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P573Scientific ComputingLecture 1: Introduction : 01/19/2005 CS267-Lecture 1 1 P573Scientific ComputingLecture 1: Introduction Peter Gottschling pgottsch@cs.indiana.edu www.osl.iu.edu/~pgottsch/courses/p573-06 Based on slides from UC Berkeley www.cs.berkeley.edu/~demmel/cs267_Spr05 Outline : 01/04/2006 P573-Lecture 1 2 Outline Introduction Large important problems require powerful computers Why powerful computers must be parallel processors Why writing (fast) parallel programs is hard Structure of the course Course Organization : 01/19/2005 CS267-Lecture 1 3 Course Organization Who is in the class? : 01/04/2006 P573-Lecture 1 4 Who is in the class? This class is listed as a CS class Normally a mix of CS and other engineering and science students This class seems to be about: 74% Computer Science 8% Mathematics 6% Informatics 3% Art and Science 3% Astronomy 3% Chemistry 3% Physics We encourage interdisciplinary teams This is the way scientific software is generally built Rough Schedule of Topics : 01/04/2006 P573-Lecture 1 5 Rough Schedule of Topics Introduction Technologies Mathematica when focused on math C++ to write fast programs Performance Tools PETSc Algorithms Dense Linear Algebra Particle methods Partial Differential Equations (PDEs) Sparse matrices Graph algorithms Home works In groups of 3 students To check in under subversion Final exam Personal engagement in home work will pay off here What you should get out of the course : 01/04/2006 P573-Lecture 1 6 What you should get out of the course In depth understanding of: How to solve scientific problems with numerical programs How this programs run fast By understanding basics of hardware features Basic understanding of computer accuracy Overview of tools. Some important scientific applications and the algorithms Performance analysis and tuning Why we need powerful computers : 01/19/2005 CS267-Lecture 1 7 Why we need powerful computers Units of Measure in HPC : 01/04/2006 P573-Lecture 1 8 Units of Measure in HPC High Performance Computing (HPC) units are: Flops: floating point operations Flops/s: floating point operations per second Bytes: size of data (a double precision floating point number is 8) Typical sizes are millions, billions, trillions… Mega Mflop/s = 106 flop/sec Mbyte = 220 = 1048576 ~ 106 bytes Giga Gflop/s = 109 flop/sec Gbyte = 230 ~ 109 bytes Tera Tflop/s = 1012 flop/sec Tbyte = 240 ~ 1012 bytes Peta Pflop/s = 1015 flop/sec Pbyte = 250 ~ 1015 bytes Exa Eflop/s = 1018 flop/sec Ebyte = 260 ~ 1018 bytes Zetta Zflop/s = 1021 flop/sec Zbyte = 270 ~ 1021 bytes Yotta Yflop/s = 1024 flop/sec Ybyte = 280 ~ 1024 bytes Simulation: The Third Pillar of Science : 01/04/2006 P573-Lecture 1 9 Simulation: The Third Pillar of Science Traditional scientific and engineering paradigm: Do theory or paper design. Perform experiments or build system. Limitations: Too difficult -- build large wind tunnels. Too expensive -- build a throw-away passenger jet. Too slow -- wait for climate or galactic evolution. Too dangerous -- weapons, drug design, climate experimentation. Computational science paradigm: Use high performance computer systems to simulate the phenomenon Base on known physical laws and efficient numerical methods. Some Particularly Challenging Computations : 01/04/2006 P573-Lecture 1 10 Some Particularly Challenging Computations Science Global climate modeling Biology: genomics; protein folding; drug design Astrophysical modeling Computational Chemistry Computational Material Sciences and Nanosciences Engineering Semiconductor design Earthquake and structural modeling Computation fluid dynamics (airplane design) Combustion (engine design) Crash simulation Business Financial and economic modeling Transaction processing, web services and search engines Defense Nuclear weapons -- test by simulations Cryptography Economic Impact of HPC : 01/04/2006 P573-Lecture 1 11 Economic Impact of HPC Airlines: System-wide logistics optimization systems on parallel systems. Savings: approx. $100 million per airline per year. Automotive design: Major automotive companies use large systems (500+ CPUs) for: CAD-CAM, crash testing, structural integrity and aerodynamics. One company has 500+ CPU parallel system. Savings: approx. $1 billion per company per year. Semiconductor industry: Semiconductor firms use large systems (500+ CPUs) for device electronics simulation and logic validation Savings: approx. $1 billion per company per year. Securities industry: Savings: approx. $15 billion per year for U.S. home mortgages. $5B World Market in Technical Computing : 01/04/2006 P573-Lecture 1 12 $5B World Market in