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1 Introduction: HPC goes mainstream Chokchai Box Leangsuksun Associate Professor, Computer Science Louisiana Tech University

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Presentation on theme: "1 Introduction: HPC goes mainstream Chokchai Box Leangsuksun Associate Professor, Computer Science Louisiana Tech University"— Presentation transcript:

1 1 Introduction: HPC goes mainstream Chokchai Box Leangsuksun Associate Professor, Computer Science Louisiana Tech University

2 3 May Outline Why HPC is critical technology ? Conclusion

3 3 May Why HPC? High Performance Computing – Parallel, Supercomputing –Enabled by multiple high speed CPUs, networking, software etc – fastest possible solution –Technologies that help solving non-trivial tasks including scientific, engineering, medical, business entertainment and etc. Time to insights, Time to discovery, Times to markets BTW, HPC is not GRID!!!.

4 3 May HPC Applications and Major Industries Finite Element Modeling –Auto/Aero Fluid Dynamics –Auto/Aero, Consumer Packaged Goods Mfgs, Process Mfg, Disaster Preparedness (tsunami) Imaging –Seismic & Medical Finance –Banks, Brokerage Houses (Regression Analysis, Risk, Options Pricing, What if, …) Molecular Modeling –Biotech and Pharmaceuticals Complex Problems, Large Datasets, Long Runs This slide is from Intel presentation “Technologies for Delivering Peak Performance on HPC and Grid Applications”

5 3 May Life Science Problem – an example of Protein Folding Take a computing year (in serial mode) to do molecular dynamics simulation for a protein folding problem Excerpted from IBM David Klepacki’s The future of HPC Petaflop = a thousand trillion floating point operations per second

6 3 May Disaster Preparedness - example Project LEAD –Severe Weather prediction (Tornado) – OU leads. HPC & Dynamically adaptation to weather forecast Professor Seidel’s LSU CCT –Hurricane Route Prediction –Emergency Preparedness –Show Movie – HPC-enabled Simulation

7 3 May Did you know that Playstation 3 is a HPC/Supercomputer? 9 cores/CPUs in one chip. Future gaming software is no longer graphic or multimedia only This diagram is from an article from IBM Cell processor & compiler challenge

8 3 May No Free Lunch (mainstream CPUs) CPU speed – plateaus 3-4 Ghz More cores in a single chip –Dual core is now –Multicore is imminent Traditional Applications won’t get a free rides Conversion to parallel computing (HPC, MT) 3-4 Ghz cap This diagram is from “no free lunch article in DDJ

9 3 May Cancer Gene-mining Unsuccessful on a uni-processor Our approach –Novel parallel gene-mining algorithms –Input from microarray –Retain accuracy –Significantly speed up (superlinear) IBM P5 supercomputer (128 node PPC).

10 3 May Significant indicators – why HPC now? No Free lunch in CPU speed up (Intel or AMD) –In past 1-2 years, CPU speed was flatten at 3+ Ghz –More CPUs in one chip – Dual core, multi-core chips –Traditional software won’t take advantage of these new processors –Personal/Desktop Supercomputing. Many real problems are highly computational intensive. –NSA uses supercomputing to do data mining –DOE – fusion, plasma, energy related (including weaponry). –Help solving many other important areas (nanotech, life science etc.) Giants recently sneeze out HPC –Bush’s state of union speech – 3 main S&T focus of which Supercomputing is one of them –Bill Gates’ keynote speech at SC05 – MS goes after HPC Google search engine - 100,000 nodes Playstation 3 is a personal supercomputing platform Hollywood (Entertainment) is HPC-bound (Pixar – more than 3000 CPUs to render animation) “I propose to double the federal commitment to the most critical basic research programs in the physical sciences over the next 10 years. This funding will support the work of America's most creative minds as they explore promising areas such as nanotechnology, supercomputing, and alternative energy sources.” Gorge W. Bush, 2005

11 3 May HPC preparedness Build work forces that understand HPC paradigm & its applications –HPC/Grid Curriculum in IT/CS/CE/ICT –Offer HPC-enabling tracks to other disciplinary (engineering, life science, physic, computational chem, business etc..) –Training business community (e.g. HPC for enterprise ; Fluent certification, HA SLA certification) –Bring awareness to public.

12 3 May Introduction to Parallel computing Need more computing power –Improve the operating speed of processors & other components constrained by the speed of light, thermodynamic laws, & the high financial costs for processor fabrication –Connect multiple processors together & coordinate their computational efforts parallel computers allow the sharing of a computational task among multiple processors

13 3 May How to Run Applications Faster ? There are 3 ways to improve performance: –Work Harder –Work Smarter –Get Help Computer Analogy –Using faster hardware –Optimized algorithms and techniques used to solve computational tasks –Multiple computers to solve a particular task

14 3 May Era of Computing –Rapid technical advances the recent advances in VLSI technology software technology –OS, PL, development methodologies, & tools grand challenge applications have become the main driving force –Parallel computing one of the best ways to overcome the speed bottleneck of a single processor good price/performance ratio of a small cluster-based parallel computer

15 3 May HPC Level-setting Definitions High performance computing is: –Computing that demands more than a single high- market-volume workstation or server can deliver HPC is based on concurrency: –Concurrency: computing in which multiple tasks are active at the same time Parallel computing occurs when you use concurrency to: –Solve bigger problems –Solve a fixed-size problem in less time

