1. 7/17/2009 1 Parallel and High Performance Computing Burton Smith Technical Fellow Microsoft

2. Agenda Introduction Definitions Architecture and Programming Examples Conclusions 7/17/2009 2

3. Introduction 7/17/2009 3

4. “Parallel and High Performance”? “Parallel computing is a form of computation in which many calculations are carried out simultaneously” G.S. Almasi and A. Gottlieb, Highly Parallel Computing. Benjamin/Cummings, 1994 A High Performance (Super) Computer is: One of the 500 fastest computers as measured by HPL: the High Performance Linpack benchmark A computer that costs 200.000.000 руб or more Necessarily parallel, at least since the 1970’s 7/17/2009 4

5. Recent Developments For 20 years, parallel and high performance computing have been the same subject Parallel computing is now mainstream It reaches well beyond HPC into client systems: desktops, laptops, mobile phones HPC software once had to stand alone Now, it can be based on parallel PC software The result: better tools and new possibilities 7/17/2009 5

6. The Emergence of the Parallel Client Uniprocessor performance is leveling off Instruction-level parallelism nears a limit (ILP Wall) Power is getting painfully high (Power Wall) Caches show diminishing returns (Memory Wall) Logic density continues to grow (Moore’s Law) So uniprocessors will collapse in area and cost Cores per chip need to increase exponentially We must all learn to write parallel programs So new “killer apps” will enjoy more speed

7. The ILP Wall Instruction-level parallelism preserves the serial programming model While getting speed from “undercover” parallelism For example, see HPS†: out-of-order issue, in-order retirement, register renaming, branch prediction, speculation, … At best, we get a few instructions/clock † Y.N. Patt et al., "Critical Issues Regarding HPS, a High Performance Microarchitecture,“Proc. 18th Ann. ACM/IEEE Int'l Symp. on Microarchitecture, 1985, pp. 109−116.

8. The Power Wall In the old days, power was kept roughly constant Dynamic power, equal to CV2f, dominated Every shrink of .7 in feature size halved transistor area Capacitance C and voltage V also decreased by .7 Even with the clock frequency f increased by 1.4, power per transistor was cut in half Now, shrinking no longer reduces V very much So even at constant frequency, power density doubles Static (leakage) power is also getting worse Simpler, slower processors are more efficient And to conserve power, we can turn some of them off

9. The Memory Wall We can get bigger caches from more transistors Does this suffice, or is there a problem scaling up? To speed up 2X without changing bandwidth below the cache, the miss rate must be halved How much bigger does the cache have to be?† For dense matrix multiply or dense LU, 4x bigger For sorting or FFTs, the square of its former size For sparse or dense matrix-vector multiply, impossible Deeper interconnects increase miss latency Latency tolerance needs memory access parallelism † H.T. Kung, “Memory requirements for balanced computer architectures,” 13th International Symposium on Computer Architecture, 1986, pp. 49−54.

10. Overcoming the Memory Wall Provide more memory bandwidth Increase DRAM I/O bandwidth per gigabyte Increase microprocessor off-chip bandwidth Use architecture to tolerate memory latency More latency  more threads or longer vectors No change in programming model is needed Use caches for bandwidth as well as latency Let compilers control locality Keep cache lines short Avoid mis-speculation

11. The End of The von Neumann Model “Instructions are executed one at a time…” We have relied on this idea for 60 years Now it (and things it brought) must change Serial programming is easier than parallel programming, at least for the moment But serial programs are now slow programs We need parallel programming paradigms that will make all programmers successful The stakes for our field’s vitality are high Computing must be reinvented

12. Definitions 7/17/2009 12

13. Asymptotic Notation Quantities are often meaningful only within a constant factor Algorithm performance analyses, for example f(n) = O(g(n)) means there exist constants c and n0 such that nn0 implies |f(n)|  |cg(n)| f(n) = (g(n)) means there exist constants c and n0 such that nn0 implies |f(n)|  |cg(n)| f(n) = (g(n)) means both f(n) = O(g(n)) and f(n) = (g(n)) 7/17/2009 13

