Case Study

High-Performance ComputingEducation and Research

Intel® Developer Tools and Online Courseware Enrich the High-PerformanceComputing Curriculum at Ural Federal UniversityIn 2012, the world saw the first systems built on Intel’s first many-cores commercialproduct, the Intel® Xeon Phi™ coprocessor. Today, Intel® Xeon® processors and thesefirst-generation Intel Xeon Phi coprocessors contribute more floating point opera-tions per second (FLOPS) to the Top500* than any other form of computation.

Tianhe-2*, the world's fastest supercomputer on the Top500 list, uses Intel Xeonprocessors and Intel Xeon Phi coprocessors.

First-generation Intel Xeon Phi coprocessors deliver one TeraFLOPS for single-thread performance. Second-generation Intel Xeon Phi coprocessors deliver a hugeleap in performance—three TeraFLOPS double-precision peak theoretical perform-ance per single socket node.

Imagine driving high-performance computing education and big data research and de-velopment at one of your country’s most prestigious technical universities. You needthe power of parallel computing at this scale. You’ll want your professors and studentsto have access to the most advanced computational potential available—plus the ex-pertise to use those technologies most effectively.

The Challenge That is Dr. Andrey Sozykin’s mission. Sozykin is head of the High Performance Com-puting Chair of the Institute of Mathematics and Computer Sciences (IMCS) at UralFederal Technologies University (UrFU) in Yekaterinburg, Russia. He’s also head ofthe Computer Science Department of the Institute of Mathematics and Mechanics atthe Ural Branch of the Russian Academy of Sciences.

Professor Elena Akimova, Sozukin’s colleague, is a doctor of physical and mathemat-ical sciences, a leading researcher at the Institute of Mathematics and Mechanics ofUral Branch of RAS, and professor of the Numerical Methods and Equations of Math-ematical Physics Chair of the Institute of Radioelectronics and Information Technolo-gies at UrFU.

Their main scientific interests are inverse geophysical problems, numerical methods,parallel algorithms, and multiprocessor computing systems. Their main researchproject is finding the theory and algorithms for solving nonlinear, inverse gravity andmagnetic problems in a multilayer medium using parallel computing systems.

Intel® Parallel Studio Cluster Edition, Intel® Software Development Suite Student EditionHigh-Performance Computing

High-Performance Computing Education and Research 2

“Through the Intel relationship,the university preparesspecialists not only with

broad theoretical knowledge,but also with practical softwaredevelopment skills. Students

are able to use effectivemodern software developmenttools to solve real scientific

problems.”

– Andrey Sozykin, Head of the High-Performance Computing DepartmentChair, Institute of Mathematics and

Computer Sciences, Ural Federal University

UrFU is one of the top-ranked scientificcenters in Russia, carrying out research innatural, technical, and social sciences;humanities; and economics. Since 2008,the university has borne the name ofBoris Yeltsin, the first President of Russia,a 1955 graduate.

UrFU is ranked 551 on the QS WorldUniversity Rankings for 2014/2015 and80th in the BRICS ranking of universitiesin five major emerging nationaleconomies (Brazil, Russia, India, China,and South Africa).

University administrators have their sightsset much higher. In 2013, UrFU becameone of 15 Russian universities chosen toreceive a special subsidy to enhance itsglobal competitiveness and raise its posi-tions on international rankings. In October2013, a roadmap was approved that is de-signed to push UrFU into the top 100 ofworld universities by 2020.

Fueling the Educational and Research Mission from a DistanceElevating UrFU’s status means traininghighly skilled specialists. And that means theUniversity must provide not only generaltheoretical courses, but also special trainingin modern computational technologies.

Sozykin’s High Performance ComputingChair offers graduate-level programs incomputer science with specializations inparallel computing and system softwaredevelopment. The curricula of both tracksincludes courses in high-performancecomputing, supercomputing technolo-gies, big data, numerical methods, dataanalysis, and more.

Students perform practical exercises onthe University’s computational clusterusing a number of development toolsfrom Intel, plus OpenMP*, OpenACC*,CUDA*, Apache Hadoop MapReduce* andother technologies. Students can alsoparticipate in scientific projects carriedout by the department in cooperationwith several institutes of the Ural Branchof the Russian Academy of Science.

“Our students need to know how to effec-tively use software development tools formodern computational architectures, in-cluding multi-core and many-core proces-sors,” said Sozykin. The university’scomputing cluster contains Intel Xeon E5-2620 and Intel Xeon Phi 5110P processors.

How can faculty members and researcherskeep their knowledge current in a fast-changing parallel computing and big dataenvironment? How can they get the consul-tative support they need when they’re a 24-hour drive from Moscow and a 20-hourflight from Intel headquarters in California?

The Solution To achieve academic and research excel-lence, the High Performance ComputingChair takes advantage of two very differ-ent types of resources from Intel: ad-vanced software developer tools andtechnical and instructional support.

