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The DEEP Project: Developing a Novel, Exascale-enabling Supercomputing Platform

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In this video from the HPC Advisory Council Switzerland Conference, Norbert Eicker from Jülich Supercomputing Centre presents: The DEEP Project.

The project DEEP will develop a novel, Exascale-enabling supercomputing platform along with the optimisation of a set of grand-challenge applications highly relevant for Europe’s science, industry and society. The DEEP System will realise a Cluster Booster Architecture that will serve as proof-of-concept for a next-generation 100 PFlop/s production system.”

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Interview: EUDAT to Bring Collaborative Data Infrastructure to ISC’13

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With the ISC’13 International Supercomputing Conference coming up in June, the time is right to check in with new voices in the European HPC community. This week, I caught up with Damien Lecarpentier, David Manset, and Adam Carter from EUDAT, an organization with a mission to build a Collaborative Data Infrastructure for the EU.

insideHPC: Who is EUDAT and who do you help?

EUDAT Team: EUDAT is a new pan-European data initiative bringing together a unique consortium of 25 partners, including research communities, national data and HPC centers, technology providers, and funding agencies from 13 countries. EUDAT aims to build a sustainable cross-disciplinary and cross-national data infrastructure providing a set of shared services to access and preserve research data.

The services being designed in EUDAT are thus of interest to a broad range of research communities and researchers that lack robust data infrastructures, or that are simply looking for additional storage and/or computing capacities to better access, use, re-use, and preserve their data. Five large research communities have initially joined the project as partners and are contributing to the design of the infrastructure and its services. These communities come from linguistics (CLARIN), solid earth sciences (EPOS), climate sciences (ENES), environmental sciences (LIFEWATCH), and biological and medical sciences (VPH). Other communities have expressed strong interest in EUDAT, and are being associated to the work by sharing their requirements, providing feedback on the services being designed and in some cases by participating to service pilots. These communities come from a large diversity of fields: environmental sciences (ICOS, EMSO, EURO-VO, ENVRI), biomedical sciences (DIXA, ECRIN, BBMRI, INCF), physical sciences (PANDATA, EISCAT), and social sciences and humanities (DARIAH, CESSDA).

Altogether, EUDAT has established contact with 20 major European research communities which are actively involved in the service design process and the shaping of the future infrastructure.

insideHPC: You are a first-time exhibitor at ISC. What will you be showing in your booth this year?

EUDAT Team: This year, EUDAT will be showcasing on its booth the Collaborative Data Infrastructure (CDI) it is developing to support researchers from all fields of science in (1) temporarily storing and sharing data, (2) long-term archiving and curating scientific data and (3) transporting data to computing centers for complex processing. Visitors at the booth will thus be provided with dissemination materials, goodies and detailed information on how to join EUDAT.

insideHPC: EUDAT aims to help users with a “Collaborative Data Infrastructure.” What do you mean by that?

EUDAT Team: The concept of Collaborative Data Infrastructure (CDI) emerged from the work of the High Level Expert Group on Scientific Data (HLEG) and was presented in the Riding the Wave as a possible collaboration framework whereby centers offering community-specific support services to their users could rely on a set of common data services shared between different research communities. Although research communities from different disciplines have different ambitions and approaches – particularly with respect to data organization and content – they also share many basic service requirements. This commonality makes it possible for EUDAT to establish common data services, designed to support multiple research communities, as part of this CDI.

The benefits associated with creating such a collaborative framework are many and will result in better exploitation of synergies: (1) By providing generic services to existing scientific communities, EUDAT will enable these communities to focus a greater part of their effort and investment on services that are discipline-specific; (2) It will also provide individual researchers, smaller communities, and projects lacking tailored data management solutions with access to sophisticated shared services, thus removing the need for large-scale capital investment in infrastructure development.(3) Lastly, the EUDAT research infrastructure will facilitate interoperability between the existing infrastructures, enable multiple users, projects, disciplines, and regions to share data and support data-intensive research collaborations.

insideHPC: EUDAT will conduct two training courses this year. What skills with students learn and how does one register?