Technical Computing Source: IDC 2004, from NRC Future of Supercomputer Report Global Climate Modeling Problem : 01/04/2006 P573-Lecture 1 13 Global Climate Modeling Problem Problem is to compute: f(latitude, longitude, elevation, time) ? temperature, pressure, humidity, wind velocity Approach: Discretize the domain, e.g., a measurement point every 10 km Devise an algorithm to predict weather at time t+dt given t Uses: Predict major events, e.g., El Nino Use in setting air emissions standards Source: http://www.epm.ornl.gov/chammp/chammp.html Global Climate Modeling Computation : 01/04/2006 P573-Lecture 1 14 Global Climate Modeling Computation One piece is modeling the fluid flow in the atmosphere Solve Navier-Stokes equations Roughly 100 Flops per grid point with 1 minute timestep Computational requirements: To match real-time, need 5 x 1011 flops in 60 seconds = 8 Gflop/s Weather prediction (7 days in 24 hours) ? 56 Gflop/s Climate prediction (50 years in 30 days) ? 4.8 Tflop/s To use in policy negotiations (50 years in 12 hours) ? 288 Tflop/s To double the grid resolution, computation is 8x to 16x State of the art models require integration of atmosphere, ocean, sea-ice, land models, plus possibly carbon cycle, geochemistry and more Current models are coarser than this Slide 15: High Resolution Climate Modeling on NERSC-3 – P. Duffy, et al., LLNL Slide 16: 01/04/2006 P573-Lecture 1 16 Climate Modeling on the Earth Simulator System Development of ES started in 1997 in order to make a comprehensive understanding of global environmental changes such as global warming. 26.58Tflops was obtained by a global atmospheric circulation code. 35.86Tflops (87.5% of the peak performance) is achieved in the Linpack benchmark. Its construction was completed at the end of February, 2002 and the practical operation started from March 1, 2002 Astrophysics: Binary Black Hole Dynamics : 01/04/2006 P573-Lecture 1 17 Astrophysics: Binary Black Hole Dynamics Massive supernova cores collapse to black holes. At black hole center spacetime breaks down. Critical test of theories of gravity – General Relativity to Quantum Gravity. Indirect observation – most galaxieshave a black hole at their center. Gravity waves show black hole directly including detailed parameters. Binary black holes most powerful sources of gravity waves. Simulation extraordinarily complex –evolution disrupts the space-time ! Heart Simulation : 01/04/2006 P573-Lecture 1 18 Heart Simulation Problem is to compute blood flow in the heart Approach: Modeled as an elastic structure in an incompressible fluid. The “immersed boundary method” due to Peskin and McQueen. 20 years of development in model Many applications other than the heart: blood clotting, inner ear, paper making, embryo growth, and others Use a regularly spaced mesh (set of points) for evaluating the fluid Uses Current model can be used to design artificial heart valves Can help in understand effects of disease (leaky valves) Related projects look at the behavior of the heart during a heart attack Ultimately: real-time clinical work Heart Simulation Calculation : 01/04/2006 P573-Lecture 1 19 Heart Simulation Calculation The involves solving Navier-Stokes equations 64^3 was possible on Cray YMP, but 128^3 required for accurate model (would have taken 3 years). Done on a Cray C90 -- 100x faster and 100x more memory Until recently, limited to vector machines Needs more features: Electrical model of the heart, and details of muscles, E.g., Chris Johnson Andrew McCulloch Lungs, circulatory systems Parallel Computing in Data Analysis : 01/04/2006 P573-Lecture 1 20 Parallel Computing in Data Analysis Finding information amidst large quantities of data General themes of sifting through large, unstructured data sets: Has there been an outbreak of some medical condition in a community? Which doctors are most likely involved in fraudulent charging to medicare? When should white socks go on sale? What advertisements should be sent to you? Data collected and stored at enormous speeds (Gbyte/hour) remote sensor on a satellite telescope scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data NSA analysis of telecommunications Why powerful computers are parallel : 01/19/2005 CS267-Lecture 1 21 Why powerful computers are parallel Tunnel Vision by Experts : 01/04/2006 P573-Lecture 1 22 Tunnel Vision by Experts “I think there is a world market for maybe five computers.” Thomas Watson, chairman of IBM, 1943. “There is no reason for any individual to have a computer in their home” Ken Olson, president and founder of Digital Equipment Corporation, 1977. “640K [of memory] ought to be enough for anybody.” Bill