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CS 267: Introduction to Parallel Machines and Programming Models

CS 267: Introduction to Parallel Machines and Programming Models. James Demmel demmel@cs.berkeley.edu www.cs.berkeley.edu/~demmel/cs267_Spr05. Outline. Overview of parallel machines and programming models Shared memory Shared address space Message passing Data parallel Clusters of SMPs

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CS 267: Introduction to Parallel Machines and Programming Models

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  1. CS 267: Introduction to Parallel Machines and Programming Models James Demmel demmel@cs.berkeley.edu www.cs.berkeley.edu/~demmel/cs267_Spr05 CS267 Lecture 3

  2. Outline • Overview of parallel machines and programming models • Shared memory • Shared address space • Message passing • Data parallel • Clusters of SMPs • Grid • Trends in real machines CS267 Lecture 3

  3. A generic parallel architecture P P P P M M M M Interconnection Network Memory • Where is the memory physically located? CS267 Lecture 3

  4. Parallel Programming Models • Control • How is parallelism created? • What orderings exist between operations? • How do different threads of control synchronize? • Data • What data is private vs. shared? • How is logically shared data accessed or communicated? • Operations • What are the atomic (indivisible) operations? • Cost • How do we account for the cost of each of the above? CS267 Lecture 3

  5. Simple Example Consider a sum of an array function: • Parallel Decomposition: • Each evaluation and each partial sum is a task. • Assign n/p numbers to each of p procs • Each computes independent “private” results and partial sum. • One (or all) collects the p partial sums and computes the global sum. Two Classes of Data: • Logically Shared • The original n numbers, the global sum. • Logically Private • The individual function evaluations. • What about the individual partial sums? CS267 Lecture 3

  6. i: 5 i: 8 i: 2 Programming Model 1: Shared Memory • Program is a collection of threads of control. • Can be created dynamically, mid-execution, in some languages • Each thread has a set of private variables, e.g., local stack variables • Also a set of shared variables, e.g., static variables, shared common blocks, or global heap. • Threads communicate implicitly by writing and reading shared variables. • Threads coordinate by synchronizing on shared variables Shared memory s s = ... y = ..s ... Private memory P1 Pn P0 CS267 Lecture 3

  7. Shared Memory Code for Computing a Sum static int s = 0; Thread 1 for i = 0, n/2-1 s = s + f(A[i]) Thread 2 for i = n/2, n-1 s = s + f(A[i]) • Problem is a race condition on variable s in the program • A race condition or data race occurs when: • two processors (or two threads) access the same variable, and at least one does a write. • The accesses are concurrent (not synchronized) so they could happen simultaneously CS267 Lecture 3

  8. Shared Memory Code for Computing a Sum static int s = 0; Thread 1 …. compute f([A[i]) and put in reg0 reg1 = s reg1 = reg1 + reg0 s = reg1 … Thread 2 … compute f([A[i]) and put in reg0 reg1 = s reg1 = reg1 + reg0 s = reg1 … 7 9 27 27 34 36 34 36 • Assume s=27, f(A[i])=7 on Thread1 and =9 on Thread2 • For this program to work, s should be 43 at the end • but it may be 43, 34, or 36 • The atomic operations are reads and writes • Never see ½ of one number • All computations happen in (private) registers CS267 Lecture 3

  9. static lock lk; lock(lk); lock(lk); unlock(lk); unlock(lk); Improved Code for Computing a Sum static int s = 0; Thread 1 local_s1= 0 for i = 0, n/2-1 local_s1 = local_s1 + f(A[i]) s = s + local_s1 Thread 2 local_s2 = 0 for i = n/2, n-1 local_s2= local_s2 + f(A[i]) s = s +local_s2 • Since addition is associative, it’s OK to rearrange order • Most computation is on private variables • Sharing frequency is also reduced, which might improve speed • But there is still a race condition on the update of shared s • The race condition can be fixed by adding locks (only one thread can hold a lock at a time; others wait for it) CS267 Lecture 3

  10. Machine Model 1a: Shared Memory • Processors all connected to a large shared memory. • Typically called Symmetric Multiprocessors (SMPs) • Sun, HP, Intel, IBM SMPs (nodes of Millennium, SP) • Difficulty scaling to large numbers of processors • <32 processors typical • Advantage: uniform memory access (UMA) • Cost: much cheaper to access data in cache than main memory. P2 P1 Pn $ $ $ bus memory CS267 Lecture 3

