Using the Amazon EC2 Cloud, a detailed evaluation of our system has been performed, considering speed up and elastic scalability in terms resource allocation and relaxation.
Data Sharing Options for Scientific Workflows on Amazon EC2 SESSION: HPC on Clouds TIME: 2:00PM - 2:30PM SESSION CHAIR: David Abramson AUTHOR(S):Gideon Juve, Ewa Deelman, Karan Vahi, Gaurang Mehta, Benjamin P. Berman, Bruce Berriman, Phil Maechling ABSTRACT:Efficient data management is a key component in achieving good performance for scientific workflows in distributed environments. Workflow applications typically communicate data between tasks using files. When tasks are distributed, these files are either transferred from one computational node to another, or accessed through a shared storage system. In grids and clusters, workflow data is often stored on network and parallel file systems. In this paper we investigate some of the ways in which data can be managed for workflows in the cloud. We ran experiments using three typical workflow applications on Amazon’s EC2. We discuss the various storage and file systems we used, describe the issues and problems we encountered deploying them on EC2, and analyze the resulting performance and cost of the workflows.
Friday Panels (19-11-2010)
Future Supercomputing Centers Thom Dunning, William Gropp, Thomas Lippert, Satoshi Matsuoka, Thomas Zacharia This panel will discuss the nature of federal- and state-supported supercomputing centers, what is required to sustain them in the future, and how they will cope with the evolution of computing technology. Since the federally supported centers were created in the mid-1980s, they have fueled innovation and discovery, increasing the number of computational researchers, stimulating the use of HPC in industry, and pioneering new technologies. The future of supercomputing is exciting—sustained petascale systems are here with planning for exascale systems now underway—but it also challenging— disruptive technology changes will be needed to reach the exascale. How can supercomputing help ensure that today’s petascale supercomputer are effectively used to advance science and engineering and how can they help the research and industrial communities prepare for an exciting, if uncertain future?
Advanced HPC Execution Models: Innovation or Disruption Panelists:Thomas L. Sterling, William Carlson, GuangGao, William Gropp, VivekSarkar, Thomas Sterling, Kathy Yelick ABSTRACT:An execution model is the underlying conceptual foundation that integrates the HPC system architecture, programming methods, and intervening Operating System and runtime system software. It is a set of governing principles that govern the co-design, operation, and interoperability of the system layers to achieve most efficient scalable computing in terms of time and energy. Historically, HPC has been driven by five previous epochs of execution models including the most recent CSP that has been exemplified by "Pax MPI" for almost two decades. HPC is now confronted by a severe barrier of parallelism, power, clock rate, and complexity exemplified by multicore and GPU heterogeneity impeding progress between today's Petascale and the end of the decade's Exascale performance. The panel will address the key questions of requirements, form, impact, and programming of such future execution models should they emerge from research in academia, industry, and government centers.