14. Speedup, Time, and Work The speedup of a computation is how much faster it runs in parallel compared to serially If one processor takes T1 and p of them take Tp then the p-processor speedup is Sp = T1/Tp The work done is the number of operations performed, either serially or in parallel W1 = O(T1) is the serial work, Wp the parallel work We say a parallel computation is work-optimal ifWp = O(W1) = O(T1) We say a parallel computation is time-optimal ifTp = O(W1/p) = O(T1/p) 7/17/2009 14

15. Latency, Bandwidth, & Concurrency In any system that moves items from input to output without creating or destroying them, Queueing theory calls this result Little’s law latency × bandwidth = concurrency concurrency = 6 bandwidth = 2 latency = 3

16. Architecture ANDPROGRAMMING 7/17/2009 16

17. Parallel Processor Architecture SIMD: Each instruction operates concurrently on multiple data items MIMD: Multiple instruction sequences execute concurrently Concurrency is expressible in space or time Spatial: the hardware is replicated Temporal: the hardware is pipelined 7/17/2009 17

18. Trends in Parallel Processors Today’s chips are spatial MIMD at top level To get enough performance, even in PCs Temporal MIMD is also used SIMD is tending back toward spatial Intel’s Larrabee combines all three Temporal concurrency is easily “adjusted” Vector length or number of hardware contexts Temporal concurrency tolerates latency Memory latency in the SIMD case For MIMD, branches and synchronization also 7/17/2009 18

19. Parallel Memory Architecture A shared memory system is one in which any processor can address any memory location Quality of access can be either uniform (UMA) or nonuniform (NUMA), in latency and/or bandwidth A distributed memory system is one in which processors can’t address most of memory The disjoint memory regions and their associated processors are usually called nodes A cluster is a distributed memory system with more than one processor per node Nearly all HPC systems are clusters 7/17/2009 19

20. Parallel Programming Variations Data Parallelism andTask Parallelism Functional Style and Imperative Style Shared Memory and Message Passing …and more we won’t have time to look at A parallel application may use all of them 7/17/2009 20

21. Data Parallelism and Task Parallelism A computation is data parallel when similar independent sub-computations are done simultaneously on multiple data items Applying the same function to every element of a data sequence, for example A computation is task parallel when dissimilar independent sub-computations are done simultaneously Controlling the motions of a robot, for example It sounds like SIMD vs. MIMD, but isn’t quite Some kinds of data parallelism need MIMD 7/17/2009 21

22. Functional and Imperative Programs A program is said to be written in (pure) functional style if it has no mutable state Computing = naming and evaluating expressions Programs with mutable state are usually called imperative because the state changes must be done when and where specified: while (z < x) { x = y; y = z; z = f(x, y);} return y; Often, programs can be written either way: let w(x, y, z) = if (z < x) then w(y, z, f(x, y)) else y; 7/17/2009 22

23. Shared Memory and Message Passing Shared memory programs access data in a shared address space When to access the data is the big issue Subcomputations therefore must synchronize Message passing programs transmit data between subcomputations The sender computes a value and then sends it The receiver recieves a value and then uses it Synchronization can be built in to communication Message passing can be implemented very well on shared memory architectures 7/17/2009 23

24. Barrier Synchronization A barrier synchronizes multiple parallel sub-computations by letting none proceed until all have arrived It is named after the barrier used to start horse races It guarantees everything before the barrier finishes before anything after it begins It is a central feature in several data-parallel languages such as OpenMP 7/17/2009 24

25. Mutual Exclusion This type of synchronization ensures only one subcomputation can do a thing at any time If the thing is a code block, it is a critical section It classically uses a lock: a data structure with which subcomputations can stop and start Basic operations on a lock object L might be Acquire(L): blocks until other subcomputations are finished with L, then acquires exclusive ownership Release(L): yields L and unblocks some Acquire(L) A lot has been written on these subjects 7/17/2009 25

