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Optimizing Applications on Blue Waters

Optimizing Applications on Blue Waters. Blue Waters Institute June 5, 2014. Victor Anisimov NCSA Science and Engineering Applications Support. Overview. Hardware Topology Aware Scheduling Balanced Injection: Sharing the Network Compilers Libraries Profiling: Finding Hot Spots in the Code

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Optimizing Applications on Blue Waters

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  1. Optimizing Applications on Blue Waters Blue Waters Institute June 5, 2014 Victor Anisimov NCSA Science and Engineering Applications Support

  2. Overview • Hardware • Topology Aware Scheduling • Balanced Injection: Sharing the Network • Compilers • Libraries • Profiling: Finding Hot Spots in the Code • File System and I/O Performance Optimization • Training materials: cp -pr /u/staff/anisimov/training_bw . Performance Optimization on Blue Waters

  3. Hardware Overview Performance Optimization on Blue Waters

  4. Blue Waters – Configuration • GPU: NVIDIA K20X (Kepler GK110) • CPU: AMD 6276 Interlagos (2.3 – 2.6 GHz) • 3D Torus Network Topology • 22,640 XE6 nodes (Dual CPU), 64 GB RAM each • 4,224 XK7 nodes (CPU+GPU), 32 GB RAM each • File system 26.4 PB, Aggregate I/O bandwidth 1 TB/s Performance Optimization on Blue Waters

  5. Cray XE6 Blade has 2 Compute nodes Z Y X Performance Optimization on Blue Waters

  6. AMD 6276 Interlagos Processor Processor socket • Compute node contains • XE6: 2 Processors • Processor has 2 numa nodes • Numa node has 4 Bulldozer modules • Each Buldozer module has single FP-unit and 2 integer cores Numa node Memory Controller Memory Controller NB/HT Links NB/HT Links Shared L3 Cache Shared L3 Cache Performance Optimization on Blue Waters

  7. Job Submitting Performance Options: XE6 BW node: 64 GB RAM, 16 Bulldozer cores, 32 cores, 4 NUMA domains • aprun enumerates cores from 0 to 31; each pair represents a BD core • BD core consists of 2 compute cores sharing single FP unit • Default task placement – fill up from 0 to 31 • -N16 will use cores 0,1,…,15 utilizing only 8 FP units • -N16 -d2 will utilize cores 0,2,4,6,8,10,12,14,…,24,26,28,30 and 16 FP units • aprun options to specify particular number of tasks per XE6 node • -N1 1 process • -N2 -cc 0,16 2 processes • -N4 -d8 4 processes, one per NUMA node(cache optimal) • -N8 -d4 8 processes, two per NUMA node • -N16 -d2 16 processes, interleaved, 16 BD cores • -N32 32 processes (may improve performance over -N16) Performance Optimization on Blue Waters

  8. Test 01: Optimal MPI task placement • Question: • When 16 cores do better job than 32 cores? • How to optimally use fewer than 32 cores per XE6 node? • Running the test: • Show task placement: export MPICH_CPUMASK_DISPLAY=1 • cc app.c; qsub run • aprun -n32 ./a.out (Time = 7.613) • aprun -n16 ./a.out (Time = 5.995) • aprun -n16 -d2 ./a.out (Time = 5.640) • Take home lesson: Contention will degrade performance. Test different task placements and choose the optimal one before starting the production computations. Performance Optimization on Blue Waters

  9. 3D Torus in XY plane Z PCIe Gen2 PCIe Gen2 Y X Performance Optimization on Blue Waters

  10. Cray 3D Torus Topology VMD image of 3D torus • Torus is a periodic box in 3D • XK nodes – red • Service nodes – blue • Compute nodes – gray • Slower – Y, X, Z – Faster • Routing X, then Y, then Z • Routing path depends on application placement on 3D torus Reference: Bob Fiedler, Cray Inc.: https://bluewaters.ncsa.illinois.edu/documents/10157/12008/AdvancedFeatures_PRAC_WS_2013-02-27.pdf Performance Optimization on Blue Waters

