1. Applications and Runtime for multicore/manycore March 21 2007 Geoffrey Fox Community Grids Laboratory Indiana University 505 N Morton Suite 224 Bloomington IN gcf@indiana.edu http://grids.ucs.indiana.edu/ptliupages/presentations/

2. Pradeep K. Dubey, pradeep.dubey@intel.com Tomorrow What is …?What if …? Is it …? RecognitionMiningSynthesis Create a model instance RMS: Recognition Mining Synthesis Model-based multimodal recognition Find a model instance Model Real-time analytics on dynamic, unstructured, multimodal datasets Photo-realism and physics-based animation Today Model-lessReal-time streaming and transactions on static – structured datasets Very limited realism

3. Discussed in Seminars http://grids.ucs.indiana.edu/ptliupages/presentations/PC2007/ Rest mainly classic parallel computing Intel’s Application Stack

4. Some Bioinformatics Datamining 1. Multiple Sequence Alignment (MSA) – Kernel Algorithms HMM (Hidden Markov Model) pairwise alignments (dynamic programming) with heuristics (e.g. progressive, iterative method) 2. Motif Discovery –Kernel Algorithms: –MEME (Multiple Expectation Maximization for Motif Elicitation) –Gibbs sampler 3. Gene Finding (Prediction) –Hidden Markov Methods 4. Sequence Database Search –Kernel Algorithms BLAST (Basic Local Alignment Search Tool) PatternHunter FASTA

5. Berkeley Dwarfs Dense Linear Algebra Sparse Linear Algebra Spectral Methods N-Body Methods Structured Grids Unstructured Grids Pleasingly Parallel Combinatorial Logic Graph Traversal Dynamic Programming Branch & Bound Graphical Models (HMM) Finite State Machine Consistent in Sprit with Intel Analysis I prefer to develop a few key applications rather than debate their classification!

6. Client side Multicore applications “Lots of not very parallel applications” Gaming; Graphics; Codec conversion for multiple user conferencing …… Complex Data querying and data manipulation/optimization/regression ; database and datamining (including computer vision) (Recognition and Mining for Intel Analysis) –Statistical packages as in Excel and R Scenario and Model simulations (Synthesis for Intel) Multiple users give several Server side multicore applications There are important architecture issues including memory bandwidth not discussed here!

7. Approach I Integrate Intel, Berkeley and other sources including database (successful on current parallel machines like scientific applications) and define parallel approaches in “white paper” Develop some key examples testing 3 parallel programming paradigms –Coarse Grain functional Parallelism (as in workflow) including pleasingly parallel instances with different data –Fine Grain functional Parallelism (as in Integer Programming) –Data parallel (Loosely Synchronous as in Science) Construct so can use different run time including perhaps CCR/DSS, MPI, Data Parallel.NET May be these will become libraries used as in MapReduce Workflow Coordination Languages ….

8. Approach II Have looked at CCR in MPI style applications –Seems to work quite well and support more general messaging models NAS Benchmark using CCR to confirm its utility Developing 4 exemplar multi-core parallel applications –Support Vector Machines (linear algebra) Data Parallel –Deterministic Annealing (statistical physics) Data Parallel –Computer Chess or Mixed Integer Programming Fine Grain Parallelism –Hidden Markov Method (Genetic Algorithms) Loosely Coupled functional Parallelism Test high level coordination to such parallel applications in libraries

9. CCR for Data Parallel (Loosely Synchronous) Applications CCR supports general coordination of messages queued in ports in Handler or Rendezvous mode DSS builds service model on CCR and supports coarse grain functional parallelism Basic CCR supports fine grain parallelism as in computer chess (and use STM enabled primitives?) MPI has well known collective communication which supply scalable global synchronization etc. Look at performance of MPI_Sendrecv What is model that encompasses best shared and distributed memory approaches for “data parallel” problems –This could be put on top of CCR? Much faster internal versions of CCR

10. Thread0 Port 3 Thread2 Port 2 Port 1 Port 0 Thread3 Thread1 Thread2 Port 2 Thread0 Port 0 Port 3 Thread3 Port 1 Thread1 Thread3 Port 3 Thread2 Port 2 Thread0 Port 0 Thread1 Port 1 (a) Pipeline(b) Shift (d) Exchange Thread0 Port 3 Thread2 Port 2 Port 1 Port 0 Thread3 Thread1 (c) Two Shifts Four Communication Patterns used in CCR Tests. (a) and (b) use CCR Receive while (c) and (d) use CCR Multiple Item Receive Use on AMD 4-core Xeon 4-core Xeon 8-core Latter do up to 8 way parallelism

11. Write Exchanged Messages Port 3 Port 2 Thread0 Thread3 Thread2 Thread1 Port 1 Port 0 Thread0 Write Exchanged Messages Port 3 Thread2 Port 2 Exchanging Messages with 1D Torus Exchange topology for loosely synchronous execution in CCR Thread0 Read Messages Thread3 Thread2 Thread1 Port 1 Port 0 Thread3 Thread1 Stage Break a single computation into different number of stages varying from 1.4 microseconds to 14 seconds for AMD (1.6 microseconds to 16 seconds for Xeon Quad core)

