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High-Performance Computing An Applications Perspective

High-Performance Computing An Applications Perspective. sunil.d.sherlekar@intel.com REACH-IIT Kanpur 10 th Oct. 2010. HPC in Science & Technology. “Theory” and “Experiment” have been two traditional pillars of scientific research; “Simulation” has been added as the third pillar

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High-Performance Computing An Applications Perspective

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  1. High-Performance ComputingAn Applications Perspective sunil.d.sherlekar@intel.com REACH-IIT Kanpur 10th Oct. 2010

  2. HPC in Science & Technology • “Theory” and “Experiment” have been two traditional pillars of scientific research; “Simulation” has been added as the third pillar • To do for other engineering what VLSI has achieved • Abstraction layers missing: hence HPC • Non-linearity & quantum mechanics: hence HPC

  3. Some HPC Applications • Fluid dynamics: • Aerospace, Automobile, chemical & power plants • Structural mechanics: • Crash simulation, armour design • Signal processing: • Oil exploration, astronomy, multimedia data mining • Quantum mechanics, MD • Nanotechnology, computational biology

  4. The Computing Crisis:Opportunity Without A Choice! • Processors not getting any faster • Hence proliferation of multicores • Computation & communication: speed mismatch • Processor, server and cluster levels • What do you with so many cores anyway? • There is a potential market: opportunity • Need to program all these cores: challenge • Mainstreaming/Democratisation of HPC?

  5. Abstractions • Important for handling complexity • Limitation of the human mind • Lemmas leading to theorems • The Clocking Abstraction: Synchronous Ckts. • Decoupling of concerns • Need to break them for efficiency • Breaking needs to be creative & selective to avoid chaos • May lead to a new (better) abstraction

  6. Abstraction: Examples • Physics & Chemistry: • QM, Classical MD, Continuum • The Computing Stack: • Polygons, transistors, gates, ALU/Register, ISP • The Communications Stack: • PHY, MAC, ... • Memory Hierarchy: • Cache, Virtual Memory • Management: • Organisation Structure, Hierarchy

  7. The Unravelling of Abstractions • Layout design • Diffraction: rectangles get fuzzy • Circuit design • All transistors are not the same • Logic design • Wire delay dominates switching delay • Processor design • Clocking hits walls: skew and power • Software • Need abstract model of processor, memory, interconnect, ...

  8. Computing:Correctness & Performance • Software abstractions mainly address correctness • True for both sequential & parallel programming • Efficiency issues: optimising compilers • Largely confined to sequential programs • Algorithms people focussed on order-of-magnitude analysis • Makes the Turing machine model suitable • Need exact analysis a la Knuth

  9. Computing:Correctness & Performance • Need a “realistic” abstraction of hardware • Must meet the need of problem abstraction • Processor: need to model the pipeline • MAC throughput different from latency • Memory: need to model access timing • Access pattern dependent: again throughput v/s latency • RAM is not exactly “random” access • Cache transparency a performance problem • Cache coherence is yet another problem

  10. Computing:Correctness & Performance • Need to model interprocessor communication • Between blades on a cluster • Between dies on a blade • Between cores on a die • Physical interconnect delays • Interconnect topologies • Interconnect protocols: retransmissions etc. • Now even on a chip!

  11. The Productivity Challenge • Programmers need to learn the “new” performance-aware models • Programs must match these models • Not Turing Machine equivalents • Not even idealised parallel computing machines • Phase I: • Write parallel programs for idealised parallel machines • Map programs onto real architectures: “machine-dependent” optimisation • Possible to (at least partially) automate mapping • Phase II: Develop algorithms for real models

  12. The Applications Perspective • Ideal programming paradigms are application-domain dependent • Most architecture-mapping problems best solved by exploiting application characteristics • Characterisation by 13 dwarfs (earlier 7 dwarfs) • “Black Magic” must graduate to methodology • Education & training is the key • Methodology should graduate to automation • Libraries • Parallelising compilers: for real machine models

  13. A Made-For-India Opportunity • Dropping hardware costs: potentially exploding market size • Large workforce adept at programming • Challenge: Education for the “new” world of performance-driven parallel computation • A case for a whole program in “Computational Science” • A great opportunity for “IISER-Class” science graduates

  14. Thank You! Questions?

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