16 3 May HPC Level-setting Hardware for Parallel Computing § SIMD has failed as a way to organize large-scale computers with multiple processors. It has succeeded, however, as a mechanism to increase instruction-level parallelism in modern microprocessors (in Intel ® MMX ™ technology). Symmetric Multiprocessor (SMP) Non-uniform Memory Architecture (NUMA) Massively Parallel Processor (MPP) Commodit y Cluster Single Instruction Multiple Data (SIMD) § Multiple Instruction Multiple Data (MIMD) Parallel Computers Shared Address SpaceDisjoint Address Space Distributed Computing

17 3 May Scalable Parallel Computer Architectures MPP –A large parallel processing system with a shared-nothing architecture –Consist of several hundred nodes with a high-speed interconnection network/switch –Each node consists of a main memory & one or more processors Runs a separate copy of the OS SMP –2-64 processors today –Shared-everything architecture –All processors share all the global resources available –Single copy of the OS runs on these systems

18 3 May Scalable Parallel Computer Architectures CC-NUMA –a scalable multiprocessor system having a cache-coherent nonuniform memory access architecture –every processor has a global view of all of the memory Distributed systems –considered conventional networks of independent computers –have multiple system images as each node runs its own OS –the individual machines could be combinations of MPPs, SMPs, clusters, & individual computers Clusters –a collection of workstations of PCs that are interconnected by a high- speed network –work as an integrated collection of resources –have a single system image spanning all its nodes

19 3 May Cluster Computer and its Architecture A cluster is a type of parallel or distributed processing system, which consists of a collection of interconnected stand-alone computers cooperatively working together as a single, integrated computing resource A node –a single or multiprocessor system with memory, I/O facilities, & OS –generally 2 or more computers (nodes) connected together –in a single cabinet, or physically separated & connected via a LAN –appear as a single system to users and applications –provide a cost-effective way to gain features and benefits

20 3 May Cluster Computer Architecture

21 3 May Beowulf Head Node Compute nodes Login Compile Submit job Run tasks

22 3 May Prominent Components of Cluster Computers (I) Multiple High Performance Computers –PCs –Workstations –SMPs (CLUMPS) –Distributed HPC Systems leading to Metacomputing

23 3 May Prominent Components of Cluster Computers (II) State of the art Operating Systems –Linux(Beowulf) –Microsoft NT(Illinois HPVM) –SUN Solaris(Berkeley NOW) –IBM AIX(IBM SP2) –HP UX(Illinois - PANDA) –Mach (Microkernel based OS) (CMU) –Cluster Operating Systems (Solaris MC, SCO Unixware, MOSIX (academic project) –OS gluing layers(Berkeley Glunix)

24 3 May Prominent Components of Cluster Computers (III) High Performance Networks/Switches –Ethernet (10Mbps), Fast Ethernet (100Mbps), –InfiniteBand (1-8 Gbps) –Gigabit Ethernet (1Gbps) –SCI (Dolphin - MPI- 12micro-sec latency) –ATM –Myrinet (1.2Gbps) –Digital Memory Channel –FDDI

25 3 May Prominent Components of Cluster Computers (IV) Network Interface Card –Myrinet has NIC –InfiniteBand (HBA) –User-level access support

26 3 May Prominent Components of Cluster Computers (VI) Cluster Middleware –Single System Image (SSI) –System Availability (SA) Infrastructure Hardware –DEC Memory Channel, DSM (Alewife, DASH), SMP Techniques Operating System Kernel/Gluing Layers – Solaris MC, Unixware, GLUnix Applications and Subsystems – Applications (system management and electronic forms) – Runtime systems (software DSM, PFS etc.) – Resource management and scheduling software (RMS) CODINE, LSF, PBS, NQS, etc.

27 3 May Prominent Components of Cluster Computers (VII) Parallel Programming Environments and Tools –Threads (PCs, SMPs, NOW..) POSIX Threads Java Threads –MPI Linux, NT, on many Supercomputers –PVM –Software DSMs (Shmem) –Compilers C/C++/Java Parallel programming with C++ (MIT Press book) –RAD (rapid application development tools) GUI based tools for PP modeling –Debuggers –Performance Analysis Tools –Visualization Tools

28 3 May Prominent Components of Cluster Computers (VIII) Applications –Sequential –Parallel / Distributed (Cluster-aware app.) Grand Challenging applications –Weather Forecasting –Quantum Chemistry –Molecular Biology Modeling –Engineering Analysis (CAD/CAM) –………………. PDBs, web servers,data-mining

29 3 May Key Operational Benefits of Clustering High Performance Expandability and Scalability High Throughput High Availability

30 3 May Divide and Conquer Says 1 CPU –1,000,000 elements –Numerical processing for 1 element =.1 secs –One computer will take 100,000 secs = 27.7 hrs Says 100 CPUs –.27 hr ~ 16 mins

31 3 May Parallel Computing A big application is divided into Multiple tasks Total computation time –Computing time –Communication time

32 3 May Summary HPC helps accelerates Time to insights, time to discovery and time to Market for challenging problems Divide and Conquer –Computing vs communication time Cluster computing is a predominant HPC system

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