EUDAT Team: EUDAT plans to run several training courses this year including training at community events, technical workshops on the EUDAT infrastructure, and a series of online webinars each on different subjects related to EUDAT’s work. We’ll look at things such as sharing data, moving data, making data more useful through the use of metadata and identifiers, and also consider topics such as data-intensive computation and data-centric workflows. In particular we’ll look at these from the point of view of large-scale data infrastructure and the services that are being put together by the EUDAT project.

Our aim – as far as training in the project is concerned – is to be driven by the needs of the end-user communities. For this reason, we plan to run several community-focused training events over the next two years which we will co–locate with existing conferences and workshops. In general these community courses will be aimed at a fairly broad audience and will cover various aspects of the wide area of data. We are already working to tailor training for those communities who have been involved in EUDAT from the outset (CLARIN, ENES, EPOS, Lifewatch and VPH) but we’re also very keen to work with other communities as they join up with EUDAT.

In addition to the community training courses, we will run cross-community training which will be targeted at data centre managers and people who are involved in managing other people’s data. These will be focused more on the details of interacting with the EUDAT infrastructure, and the technologies used. We’ll be advertising all of these courses on the EUDAT website and there will be links here to allow interested people to sign up when the times and locations for these courses are fixed. The community events will also be advertised through the communities’ normal communication channels.

A good opportunity to catch up on our training activities will be the 2nd EUDAT Conference which will be held in Rome on 28-30 October and during which we plan to hold a full day training session.

insideHPC: You have 25 European partners. Are you looking to expand?

EUDAT Team: EUDAT is interested to engage with additional stakeholders, in particular research communities interested in using and developing the services we are offering, but also with everybody willing to contribute to the development of the CDI. Interested organisations and institutions can already join the Consortium as Observers or Associate Partners which are two efficient ways of following and contributing to the work in progress. As a pan-European initiative, EUDAT must have broad coverage – not only in geographical terms but also in terms of scientific representation – and this will be taken into account for future expansion plans.

insideHPC: ISC brings in scientists and engineers from around the globe. Is this what attracted you to participate in the conference?

EUDAT Team: There are a number of things that make ISC compelling. EUDAT’s booth will this year place the focus on industry. Indeed, our aim at ISC is to organize and support networking activities and thus welcome industry representatives from innovative sectors such as Cloud computing, Big Data and Data Analytics. At the booth, visitors will be given a tour of EUDAT services and facilities, and discussions on public-private partnerships as well as long-term collaborations will be encouraged. Given the fairly significant attendance of industry that ISC witnesses every year, it is a very interesting opportunity for EUDAT’s outreach.

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Tuesday Keynote from GPU Technology Conference

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In this video, Nvidia’s CEO Jen-Hsun Huang kicks off the GTC Conference with a talk on What’s Next in GPU Technology.

Short on time? In this video, we’ve grabbed the HPC section of the keynote for your viewing pleasure.

At insideHPC, we are very pleased to bring you live streaming keynotes from the GPU Technology Conference all this week from San Jose. Tune in right here on Wednesday, March 20 at 11:00am PT for the next keynote from Erez Lieberman Aiden from the Baylor College of Medicine.

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Video: KernelGen — Next-generation Compiler Platform for Accelerating GPUs

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In this video from the HPC Advisory Council Switzerland Conference, Dmitry Mikushin from the University of Lugano presents KernelGen — Next-generation Compiler Platform for Accelerating GPUs. Download the Slides (PDF).

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Video: GPU Computing With Nvidia’s Kepler Architecture

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In this video from the HPC Advisory Council Switzerland Conference, Axel Koehler from Nvidia presents: GPU Computing With Nvidia’s Kepler Architecture.

Download the Slides (PDF). You can also see Kohler’s other talk from last week on Management of Large-scale GPU Clusters.

In related news, be sure to tune in to insideHPC tomorrow for an exclusive, live-streamed keynote from the GPU Technology Conference, starting at 9:00am PT, Tuesday, March 19. Nvidia CEO Jen-Hsun Huang will present What’s Next in GPU Technology.

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Grand Challenges Déjà Vu

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In this special guest feature, Doug Black from The Exascale Report writes that, while the idea of Grand Challenges is not new, the need for powerful computational tools to solve these global issues remains unchanged.

