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high-performance computing hardware 373Board(16 cards)Cabinet(32 boards)System(64 cabinets)Card(2 chips)Chip(2 processors)2.8/5.6 Gflops4MB80 Gflops16 GB DDR11.2 Gflops1GBDDR5.7 Tflops512 GB360 Tflops32TBFigure 14.10 The building blocks of Blue Gene (adapted from [Gara 05]).(a)Gigabit Ethernet(b)I/O nodeFast EthernetJTAGFGPA(c)Compute nodesFigure 14.11 (a) A 3-D torus connecting 2 × 2 × 2 memory blocks. (b) The global collectivememory system. (c) The control and GB-Ethernet memory system (adapted from [Gara 05]).−101COPYRIGHT 2008, PRINCET O N UNIVE R S I T Y P R E S SEVALUATION COPY ONLY. NOT FOR USE IN COURSES.ALLpup_06.04 — 2008/2/15 — Page 373
374 chapter 14yet in order to obtain high speed, it is necessary to keep communication local. Fornode-to-node communication a rate of 1.4 Gb/s =1/10 −9 s = 1 ns is obtained.The latency ranges from 100 ns for 1 hop, to 6.4 µs for 64 hops between processors.The collective network in Figure 14.11b is used to communicate with all theprocessors simultaneously, what is known as a broadcast, and does so at 4 b/cycle.Finally, the control and gigabit ethernet network (Figure 14.11c) is used for I/Oto communicate with the switch (the hardware communication center) and withethernet devices. Its total capacity is greater than 1 tb (=10 12 )¯/s.The computing heart of Blue Gene is its integrated circuit and the associatedmemory system (Figure 14.12). This is essentially an entire computer system on achip containing, among other things,• two PowerPC 440s with attached floating-point units (for rapid processingof floating-point numbers); one CPU is for computing, and one is for I/O.• a RISC architecture CPU with seven stages, three pipelines, and 32-b, 64-wayassociative cache lines,• variable memory page size,• embedded dynamic memory controllers,• a gigabit ethernet adapter,• a total of 512 MB/node (32 tB when summed over nodes),• level-1 (L1) cache of 32 kB, L2 cache of 2 kB, and L3 cache of 4 MB.14.14 Unit III. HPC Program OptimizationThe type of optimization often associated with high-performance or numerically intensivecomputing is one in which sections of a program are rewritten and reorganizedin order to increase the program’s speed. The overall value of doing this, especiallyas computers have become so fast and so available, is often a subject of controversybetween computer scientists and computational scientists. Both camps agree thatusing the optimization options of compilers is a good idea. However, computationalscientists tend to run large codes with large amounts of data in order to solvereal-world problems and often believe that you cannot rely on the compiler to doall the optimization, especially when you end up with time on your hands waitingfor the computer to finish executing your program. Here are some entertaining, yetinsightful, views [Har 96] reflecting the conclusions of those who have reflectedupon this issue:More computing sins are committed in the name of efficiency (without necessarilyachieving it) than for any other single reason—including blind stupidity.— W.A. WulfWe should forget about small efficiencies, say about 97% of the time: prematureoptimization is the root of all evil.— Donald Knuth−101COPYRIGHT 2008, PRINCET O N UNIVE R S I T Y P R E S SEVALUATION COPY ONLY. NOT FOR USE IN COURSES.ALLpup_06.04 — 2008/2/15 — Page 374
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−101COPYRIGHT 2008, PRINCET O N U
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Copyright © 2008 by Princeton Univ
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viiicontents2.3 Experimental Error
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2 chapter 1PhysicsCPFigure 1.1 A re
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58 chapter 33.4.1 Gnuplot Input Dat
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138 chapter 6f(x)< f(x) >xFigure 6.
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7Differentiation & SearchingIn this
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Appendix A: Glossaryabsolute value
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glossary 557control character — A
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glossary 559log in (on) — To sign
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glossary 561supercomputer — The c
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installing ptplot & java developer
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industrial-strength data visualizat
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Appendix D: An MPI TutorialIn this
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an mpi tutorial 595• Open a shell
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an mpi tutorial 597of all the compu
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an mpi tutorial 599TABLE D.1Some Co
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an mpi tutorial 601jobs to MPI. 2 A
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an mpi tutorial 603✞> cd $HOME Do
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an mpi tutorial 605✞/ / MPIhello
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an mpi tutorial 607Argument Namemsg
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an mpi tutorial 609D.3.4 Broadcast
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an mpi tutorial 611D.4 Parallel Tun
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an mpi tutorial 613✝}MPI_Recv ( &
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an mpi tutorial 615✞/ / Code list
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an mpi tutorial 619D.6.1 Nonblockin
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an mpi tutorial 621D.9 List of MPI
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Appendix E: Calling LAPACK from CCa
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calling LAPACK from C 625E.2 Compil
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software on the CD 627JavaCodes Con
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software on the CD 629JavaCodes Con
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software on the CD 631Applets Direc
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software on the CD 633Fortran77code
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Appendix G: Compression via DWTwith
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compression via DWT with thresholdi
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compression via DWT with thresholdi
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BIBLIOGRAPHY[Abar 93] Abarbanel, H.
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ibliography 643[Erco] Ercolessi, F.
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ibliography 645[L&F 93] Landau, R.
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ibliography 647[P&W 91] Pinson, L.
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ibliography 649[VdeV 94] van de Vel
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IndexAbstract data structures,70Abs
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index 653drand, 114Driving force, 2
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index 655Libraries, see Subroutines
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index 657structured, 10for virtual
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