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High-Performance Computing An Applications Perspective

High-Performance Computing (HPC) represents a revolutionary shift in scientific research, complementing traditional "Theory" and "Experiment" with "Simulation." This perspective explores the critical role of HPC in various domains, including fluid dynamics, structural mechanics, and quantum mechanics. With the evolution of multicore processors, programming remains a key challenge. Furthermore, the seminar highlights the necessity for robust abstractions and methodologies in software development to enhance productivity. It outlines the immense opportunities for educational growth in computational science and the need for a skilled workforce in this burgeoning field.

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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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