  11. Why My Job Run Slow - Performance Variation • Performance variation due to job-job interaction • Yellow – defragmented 1000-node job, red – XK nodes, blue –service nodes, other jobs not shown Dedicated-machine performance Example of defragmented node allocation Performance Optimization on Blue Waters

  12. Moab: Nodesets Available Shapes and Sizes (number of nodes in them): Sheets: 1100, 2200, 6700, 8200, 12300 Bars: 6700 Cubes: 3300 Yellow: Job placement in a cube nodeset Specifying a nodeset in PBS script: #PBS -l nodes=3360:ppn=32 #PBS -l nodeset=ONEOF:FEATURE:c1_3300n:c2_3300n:c3_3300n: c5_3300n:c6_3300n:c7_3300n Performance Optimization on Blue Waters

  13. Moab: Hostlist Yellow – Job placement in 3D torus #PBS -l nodes=1000:ppn=32 #PBS -l hostlist=1152+1153+1154+1155+1156+…+8207^ Performance Optimization on Blue Waters

  14. Test 02: Optimal job placement on 3D torus • Challenge: Chose a pair of adjacent nodes on 3D torus. Find the application performance on 0-1, Y-Y, and Z-Z links. • Runnig the test: • Show node ids and X,Y,Z coordinates: getnodexyz.sh • cc app.c; qsub run • Example of 0-1, Z-Z, Y-Y links • aprun -n64 -L 24124,24125 ./a.out • aprun -n64 -L 24125,24126 ./a.out • aprun -n64 -L 24108,24178 ./a.out • (non-local) aprun -n64 -L 23074,24102 ./a.out • Try your own node ids to test your understanding • Take home lesson: Different links have different network bandwidth: 0-1 > Z-Z = X-X > Y-Y Z Y node 1 X node 0 Performance Optimization on Blue Waters

  15. Congestion Protection • Network congestion is a condition that occurs when the volume of traffic on the high-speed network (HSN) exceeds the capacity to handle it. • To "protect" the network from data loss, congestion protection (CP) globally “throttles” injection bandwidth per-node. • If CP happens often, application performance degrades. http://lh5.google.ca/abramsv/R9WYOKtLe1I/AAAAAAAALO4/FLefbnOq5rQ/s1600-h/495711679_52f8d76d11_o.jpg • At job completion you might see the following message reported to stdout: Application 61435 network throttled: 4459 nodes throttled, 25:31:21 node-seconds Application 61435 balanced injection 100, after throttle 63 • Throttling disrupts the work on the entire machine. Performance Optimization on Blue Waters

  16. Types of congestion events • There are two main forms of congestion: many-to-one and long-path. The former is easy to detect and correct. The latter is harder to detect and may not be correctable. • Many-to-one congestion occurs in some algorithms and can be corrected. See “Modifying Your Application to Avoid Gemini Network Congestion Errors” on balanced injection section on the portal. • Long-path congestion is typically due to a combination of communication pattern and node allocation. It can also be due to a combination of jobs running on the system. • We monitor for cases of congestion protection and try to determine the most likely cause. Performance Optimization on Blue Waters

  17. Congestion on a Shared, Torus Network • HSN uses dimension ordered routing: X-then-Y-then-Z between two locations on the torus. Note that AB≠BA. • Shortest route can sometimes cause traffic to pass through geminis used by other jobs. A B • Non-compact node allocations can have traffic that passes through geminis used by other jobs. • I/O traffic can lead to network hot-spots. • We are working with Adaptive and Cray on eliminating some of the above causes of congestion with better node allocation: shape, location, etc. Performance Optimization on Blue Waters