Flash back to 1992. Do you remember the ‘Blue Book’ and the HPCC program? If this is your first exposure to the ‘Grand Challenges’ you may find this quite interesting. On November 7, 2012, senior representatives of the DOE labs sent a letter to Secretary of Energy, Steven Chu to report on a Grand Challenges Workshop on Advanced Computing for Energy Innovation held in late July – early August 2012.

While the workshop recommendations focused on what it called Technical, Structural and Incentive ‘Grand Challenges’, one of its final recommendations was to establish an Advanced Computing for Energy (ACE) program within the Department of Energy. When I read this letter, I had an intense sense of déjà vu – one of those ‘here we go again’ feelings. But in a good way.

For a moment, it felt like 1992 all over again, a year of unusually high energy and high promise in the HPC community. It’s the year we really sank our teeth into the teraFLOPS challenge. It seemed the entire community rallied in support of what the first President Bush’s science advisor, Alan Bromley, labeled the Grand Challenges – referring to high performance computing and communications. Those Grand Challenges were the challenges of science.

It was the beginning of a period of powerful government and private industry collaboration referred to as the HPCC program. I pulled this quote from the program’s overview documentation: The HPCC Program is driven by the recognition that unprecedented computational power and capability is needed to investigate and understand a wide range of scientific and engineering “grand challenge” problems.

The program’s famous “Blue Book” also made this point:

The HPCC Program is the result of several years of effort on the part of senior government, industry, and academic scientists and managers to design a research agenda to extend U.S. leadership in high performance computing and networking technologies.

So, in many ways, nothing has really changed. Again, I mean this in a good way. The 2012 appeal to address the world’s ‘Grand Challenges’ is eerily similar to what we addressed 20 years ago. HPC is an ever widening circle that keeps coming around. Twenty years ago, the Grand Challenges included climate prediction and genome mapping. Today, the great need is energy innovation and saving the environment. Tomorrow, it may be food. This is HPC and that’s how HPC works, tackling as ever the need for funding and the need for urgency to apply extreme computational resources on the greatest scientific challenges of our time.

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Supercomputer Scientists Accelerate Code with Allinea MAP

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Today Allinea Software announced that the company has cracked the “performance profiling pain barrier” with the release of Allinea MAP, a powerful performance-analysis tool easy enough for scientists to diagnose problems in their own code.

Allinea MAP runs without the need to instrument or compile with special options. The program annotates the source code with performance information in colored graphs so users can see any problems at a glance. More importantly, Allinea MAP is a lightweight application that adds little overhead even when scaled up to profile tens of thousands of processes.

I think visual tools like Allinea MAP are the only way forward as we approach the daunting complexity of exascale computing,” says Rich Brueckner, president of the popular insideHPC news blog. “Algorithms that scale at hundreds or thousands of nodes tend to behave very differently at ultra-scale, where one has tens of thousands or even millions of nodes to contend with,” says Brueckner. “How one tackles such a problem requires new approaches and ways of thinking. You are never going to make parallel computing easy. What you can do is give the programmer a way to navigate in an ocean of code.”

Allinea MAP can be combined with Allinea DDT, sharing a single interface, so when Allinea MAP shows where performance bottlenecks are forming, you can flip to the Allinea DDT view and step through the code to find the source of the problem.

A lot of code out there is performing badly because the people who write and run it don’t have tools to rapidly and regularly analyze it. We’ve had HPC experts tell us they have to correct the same basic mistakes time after time,” says O’Connor. “A single optimization found with Allinea MAP can save hundreds of thousands of core hours over the lifetime of the code, delivering results faster and letting scientists focus on their real work instead of fighting the tools.”

In this video from SC12, Mark O’Connor from Allinea demonstrates the company’s new MAP performance profiler tool. Read the Full Story.


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Python for CUDA to Bolster Next Wave of GPU-powered HPC and Big Data Analytics

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Today Nvidia announced that growing ranks of Python users can now take full advantage of GPU acceleration for HPC and Big Data analytics applications by using the CUDA parallel programming model. As a popular, easy-to-use language, Python enables users to write high-level software code that captures their algorithmic ideas without delving deep into programming details. Python’s extensive libraries and advanced features make it ideal for a broad range of HPC science, engineering and big data analytics applications.