  11. Problems Scaling Shared Memory • Why not put more processors on (with larger memory?) • The memory bus becomes a bottleneck • Example from a Parallel Spectral Transform Shallow Water Model (PSTSWM) demonstrates the problem • Experimental results (and slide) from Pat Worley at ORNL • This is an important kernel in atmospheric models • 99% of the floating point operations are multiplies or adds, which generally run well on all processors • But it does sweeps through memory with little reuse of operands, which exercises the memory system • These experiments show serial performance, with one “copy” of the code running independently on varying numbers of procs • The best case for shared memory: no sharing • But the data doesn’t all fit in the registers/cache CS267 Lecture 3

  12. Example: Problem in Scaling Shared Memory • Performance degradation is a “smooth” function of the number of processes. • No shared data between them, so there should be perfect parallelism. • (Code was run for a 18 vertical levels with a range of horizontal sizes.) CS267 Lecture 3 From Pat Worley, ORNL

  13. Machine Model 1b: Distributed Shared Memory • Memory is logically shared, but physically distributed • Any processor can access any address in memory • Cache lines (or pages) are passed around machine • SGI Origin is canonical example (+ research machines) • Scales to 100s (512 have been built) • Limitation is cache coherent protocols – need to keep cached copies of the same address consistent P2 P1 Pn $ $ $ network memory memory memory CS267 Lecture 3

  14. receive Pn,s s: 11 s: 14 s: 12 i: 1 i: 3 i: 2 send P1,s Programming Model 2: Message Passing • Program consists of a collection of named processes. • Usually fixed at program startup time • Thread of control plus local address space -- NO shared data. • Logically shared data is partitioned over local processes. • Processes communicate by explicit send/receive pairs • Coordination is implicit in every communication event. • MPI is the most common example Private memory y = ..s ... P1 Pn P0 Network CS267 Lecture 3

  15. Second possible solution Processor 2 xloadl = A[2] receive xremote, proc1 send xlocal, proc1 s = xlocal + xremote Processor 1 xlocal = A[1] send xlocal, proc2 receive xremote, proc2 s = xlocal + xremote Computing s = A[1]+A[2] on each processor • First possible solution – what could go wrong? Processor 1 xlocal = A[1] send xlocal, proc2 receive xremote, proc2 s = xlocal + xremote Processor 2 xlocal = A[2] send xlocal, proc1 receive xremote, proc1 s = xlocal + xremote • If send/receive acts like the telephone system? The post office? CS267 Lecture 3

  16. MPI – the de facto standard In 2002 MPI has become the de facto standard for parallel computing The software challenge: overcoming the MPI barrier • MPI created finally a standard for applications development in the HPC community • Standards are always a barrier to further development • The MPI standard is a least common denominator building on mid-80s technology Programming Model reflects hardware! • “I am not sure how I will program a Petaflops computer, but I am sure that I will need MPI somewhere” – HDS 2001 CS267 Lecture 3

  17. P1 NI P0 NI Pn NI memory memory memory . . . interconnect Machine Model 2a: Distributed Memory • Cray T3E, IBM SP2 • PC Clusters (Berkeley NOW, Beowulf) • IBM SP-3, Millennium, CITRIS are distributed memory machines, but the nodes are SMPs. • Each processor has its own memory and cache but cannot directly access another processor’s memory. • Each “node” has a network interface (NI) for all communication and synchronization. CS267 Lecture 3

  18. Tflop/s Clusters The following are examples of clusters configured out of separate networks and processor components • Barcelona: 4th fastest in world (20 Tflop on Top500 Nov 2004; 4,536 2.2GHz IBM Power PC970s + Myrinet) • Shell: largest commercial engineering/scientific cluster • NCSA: 1024 processor cluster (IA64) • Univ. Heidelberg cluster • PNNL: announced 8 Tflops (peak) IA64 cluster from HP with Quadrics interconnect • DTF in US: announced 4 clusters for a total of 13 Teraflops (peak) CS267 Lecture 3

  19. Machine Model 2b: Internet/Grid Computing • SETI@Home: Running on 500,000 PCs • ~1000 CPU Years per Day • 485,821 CPU Years so far • Sophisticated Data & Signal Processing Analysis • Distributes Datasets from Arecibo Radio Telescope Next Step- Allen Telescope Array CS267 Lecture 3

  20. i: 5 i: 8 i: 2 Programming Model 2b: Global Addr Space • Program consists of a collection of named threads. • Usually fixed at program startup time • Local and shared data, as in shared memory model • But, shared data is partitioned over local processes • Cost models says remote data is expensive • Examples: UPC, Titanium, Co-Array Fortran • Global Address Space programming is an intermediate point between message passing and shared memory Shared memory s[n]: 27 s[0]: 27 s[1]: 27 y = ..s[i] ... Private memory s[myThread] = ... P1 Pn P0 CS267 Lecture 3