26. Non-Blocking Synchronization The basic idea is to achieve mutual exclusion using memory read-modify-write operations Most commonly used is compare-and-swap: CAS(addr, old, new) reads memory at addr and if it contains old then old is replaced by new Arbitrary update operations at an addr require {read old; compute new; CAS(addr, old, new);}be repeated until the CAS operation succeeds If there is significant updating contention at addr, the repeated computation of new may be wasteful 7/17/2009 26

27. Load Balancing Some processors may be busier than others To balance the workload, subcomputations can be scheduled on processors dynamically A technique for parallel loops is self-scheduling: processors repetitively grab chunks of iterations In guided self-scheduling, the chunk sizes shrink Analogous imbalances can occur in memory Overloaded memory locations are called hot spots Parallel algorithms and data structures must be designed to avoid them Imbalanced messaging is sometimes seen 7/17/2009 27

28. Examples 7/17/2009 28

29. A Data Parallel Example: Sorting 7/17/2009 29 void sort(int *src, int *dst,int size, intnvals) { inti, j, t1[nvals], t2[nvals]; for (j = 0 ; j < nvals ; j++) { t1[j] = 0; } for (i = 0 ; i < size ; i++) { t1[src[i]]++; } //t1[] now contains a histogram of the values t2[0] = 0; for (j = 1 ; j < nvals ; j++) { t2[j] = t2[j-1] + t1[j-1]; } //t2[j] now contains the origin for value j for (i = 0 ; i < size ; i++) {dst[t2[src[i]]++] = src[i]; } }

30. When Is a Loop Parallelizable? The loop instances must safely interleave A way to do this is to only read the data Another way is to isolate data accesses Look at the first loop: The accesses to t1[] are isolated from each other This loop can run in parallel “as is” 7/17/2009 30 for (j = 0 ; j < nvals ; j++) { t1[j] = 0; }

31. Isolating Data Updates The second loop seems to have a problem: Two iterations may access the same t1[src[i]] If both reads precede both increments, oops! A few ways to isolate the iteration conflicts: Use an “isolated update” (lock prefix) instruction Use an array of locks, perhaps as big as t1[] Use non-blocking updates Use a transaction 7/17/2009 31 for (i = 0 ; i < size ; i++) { t1[src[i]]++; }

32. Dependent Loop Iterations The 3rd loop is an interesting challenge: Each iteration depends on the previous one This loop is an example of a prefix computation If • is an associative binary operation on a set S, the • - prefixes of the sequence x0 ,x1 ,x2 … of values from S is x0,x0•x1,x0•x1•x2 … Prefix computations are often known as scans Scan can be done in efficiently in parallel 7/17/2009 32 for (j = 1 ; j < nvals ; j++) { t2[j] = t2[j-1] + t1[j-1]; }

33. Cyclic Reduction Each vertical line represents a loop iteration The associated sequence element is to its right On step k of the scan, iteration j prefixes its own value with the value from iteration j – 2k 7/17/2009 33 a b c d e f g a ab bc cd de ef fg a ab abc abcd bcde cdef defg a ab abc abcd abcde abcdef abcdefg

34. Applications of Scan Linear recurrences like the third loop Polynomial evaluation String comparison High-precision addition Finite automata Each xi is the next-state function given the ith input symbol and • is function composition APL compress When only the final value is needed, the computation is called a reduction instead It’s a little bit cheaper than a full scan

35. More Iterations nThan Processors p 7/17/2009 35 Wp = 3n + O(p log p), Tp = 3n / p + O(log p)

36. OpenMP OpenMP is a widely-implemented extension to C++ and Fortran for data† parallelism It adds directives to serial programs A few of the more important directives: #pragmaomp parallel for <modifiers><for loop> #pragmaomp atomic<binary op=,++ or -- statement> #pragmaomp critical <name><structured block> #pragmaomp barrier 7/17/2009 36 †And perhaps task parallelism soon