Our research group typically prototypes and iterates new ideas and algorithms in Python and then rewrites the algorithm in C or C++ once the algorithm is proven effective,” said Vijay Pande, professor of Chemistry and of Structural Biology and Computer Science at Stanford University. “CUDA support in Python enables us to write performance code while maintaining the productivity offered by Python.”

Support for CUDA parallel programming comes from NumbaPro, a Python compiler in the new Anaconda Accelerate product from Continuum Analytics. This support was made possible by Nvidia’s contribution of the CUDA compiler source code into the core and parallel thread execution backend of LLVM, a widely used open source compiler infrastructure. Read the Full Story.

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Direct MPI from NVIDIA Tesla and Intel Xeon Phi Accelerator Memories on an IB Cluster

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In this video from the HPC Advisory Council Switzerland Conference, Sadaf Alam from the Swiss Supercomputing Center presents: Direct MPI from NVIDIA Tesla and Intel Xeon Phi Accelerator Memories on an InfiniBand Cluster.

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Heterogeneous Computing for Dummies

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Over at the AccelerEyes Blog, John Melonakos writes that the next 10 years of HPC will be defined by heterogeneous computing.

In terms of sheer capacity to crunch numbers, GPUs can crunch more numbers per minute than CPUs. They have thousands of cores for number crunching. They are more powerful. They also use less energy per computation than CPUs. Note that a GPU core is not nearly as capable as a CPU core in terms of the kinds of things they can do, but there are many more of them available.

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Video: SuperMUC – One year of Operation

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In this video from the HPC Advisory Council Switzerland Conference, Klaus Gottschalk from IBM presents: LRZ SuperMUC – One year of Operation.

The SuperMUC has 147,456 cores and a peak performance of about 3 petaflop/s. The main memory will be 288 terabytes together with 12 petabytes hard disk space based on the GPFS file system. The system will use 18,432 Intel Xeon Sandy Bridge-EP processors running in IBM System x iDataPlex servers. It will also use a new form of cooling that IBM developed, called Aquasar, that uses hot water to cool the processors, a design that cuts cooling electricity usage by 40 percent.

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Video: Accelerating Big Data with Hadoop (HDFS, MapReduce and HBase) and Memcached

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In this video from the HPC Advisory Council Switzerland Conference, D.K. Panda from Ohio State University presents: Accelerating Big Data with Hadoop (HDFS, MapReduce and HBase) and Memcached. Download the slides (PDF).

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ClusterVision Fires Up 200 Teraflop Super at University of Paderborn

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Today ClusterVision announced the installation of a 200 Teraflop supercomputer a the University of Paderborn. With 614 compute nodes and 10,000 cores, the hybrid system will run a wide range of commercial and open source HPC applications in technology and science. As a hybrid system, the supercomputer also includes 32 NVIDIA K20 GPUs and 8 Intel Xeon Phi coprocessors, providing an additional 40 Teraflops of compute power.

This system is a powerful compute resource for all researchers in the region of East Westphalia and Lippe, and our partners in Germany and Europe,” Prof.Dr. Holger Karl, head of the PC2 board.

With a system interconnect powered by Mellanox QDR InfiniBand, the Paderborn cluster uses Dell PowerVault MD3200 storage components powered by the FraunhoferFS FhGFS the parallel file- system. Read the Full Story.

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Video: The UberCloud HPC Experiment – Paving the Way to HPC as a Service

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In this video from the HPC Advisory Council Switzerland Conference, Wolfgang Gentzsch presents: The UberCloud HPC Experiment – Paving the Way to HPC as a Service.

You can participate in this experiment as an industrial End-User in need of instant additional computing power accessible remotely, or as a compute Resource Provider, or as a Software Provider, or as an HPC Expert.

Read the Full Story or Download the Slides (PDF).

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Video: Mellanox – The Foundation for Scalable Computing

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In this video from the HPC Advisory Council Switzerland Conference, Colin Bridger presents: Mellanox: The Foundation for Scalable Computing.

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