  21. P1 NI P0 NI Pn NI memory memory memory . . . interconnect Machine Model 2c: Global Address Space • Cray T3D, T3E, X1, and HP Alphaserver cluster • Clusters built with Quadrics, Myrinet, or Infiniband • The network interface supports RDMA (Remote Direct Memory Access) • NI can directly access memory without interrupting the CPU • One processor can read/write memory with one-sided operations (put/get) • Not just a load/store as on a shared memory machine • Remote data is typically not cached locally Global address space may be supported in varying degrees CS267 Lecture 3

  22. A: f fA: sum Programming Model 3: Data Parallel • Single thread of control consisting of parallel operations. • Parallel operations applied to all (or a defined subset) of a data structure, usually an array • Communication is implicit in parallel operators • Elegant and easy to understand and reason about • Coordination is implicit – statements executed synchronously • Similar to Matlab language for array operations • Drawbacks: • Not all problems fit this model • Difficult to map onto coarse-grained machines A = array of all data fA = f(A) s = sum(fA) s: CS267 Lecture 3

  23. P1 P1 P1 P1 P1 NI NI NI NI NI memory memory memory memory memory Machine Model 3a: SIMD System • A large number of (usually) small processors. • A single “control processor” issues each instruction. • Each processor executes the same instruction. • Some processors may be turned off on some instructions. • Machines are very specialized to scientific computing, so they are not popular with vendors (CM2, Maspar) • Programming model can be implemented in the compiler • mapping n-fold parallelism to p processors, n >> p, but it’s hard (e.g., HPF) control processor . . . interconnect CS267 Lecture 3

  24. Machine Model 3b: Vector Machines • Vector architectures are based on a single processor • Multiple functional units • All performing the same operation • Instructions may specific large amounts of parallelism (e.g., 64-way) but hardware executes only a subset in parallel • Historically important • Overtaken by MPPs in the 90s • Re-emerging in recent years • At a large scale in the Earth Simulator (NEC SX6) and Cray X1 • At a small sale in SIMD media extensions to microprocessors • SSE, SSE2 (Intel: Pentium/IA64) • Altivec (IBM/Motorola/Apple: PowerPC) • VIS (Sun: Sparc) • Key idea: Compiler does some of the difficult work of finding parallelism, so the hardware doesn’t have to CS267 Lecture 3

  25. … … … … … vr1 vr2 vr2 vr1 vr3 + + + + + + + Vector Processors • Vector instructions operate on a vector of elements • These are specified as operations on vector registers • A supercomputer vector register holds ~32-64 elts • The number of elements is larger than the amount of parallel hardware, called vector pipes or lanes, say 2-4 • The hardware performs a full vector operation in • #elements-per-vector-register / #pipes r1 r2 + (logically, performs # elts adds in parallel) r3 (actually, performs # pipes adds in parallel) CS267 Lecture 3

  26. S S S S V V V V V V V V 51 GB/s 0.5 MB $ 0.5 MB $ 0.5 MB $ 0.5 MB $ Cray X1 Node • Cray X1 builds a larger “virtual vector”, called an MSP • 4 SSPs (each a 2-pipe vector processor) make up an MSP • Compiler will (try to) vectorize/parallelize across the MSP custom blocks 12.8 Gflops (64 bit) 25.6 Gflops (32 bit) 25-41 GB/s 2 MB Ecache At frequency of 400/800 MHz To local memory and network: 25.6 GB/s 12.8 - 20.5 GB/s CS267 Lecture 3 Figure source J. Levesque, Cray

  27. Cray X1: Parallel Vector Architecture Cray combines several technologies in the X1 • 12.8 Gflop/s Vector processors (MSP) • Shared caches (unusual on earlier vector machines) • 4 processor nodes sharing up to 64 GB of memory • Single System Image to 4096 Processors • Remote put/get between nodes (faster than MPI) CS267 Lecture 3

  28. Earth Simulator Architecture • Parallel Vector Architecture • High speed (vector) processors • High memory bandwidth (vector architecture) • Fast network (new crossbar switch) Rearranging commodity parts can’t match this performance CS267 Lecture 3

  29. Machine Model 4: Clusters of SMPs • SMPs are the fastest commodity machine, so use them as a building block for a larger machine with a network • Common names: • CLUMP = Cluster of SMPs • Hierarchical machines, constellations • Most modern machines look like this: • Millennium, IBM SPs, ASCI machines • What is an appropriate programming model #4 ??? • Treat machine as “flat”, always use message passing, even within SMP (simple, but ignores an important part of memory hierarchy). • Shared memory within one SMP, but message passing outside of an SMP. CS267 Lecture 3