Advanced Software Developer Tools

UrFU uses Intel® Parallel Studio XE Clus-ter Edition (formerly called Intel® ClusterStudio XE) for modeling living systemssolutions and geophysical problems, han-dling large graphs, and for other applica-tions. The developers’ toolkit includes:

• C++ and Fortran compilers

• Performance libraries and parallelmodels optimized for fast parallel code

• Performance profiler, threadingdesign/prototyping tools, and mem-ory and thread debugger

• MPI (Message Passing Interface) clus-ter communications library witherror checking and tuning

These capabilities make it easier for pro-fessors and students to design, develop,debug, and tune code that uses parallelprocessing. The result is a boost in appli-cation performance with less effort oncompatible Intel® processors and co-processors.

Training Highly Skilled Specialists

Figure 1. Modeling the drift of scroll waves in an anisotropic model of the cardiac left ventricle

Figure 2. Solving the inverse problem of magnetometry

• Architecture of High PerformanceComputing Systems

Students watch Intel-provided video lec-tures online from anywhere, and thenrun the practical exercises on the UrFUcomputing cluster and discuss whatthey’ve learned from the results and on-line seminars.

The combination of online, practical,and classroom learning provides aricher and more comprehensive educa-tional experience.

High-Performance Computing Education and Research 3

“Intel® software helps to significantly re-duce the computational time of programson our computing cluster, sometimes byseveral times,” said Akimova.

Technical and Instructional Support

“The teachers must have practical experi-ence and deep understanding of techni-cal details of such tools, which is almostimpossible for university professors; theycannot provide such training by them-selves,” said Sozykin. “We rely on helpfrom leading commercial companies andindustry specialists.”

Intel has been instrumental in several ways:

• Intel Russia delivered special train-ing sessions to accelerate profes-sors’ expertise with Intel softwareand hardware.

• Intel and UrFU collaborated to opena joint UrFU-Intel High-PerformanceComputing Competence Center. Intelengineers provide permanent techni-cal support to this center.

In cooperation with the Intuit.ru portal,Intel provides the Intel Academy, a trackof free, online courses about Intel’s soft-ware development tools for mobile appli-cations and parallel computing, toaugment the university curriculum:

• Intel® Parallel ProgrammingProfessional

• Introduction to ApplicationPerformance Optimization UsingIntel® Compilers

• Application PerformanceOptimization Using Intel® Compilers

• Application PerformanceOptimization Using Intel® MathKernel Library

• Introduction to Silk Plus*Programming

In addition, materials from Intel Acad-emy courses are used in UrFU courses:

• Parallel Computing

• Application PerformanceOptimization

About Ural FederalUniversityUral Federal University is a public,government-owned institution thatoffers 350 degree programsthrough 17 institutes to more than50,000 students. The 2011 mergerof Ural Federal University and UralState University—the region’s twooldest universities—significantlyraised the university’s internationalreputation and its position inglobal rankings.

Ural Federal University is the coreof a research cluster comprisingscientific institutes of the UralBranch of the Russian Academy ofSciences, specializing laboratories,and high-tech enterprises.

The university is engaged in manyinternational projects funded bygovernmental and non-govern-mental organizations from Russia,the European Union, and the U.S.

Ural Federal University is a mem-ber of the Shanghai CooperationOrganization (SCO) Network Uni-versity, the Community of Inde-pendent States (CIS) NetworkUniversity, and the Network Uni-versity of the Arctic.

UrFU also uses certification tests on ap-plication performance optimization pro-vided by Intel Academy. Students cangain credentials that will springboardtheir careers.

Results

Faster Computing

Intel® software helps to significantly re-duce the computational time to run pro-grams on UrFU’s computing cluster.Students and researchers can achievemore with less, or run more iterations,ask more what-if questions and, in turn,produce better results.

Academic Quality

“With the help of Intel, we can providequality, university-level instruction inthe most effective use of Intel® tools todevelop scientific applications and opti-mize application performance on modernmulti-core and many-core processors,”said Sozykin. “Students can have morepractical classes in the university, be-cause they have already gained the nec-essary theoretical knowledge from theonline lectures.”

Blending online and classroom learningto take advantage of expertise half aworld away. It’s like implementing theparallel processing, cloud computingmodel for high-performance learning.And it is an important key leading to amore prominent role on the world stagefor UrFU.

ASC 15 Student Supercomputer Challenge

A team of students and masters of theNumerical Methods and Equations ofMathematical Physics Chair, under the su-pervision of Dr. Elena Akimova, won theSilver Cup First Prize and was awardedwith Diplomas-Letters of Honour on theinternational ASC15 Student Supercom-puter Challenge. ASC15 was co-organizedby Asia Supercomputer Community, In-spur Group, and Taiyuan University ofTechnology. The team was one of 16 fi-nalists out of 152 participating teamsfrom 135 universities and six continents.

1 2 3 4 5 6 7 8Number of CPU Cores

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3

2

1

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Figure 3. Average cardiac ventricle simulation time comparison: serial versus parallel

Figure 4. Using Intel® VTune Amplifier to find hotspots in geophysical simulation software

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Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured usingspecific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performancetests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to www.intel.com/perfor-mance.

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Learn More

Ural Federal University:http://urfu.ru/en/

Intel® Software Developer Tools:https://software.intel.com/en-us/intel-sdp-home

Intel® Parallel Studio XE:https://software.intel.com/en-us/intel-parallel-studio-xe