Gates, chairman of Microsoft,1981. Slide source: Warfield et al. Technology Trends: Microprocessor Capacity : 01/04/2006 P573-Lecture 1 23 Technology Trends: Microprocessor Capacity 2X transistors/Chip Every 1.5 years Called “Moore’s Law” Moore’s Law Microprocessors have become smaller, denser, and more powerful. Gordon Moore (co-founder of Intel) predicted in 1965 that the transistor density of semiconductor chips would double roughly every 18 months. Slide source: Jack Dongarra Impact of Device Shrinkage : 01/04/2006 P573-Lecture 1 24 Impact of Device Shrinkage What happens when the feature size (transistor size) shrinks by a factor of x ? Clock rate goes up by x because wires are shorter actually less than x, because of power consumption Transistors per unit area goes up by x2 Die size also tends to increase typically another factor of ~x Raw computing power of the chip goes up by ~ x4 ! of which x3 is devoted either to parallelism or locality Microprocessor Transistors per Chip : 01/04/2006 P573-Lecture 1 25 Microprocessor Transistors per Chip Growth in transistors per chip Increase in clock rate But there are limiting forces: Increased cost and difficulty of manufacturing : 01/19/2005 CS267-Lecture 1 26 But there are limiting forces: Increased cost and difficulty of manufacturing Moore’s 2nd law (Rock’s law) Demo of 0.06 micron CMOS More Limits: How fast can a serial computer be? : 01/04/2006 P573-Lecture 1 27 More Limits: How fast can a serial computer be? Consider the 1 Tflop/s sequential machine: Data must travel some distance, r, to get from memory to CPU. To get 1 data element per cycle, this means 1012 times per second at the speed of light, c = 3x108 m/s. Thus r < c/1012 = 0.3 mm. Now put 1 Tbyte of storage in a 0.3 mm x 0.3 mm area: Each word occupies about 3 square Angstroms (10-20m2), or the size of a small atom. No choice but parallelism r = 0.3 mm 1 Tflop/s, 1 Tbyte sequential machine Performance on Linpack Benchmark : 01/04/2006 P573-Lecture 1 28 Performance on Linpack Benchmark Nov 2004: IBM Blue Gene L, 70.7 Tflops Rmax Gflops www.top500.org Why writing (fast) parallel programs is hard?And why we limit ourselves to sequential programming in this course. ;-) : 01/19/2005 CS267-Lecture 1 29 Why writing (fast) parallel programs is hard?And why we limit ourselves to sequential programming in this course. ;-) Principles of Parallel Computing : 01/04/2006 P573-Lecture 1 30 Principles of Parallel Computing Finding enough parallelism (Amdahl’s Law) Granularity Locality Load balance Coordination and synchronization Performance modeling All of these things makes parallel programming even harder than sequential programming. “Automatic” Parallelism in Modern Machines : 01/04/2006 P573-Lecture 1 31 “Automatic” Parallelism in Modern Machines Bit level parallelism within floating point operations, etc. Instruction level parallelism (ILP) multiple instructions execute per clock cycle Memory system parallelism overlap of memory operations with computation OS parallelism multiple jobs run in parallel on commodity SMPs Limits to all of these -- for very high performance, need user to identify, schedule and coordinate parallel tasks Finding Enough Parallelism : 01/04/2006 P573-Lecture 1 32 Finding Enough Parallelism Suppose only part of an application seems parallel Amdahl’s law let s be the fraction of work done sequentially, so (1-s) is fraction parallelizable P = number of processors Speedup(P) = Time(1)/Time(P) <= 1/(s + (1-s)/P) <= 1/s Even if the parallel part speeds up perfectly may be limited by the sequential part Amdahl’s Law and how it really was … : 01/04/2006 P573-Lecture 1 33 Amdahl’s Law and how it really was … It is true that Amdahl pointed out this bottle-neck In 1967 He was director of Advanced Computing Systems Lab of IBM His observation was that programs spend 40 % of time on OS tasks OS are hard to parallelize There are several projects, anyway Our observation today is most parallel programs are dominated by computation OS tasks are usually less important in these applications Hard-to-parallelize problems are often avoided Only crazy people would for instance parallelize graph algorithms File in and output can be a serious bottle-neck Parallel I/O helps a lot with this Data-intensive computing emphasizes on these issues Overhead of Parallelism : 01/04/2006 P573-Lecture 1 34 Overhead of Parallelism Given enough parallel work, this is the biggest barrier to getting desired speedup Parallelism overheads include: cost of starting a thread or process cost of communicating shared data cost of synchronizing extra (redundant) computation Each of these can be in the range of milliseconds (=millions of flops) on some systems Tradeoff: Algorithm needs sufficiently large units of work to run fast in parallel (I.e. large granularity), but not so large that there is not enough parallel work Locality and Parallelism : 01/04/2006 P573-Lecture 1 Locality and Parallelism Large memories are slow, fast memories are small Storage hierarchies are large and fast on average Parallel processors, collectively, have large, fast $ the slow accesses to “remote” data we call “communication” Algorithm should do most work on local data Proc Cache L2 Cache L3 Cache Memory Conventional Storage Hierarchy Proc Cache L2 Cache L3 Cache Memory Proc Cache L2 Cache L3 Cache Memory potential interconnects Processor-DRAM Gap (latency) : 01/04/2006 P573-Lecture 1 36 Processor-DRAM Gap (latency) µProc 60%/yr. DRAM 7%/yr. 1 10 100 1000 1980 1981 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 DRAM CPU 1982 Processor-Memory Performance Gap:(grows 50% / year) Performance Time “Moore’s Law” Load Imbalance : 01/04/2006 P573-Lecture 1 37 Load Imbalance Load imbalance is the time that some processors in the system are idle due to insufficient parallelism (during that phase) unequal size tasks Examples of the latter adapting to “interesting parts of a domain” tree-structured computations fundamentally unstructured problems Algorithm needs to balance load MeasuringPerformance : 01/19/2005 CS267-Lecture 1 38 MeasuringPerformance Improving Real Performance : 01/04/2006 P573-Lecture 1 39 Improving Real Performance 0.1 1 10 100 1,000 2000 2004 Teraflops 1996 Peak Performance grows exponentially, à la Moore’s Law In 1990’s, peak performance increased 100x; in 2000’s, it will increase 1000x But efficiency (the performance relative to the hardware peak) has declined was 40-50% on the vector supercomputers of 1990s now as little as 5-10% on parallel supercomputers of today Close the gap through ... Mathematical methods and algorithms that achieve high performance on a single processor and scale to thousands of processors More efficient programming models and tools for massively parallel supercomputers Performance Gap Peak Performance Real Performance Performance Levels : 01/04/2006 P573-Lecture 1 40 Performance Levels Peak advertised performance (PAP) You can’t possibly compute faster than this speed Speed that computer companies guarantee not to exceed. Speed of computer without programs and data LINPACK The “hello world” program for parallel computing Solve Ax=b using Gaussian Elimination, highly tuned Gordon Bell Prize winning applications performance The right application/algorithm/platform combination plus years of work Average sustained applications performance What one reasonable can expect for standard applications When reporting performance results, these levels are often confused, even in reviewed publications Performance on Linpack Benchmark : 01/04/2006 P573-Lecture 1 41 Performance on Linpack Benchmark Nov 2004: IBM Blue Gene L, 70.7 Tflops Rmax Gflops www.top500.org Performance Levels (for example on NERSC-3) : 01/04/2006 P573-Lecture 1 42 Performance Levels (for example on NERSC-3) Peak advertised performance (PAP): 5 Tflop/s LINPACK (TPP): 3.05 Tflop/s Gordon Bell Prize winning applications performance : 2.46 Tflop/s Material Science application at SC01 Average sustained applications performance: ~0.4 Tflop/s Less than 10% peak! What Characterizes a Good SC Application? : 01/19/2005 CS267-Lecture 1 43 What Characterizes a Good SC Application? What require fast SC applications? : 01/04/2006 P573-Lecture 1 44 What require fast SC applications? An efficient algorithm A performing implementation A scaling parallelization (if parallel computers used) Which is the most important? Example: Computing p by integration : 01/04/2006 P573-Lecture 1 45 Example: Computing p by integration Consider upper right quarter of a unit circle Which has area p/4 Y is given by trigonometry y(x) = (1-x2)1/2 p = 0 ?1 4(1-x2)1/2 dx y x Example: Computing p by integration : 01/04/2006 P573-Lecture 1 46 Example: Computing p by integration Another way arctan(1) = p/4, arctan(0) = 0 arctan’(x) = 1 / (1+x2) Thus p = 4 (arctan(1) - arctan(0)) = 0 ?1 4 / (1+x2 ) dx This is a very popular introduction example for parallel computing The intervals in the integral can be computed separately Only communication is to sum the partial results It scales to any number of processors without much lost E.g. for 1 million processors it would take ~1/million of time Let’s program both Source program for p by integration : 01/04/2006 P573-Lecture 1 47 Source program for p by integration #include <iostream> #include <math.h> // Adapted from MPI tutorial int main(int