  30. Outline • Overview of parallel machines and programming models • Shared memory • Shared address space • Message passing • Data parallel • Clusters of SMPs • Trends in real machines CS267 Lecture 3

  31. TOP500 - Listing of the 500 most powerful Computers in the World - Yardstick: Rmax from Linpack Ax=b, dense problem - Updated twice a year: ISC‘xy in Germany, June xy SC‘xy in USA, November xy - All data available from www.top500.org TPP performance Rate Size CS267 Lecture 3

  32. TOP500 list - Data shown • Manufacturer Manufacturer or vendor • Computer Type indicated by manufacturer or vendor • Installation Site Customer • Location Location and country • Year Year of installation/last major update • Customer Segment Academic,Research,Industry,Vendor,Class. • # Processors Number of processors • Rmax Maxmimal LINPACK performance achieved • Rpeak Theoretical peak performance • Nmax Problemsize for achieving Rmax • N1/2 Problemsize for achieving half of Rmax • Nworld Position within the TOP500 ranking CS267 Lecture 3

  33. 22nd List: The TOP10 (2003) CS267 Lecture 3

  34. Continents Performance CS267 Lecture 3

  35. Continents Performance CS267 Lecture 3

  36. Customer Types CS267 Lecture 3

  37. Manufacturers CS267 Lecture 3

  38. Manufacturers Performance CS267 Lecture 3

  39. Processor Types CS267 Lecture 3

  40. Architectures CS267 Lecture 3

  41. NOW – Clusters CS267 Lecture 3

  42. Analysis of TOP500 Data • Annual performance growth about a factor of 1.82 • Two factors contribute almost equally to the annual total performance growth • Processor number grows per year on the average by a factor of 1.30 and the • Processor performance grows by 1.40 compared to 1.58 of Moore's Law Strohmaier, Dongarra, Meuer, and Simon, Parallel Computing 25, 1999, pp 1517-1544. CS267 Lecture 3

  43. Summary • Historically, each parallel machine was unique, along with its programming model and programming language. • It was necessary to throw away software and start over with each new kind of machine. • Now we distinguish the programming model from the underlying machine, so we can write portably correct codes that run on many machines. • MPI now the most portable option, but can be tedious. • Writing portably fast code requires tuning for the architecture. • Algorithm design challenge is to make this process easy. • Example: picking a blocksize, not rewriting whole algorithm. CS267 Lecture 3

  44. Reading Assignment • Extra reading for today • Cray X1 http://www.sc-conference.org/sc2003/paperpdfs/pap183.pdf • Clusters http://www.mirror.ac.uk/sites/www.beowulf.org/papers/ICPP95/ • "Parallel Computer Architecture: A Hardware/Software Approach" by Culler, Singh, and Gupta, Chapter 1. • Next week: Current high performance architectures • Shared memory (for Monday) • Memory Consistency and Event Ordering in Scalable Shared-Memory  Multiprocessors, Gharachorloo et al, Proceedings of the International symposium on Computer Architecture, 1990. • Or read about the Altix system on the web (www.sgi.com) • Blue Gene L (for Wednesday) • http://sc-2002.org/paperpdfs/pap.pap207.pdf CS267 Lecture 3

  45. Extra Slides CS267 Lecture 3

  46. PC Clusters: Contributions of Beowulf • An experiment in parallel computing systems • Established vision of low cost, high end computing • Demonstrated effectiveness of PC clusters for some (not all) classes of applications • Provided networking software • Conveyed findings to broad community (great PR) • Tutorials and book • Design standard to rally community! • Standards beget: books, trained people, software … virtuous cycle Adapted from Gordon Bell, presentation at Salishan 2000 CS267 Lecture 3

  47. Open Source Software Model for HPC • Linus's law, named after Linus Torvalds, the creator of Linux, states that "given enough eyeballs, all bugs are shallow". • All source code is “open” • Everyone is a tester • Everything proceeds a lot faster when everyone works on one code (HPC: nothing gets done if resources are scattered) • Software is or should be free (Stallman) • Anyone can support and market the code for any price • Zero cost software attracts users! • Prevents community from losing HPC software (CM5, T3E) CS267 Lecture 3

  48. Cluster of SMP Approach • A supercomputer is a stretched high-end server • Parallel system is built by assembling nodes that are modest size, commercial, SMP servers – just put more of them together Image from LLNL CS267 Lecture 3

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