  18. Balanced Injection • Balanced Injection (BI) is a mechanism that attempts to reduce compute node injection bandwidth in order to prevent throttling and which may have the effect of improving application performance for certain communication patterns. • BI can be applied “per-job” using an environment variable or with user accessible API. • export APRUN_BALANCED_INJECTION=63 • Can be set from 1-100 (100 = no BI). • There isn’t a linear relation of BI to application performance. • MPI-based applications have “balanced injection” enabled in collective MPI calls that locally “throttle” injection bandwidth. Performance Optimization on Blue Waters

  19. Compilers Performance Optimization on Blue Waters

  20. Available Compilers • Cray Compilers - Cray Compiling Environment (CCE) • Fortran 2003, Co-arrays, UPC, PGAS, OpenACC • GNU Compiler Collection (GCC) • Portland Group Inc (PGI) Compilers • OpenACC • Intel Compilers (to be available soon) • All compilers provide Fortran, C, C++, OpenMP support • Use cc, CC, ftnwrappers for C, C++, and FORTRAN • So which compiler do I choose? • Experiment with various compilers • Work with BW staff • Mixing libraries created by different compilers may cause issues Performance Optimization on Blue Waters

  21. Compiler Choices – Relative Strength • CCE – outstanding Fortran, very good C, and okay C++ • Very good vectorization • Very good Fortran language support; only real choice for coarrays • C support is very good, with UPC support • Very good scalar optimization and automatic parallelization • Clean implementation of OpenMP 3.0 with tasks • Cleanest integration with other Cray tools (Performance tools, debuggers, upcoming productivity tools) • No inline assembly support • Excellent support from Cray (bugs, issues, performance etc) Performance Optimization on Blue Waters

  22. Compiler Choices – Relative Strength • PGI – very good Fortran, okay C and C++ • Good vectorization • Good functional correctness with optimization enabled • Good automatic prefetch capabilities • Company (NVIDIA) focused on HPC market • Excellent working relationship with Cray • Slow bug fixing Performance Optimization on Blue Waters

  23. Compiler Choices – Relative Strength • GNU – good Fortran, outstanding C and C++ • Obviously, the best gcc compatibility • Scalable optimizer was recently rewritten and is very good • Vectiorization capabilities focus mostly on inline assembly • Few releases have been incompatible with each other and require recompilation of modules (4.3, 4.4, 4.5) • General purpose application, not necessarily HPC Performance Optimization on Blue Waters

  24. Recommended CCE Compilation Options • Use default optimization levels • It’s the equivalent of most other compilers -O3 or -fast • Use -O3, fp3 (or -O3 -hfp3 or some variation) • -O3 gives slightly more than -O2 • -hfp3 gives a lot more floating point optimizations, esp 32 bit • If an application is intolerant of floating point reassociation, try lower hfp number, try hfp1 first, only hfp0 if absolutely necessary • Might be needed for tests that require strict IEEE conformance • Or applications that have validated results from diffferent compiler • Do not suggest using -Oipa5, -Oaggress and so on; higher numbers are not always correlated with better performance • Compiler feedback : -rm (fortran), -hlist=m ( C ) • If don’t want OpenMP : -xomp or -Othread0 or -hnoomp • Manpages : crayftn, craycc, crayCC Performance Optimization on Blue Waters

  25. Starting Point for PGI Compilers • Suggested Option : -fast • Interprocedural analysis allows the compiler to perform whole program optimizations : -Mipa=fast(,safe) • If you can be flexible with precision, also try -Mfprelaxed • Option -Msmartalloc, calls the subroutine mallopt in the main routine, can have a dramatic impact on the performance of program that uses dynamic allocation of memory • Compiler feedback : -Minfo=all, -Mneginfo • Manpages : pgf90, pgcc, pgCC Performance Optimization on Blue Waters

  26. Additional PGI Compiler Options • -default64 : Fortran driver option for -i8 and -r8 • -i8, -r8 : Treats INTEGER and REAL variables in Fortran as 8 bytes (use ftn -default64 option to link the right libraries) • -byteswapio : Reads big endian files in fortran • -Mnomain : Uses ftn driver to link programs with the main program (written in C or C++) and one or more subroutines (written in fortran) Performance Optimization on Blue Waters