argc, char** argv) { int n, myid, numprocs, i; double PI25DT = 3.141592653589793238462643; double pi, pi2, h, sum, sum2, x; while (1) { if (myid == 0) { printf("Enter the number of intervals: (0 quits) "); scanf("%d",&n); } if (n == 0) break; else { h = 1.0 / (double) n; sum = 0.0; sum2 = 0.0; for (i = 1; i <= n; i ++) { x = h * ((double)i - 0.5); sum += 4.0 / (1.0 + x*x); sum2 += 4.0 * sqrt(1.0 - x*x); } pi = h * sum; pi2 = h * sum2; printf("pi with arctan\' is approximately %.16f, Error is %.16f\n", pi, fabs(pi - PI25DT)); printf("pi with circle is approximately %.16f, Error is %.16f\n", pi2, fabs(pi2 - PI25DT)); } } return 0; } Results : 01/04/2006 P573-Lecture 1 48 Results > pi Enter the number of intervals: (0 quits) 10 pi with arctan' is approximately 3.1424259850010987, Error is 0.0008333314113056 pi with circle is approximately 3.1524114332616446, Error is 0.0108187796718515 Enter the number of intervals: (0 quits) 100 pi with arctan' is approximately 3.1416009869231254, Error is 0.0000083333333323 pi with circle is approximately 3.1419368579000082, Error is 0.0003442043102151 Enter the number of intervals: (0 quits) 200 pi with arctan' is approximately 3.1415947369231252, Error is 0.0000020833333321 pi with circle is approximately 3.1417143893448611, Error is 0.0001217357550680 Enter the number of intervals: (0 quits) 1000 pi with arctan' is approximately 3.1415927369231227, Error is 0.0000000833333296 pi with circle is approximately 3.1416035449129063, Error is 0.0000108913231132 Enter the number of intervals: (0 quits) 3000 pi with arctan' is approximately 3.1415926628490589, Error is 0.0000000092592658 pi with circle is approximately 3.1415947497204164, Error is 0.0000020961306233 Analysis of the Results : 01/04/2006 P573-Lecture 1 49 Analysis of the Results The arctan integration adds 2 correct digits for increasing the number of intervals by factor of 10 The circle integration is even worse Both converge logarithmically That means the error decreases proportional to the logarithm of the compute effort Inversely we need 10 times more time to get 2 digits Or 10 times more processors Because it parallelizes perfectly Ergo the example is okay as a parallel programming exercise but not for serious research Funny C program : 01/04/2006 P573-Lecture 1 50 Funny C program d,e;main(b){int a=1e4,c=a,f[a];for(;b--;d/=b*2-1)b?d=d*b+(e?f[b]:2)*a,f[b]=d%(b*2-1): printf("%.4d",e+d/a,e=d%a,b=c-=20);} Simple program in 121 characters from Darren Smith Prints accurately 1993 digits of Almost standard conform Doesn’t really computes p but produces the sequence in a tricky way How long would the integration program need for it? With the largest computer in the world? Almost 101000 iterations Universe is only 13.7 billion years = 4.32 1017s old (+/- 6 1015s) The number of atoms is guessed between 1078 and 1081 Quantitative Guesses on Compute Time : 01/04/2006 P573-Lecture 1 51 Quantitative Guesses on Compute Time Fast implementation can accelerate up to 1 order of magnitude Occasionally even more Some techniques are very hardware dependent Other techniques enlarge the programs dramatically (esp. with old languages) Parallelization can accelerate up to 4-5 o.o.m. If one can afford such large systems Also requires more programming efforts Algorithmic modifications can even change the complexity Thus, the ratio can be arbitrary Example on p computation was an extreme case Occasionally slightly slower algorithms are useful if their better implementability compensates the extra operations In general, fast algorithms are more important than efficient programs How NOT to do Scientific Computing : 01/04/2006 P573-Lecture 1 52 How NOT to do Scientific Computing Only looking at algorithms Don’t bother if they are implementable on real computers Only looking at performance No matter what you compute as long as you get enough operations per second For instance, using dense matrices instead of sparse can be much faster But if 95% or >99% only zeros are multiplied it is still waste of time (and memory) It really happens and people impress with their performance (not everybody) Looking only at parallel speed-up Sometimes slow algorithms or implementations are used if they have better parallelism Low single-processor performance improves speed-up Resuming compute time : 01/04/2006 P573-Lecture 1 53 Resuming compute time Best combination of algorithm, performance and parallelism is searched Implies compromises on some of these properties Realistic development costs can imply further compromises Accuracy of results may exclude some techniques Even if they are so nicely fast

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