37. The Sorting Example in OpenMP Only the third “scan” loop is a problem We can at least do this loop “manually”: 7/17/2009 37 nt = omp_get_num_threads(); intta[nt], tb[nt]; #omp parallel for for(myt = 0; myt < nt; myt++) { //Set ta[myt]= local sum of nvals/nt elements of t1[] #pragmaomp barrier for(k = 1; k <= myt; k *= 2){ tb[myt] = ta[myt]; ta[myt] += tb[myt - k]; #pragmaomp barrier } fix = (myt > 0) ? ta[myt – 1] : 0; //Setnvals/ntelements of t2[] to fix + local scan of t1[] }

38. Parallel Patterns Library (PPL) PPL is a Microsoft C++ library built on top of the ConcRT user-mode scheduling runtime It supports mixed data- and task-parallelism: parallel_for, parallel_for_each, parallel_invoke agent, send, receive, choice, join, task_group Parallel loops use C++ lambda expressions: Updates can be isolated using intrinsic functions Microsoft and Intel plan to unify PPL and TBB 7/17/2009 38 parallel_for(1,nvals,[&t1](int j) { t1[j] = 0; }); (void)_InterlockedIncrement(t1[src[i]]++);

39. Dynamic Resource Management PPL programs are written for an arbitrary number of processors, could be just one Load balancing is mostly done by work stealing There are two kinds of work to steal: Work that is unblocked and waiting for a processor Work that is not yet started and is potentially parallel Work of the latter kind will be done serially unless it is first stolen by another processor This makes recursive divide and conquer easy There is no concern about when to stop parallelism 7/17/2009 39

40. A Quicksort Example void quicksort (vector<int>::iterator first, vector<int>::iterator last) { if (last - first < 2){return;} int pivot = *first; auto mid1 = partition (first, last, [=](int e){return e < pivot;}); auto mid2 = partition (mid1, last, [=](int e){return e == pivot;}); parallel_invoke( [=] { quicksort(first, mid1); }, [=] { quicksort(mid2, last); } ); }; 7/17/2009 40

41. LINQ and PLINQ LINQ (Language Integrated Query) extends the .NET languages C#, Visual Basic, and F# A LINQ query is really just a functional monad It queries databases, XML, or any IEnumerable PLINQ is a parallel implementation of LINQ Non-isolated functions must be avoided Otherwise it is hard to tell the two apart 7/17/2009 41

42. A LINQ Example 7/17/2009 42 PLINQ .AsParallel() var q = from n in names where n.Name == queryInfo.Name && n.State == queryInfo.State && n.Year >= yearStart && n.Year <= yearEnd orderbyn.Year ascending select n;

43. Message Passing Interface (MPI) MPI is a widely used message passing library for distributed memory HPC systems Some of its basic functions: A few of its “collective communication” functions: 7/17/2009 43 MPI_Init MPI_Comm_rank MPI_Comm_size MPI_Send MPI_Recv MPI_Reduce MPI_Allreduce MPI_Scan MPI_Exscan MPI_Barrier MPI_Gather MPI_Allgather MPI_Alltoall

44. Sorting in MPI Roughly, it could work like this on n nodes: Run the first two loops locally Use MPI_Allreduce to build a global histogram Run the third loop (redundantly) at every node Allocate n value intervals to nodes (redundantly) Balancing the data per node as well as possible Run the fourth loop using the local histogram Use MPI_Alltoall to redistribute the data Merge the n sorted subarrays on each node Collective communication is expensive But sorting needs it (see the Memory Wall slide) 7/17/2009 44

45. Another Way to Sort in MPI The Samplesort algorithm is like Quicksort It works like this on n nodes: Sort the local data on each node independently Take s samples of the sorted data on each node Use MPI_Allgather to send all nodes all samples Compute n  1 splitters (redundantly) on all nodes Balancing the data per node as well as possible Use MPI_Alltoall to redistribute the data Merge the n sorted subarrays on each node 7/17/2009 45

46. CONCLUSIONS 7/17/2009 46

47. Parallel Computing Has Arrived We must rethink how we write programs And we are definitely doing that Other things will also need to change Architecture Operating systems Algorithms Theory Application software We are seeing the biggest revolution in computing since its very beginnings 7/17/2009 47