12. Stages (millions) Fixed amount of computation (4.10 7 units) divided into 4 cores and from 1 to 10 7 stages on HP Opteron Multicore. Each stage separated by reading and writing CCR ports in Pipeline mode Time Seconds 8.04 microseconds overhead per stage averaged from 1 to 10 million stages Overhead = Computation Computation Component if no Overhead 4-way Pipeline Pattern 4 Dispatcher Threads HP Opteron 1.4 microseconds computation per stage 14 microseconds computation per stage

13. Stage Overhead versus Thread Computation time Overhead per stage constant up to about million stages and then increases 14 Seconds Stage Computation 14 Microseconds

14. Stages (millions) Fixed amount of computation (4.10 7 units) divided into 4 cores and from 1 to 10 7 stages on Dell 2 processor 2-core each Xeon Multicore. Each stage separated by reading and writing CCR ports in Pipeline mode Time Seconds 12.40 microseconds per stage averaged from 1 to 10 million stages 4-way Pipeline Pattern 4 Dispatcher Threads Dell Xeon Overhead = Computation Computation Component if no Overhead

15. Summary of Stage Overheads for AMD 2-core 2-processor Machine These are stage switching overheads for a set of runs with different levels of parallelism and different message patterns –each stage takes about 28 microseconds (500,000 stages)

16. Summary of Stage Overheads for Intel 2-core 2-processor Machine These are stage switching overheads for a set of runs with different levels of parallelism and different message patterns –each stage takes about 30 microseconds. AMD overheads in parentheses These measurements are equivalent to MPI latencies

17. Summary of Stage Overheads for Intel 4-core 2-processor Machine These are stage switching overheads for a set of runs with different levels of parallelism and different message patterns –each stage takes about 30 microseconds. 2-core 2- processor Xeon overheads in parentheses These measurements are equivalent to MPI latencies

18. XP-Pro 8-way Parallel Pipeline on two 4-core Xeon Histogram of 100 runs -- each run has 500,000 synchronizations following a thread execution that takes 33.92 microseconds –So overhead of 6.1 microseconds modest Message size is just one integer Choose computation unit that is appropriate for a few microsecond stage overhead AMD 4-way 27.94 microsecond Computation Unit XP Pro

19. 8-way Parallel Shift on two 4-core Xeon Histogram of 100 runs -- each run has 500,000 synchronizations following a thread execution that takes 33.92 microseconds –So overhead of 8.2 microseconds modest Shift versus pipeline adds a microsecond to cost Unclear what causes second peak XP-Pro VISTA AMD 4-way 27.94 microsecond Computation Unit XP Pro

20. 8-way Parallel Double Shift on two 4-core Xeon Histogram of 100 runs -- each run has 500,000 synchronizations following a thread execution that takes 33.92 microseconds –So overhead of 22.3 microseconds significant –Unclear why double shift slow compared to shift Exchange performance partly reflects number of messages Opteron overheads significantly lower than Intel XP-Pro AMD 4-way 27.94 microsecond Computation Unit XP Pro

21. AMD 2-core 2-processor Bandwidth Measurements Previously we measured latency as measurements corresponded to small messages. We did a further set of measurements of bandwidth by exchanging larger messages of different size between threads We used three types of data structures for receiving data Array in thread equal to message size Array outside thread equal to message size Data stored sequentially in a large array (“stepped” array) For AMD and Intel, total bandwidth 1 to 2 Gigabytes/second

22. Intel 2-core 2-processor Bandwidth Measurements For bandwidth, the Intel did better than AMD especially when one exploited cache on chip with small transfers For both AMD and Intel, each stage executed a computational task after copying data arrays of size 10 5 (labeled small), 10 6 (labeled large) or 10 7 double words. The last column is an approximate value in microseconds of the compute time for each stage. Note that copying 100,000 double precision words per core at a gigabyte/second bandwidth takes 3200 µs. The data to be copied (message payload in CCR) is fixed and its creation time is outside timed process

23. Typical Bandwidth measurements showing effect of cache with slope change 5,000 stages with run time plotted against size of double array copied in each stage from thread to stepped locations in a large array on Dell Xeon Multicore Time Seconds 4-way Pipeline Pattern 4 Dispatcher Threads Dell Xeon Total Bandwidth 1.0 Gigabytes/Sec up to one million double words and 1.75 Gigabytes/Sec up to 100,000 double words Array Size: Millions of Double Words Slope Change (Cache Effect)

24. Timing of HP Opteron Multicore as a function of number of simultaneous two- way service messages processed (November 2006 DSS Release) CGL Measurements of Axis 2 shows about 500 microseconds – DSS is 10 times better DSS Service Measurements