  27. Starting Point for GNU Compilers • -O3 –ffast-math –funroll-loops • Compiler feedback : -ftree-vectorizer-verbose=2 • Manpages : gfortran, gcc, g++ Performance Optimization on Blue Waters

  28. Test 03: Manual code optimization • Challenge: Find and fix two programming blunders in the test code. Modify app2.c and app3.c so their runtime T(app1) > T(app2) > T(app3). • Runnig the test: • cc -hlist=m -o app1.x app1.c • cc -hlist=m -o app2.x app2.c • cc -hlist=m -o app3.x app3.c • qsub run • Target: T(app1)=5.665s, T(app2)=0.242s, T(app3)=0.031s Performance Optimization on Blue Waters

  29. Test 03: Solution app1.c app2.c app3.c Presentation Title

  30. Libraries Performance Optimization on Blue Waters

  31. Libraries: Where to Start • Libraries motto: Reuse rather than reinvent • Libraries are tailored to Programming Environment • Choose programming environment PrgEnv-[cray,gnu,pgi] • Load library module: module load <libname> • See actual path: module show <libname> • Location path: ls –l $CRAY_LIBSCI_PREFIX_DIR/lib • Use compiler wrappers: ftn, cc, CC • For most applications, using default settings work very well • OpenMP threaded BLAS/LAPACK libraries are available • The serial version is used if “OMP_NUM_THREADS” is not set or set to 1 Performance Optimization on Blue Waters

  32. PETSc (Argonne National Laboratory) • Programmable, Extensible Toolkit for Scientific Computing • Widely-used collection of many different types of linear and non-linear solvers • Actively under development; very responsive team • Can also interface with numerous optional external packages (e.g., SLEPC, HYPRE, ParMETIS, …) • Optimized version installed by Cray, along with many external packages • Use “module load petsc[/version]” Performance Optimization on Blue Waters

  33. Other Numerical Libraries • ACML (AMD Core Math Library) • BLAS, LAPACK, FFT, Random Number Generators • Trilinos (from Sandia National Laboratories) • Somewhat similar to PETSc, interfaces to a large collection of preconditioners, solvers, and other computational tools • GSL (GNU Scientific Library) • Collection of numerous computational solvers and tools for C and C++ programs • See all available modules “module avail” Performance Optimization on Blue Waters

  34. Cray Scientific Library (libsci) • Contains optimized versions of several popular scientific software routines • Available by default; see available versions with “module avail cray-libsci” and load particular version “module load cray‑libsci[/version]” • BLAS, BLACS • LAPACK, ScaLAPACK • FFT, FFTW • Unique to Cray (affects portability) • CRAFFT, CASE, IRT Performance Optimization on Blue Waters

  35. Cray Accelerated Libraries Cray LibSci accelerated BLAS, LAPACK, and ScaLAPACK libraries PrgEnv-cray or PrgEnv-gnu Programming Environment module add craype-accel-nvidia35 call libsci_acc_init() … (your code) … call libsci_acc_finalize() The Library interface automatically initiates appropriate execution mode (CPU, GPU, Hybrid). When BLAS or LAPACK routines are called from applications built with the Cray or GNU compilers, Cray LibSci automatically loads and links libsci_acc libraries upon execution if it determines performance will be enhanced. Execution control from source code: routine_name invokes automated method routine_name_cpu executed on host CPU only routine_name_acc executed on GPU only See “man intro_libsci_acc” for more information. Performance Optimization on Blue Waters

  36. Cray Accelerated Libraries, continued Libsci_acc is not thread safe. It will fail when called concurrently from OpenMP threads. ENVIRONMENT VARIABLES CRAY_LIBSCI_ACC_MODE Specifies execution mode for libsci_acc routine: 0 Use automated mode. Adds slight overhead. This is the default. 1 Forces all supported auto-tuned routines to execute on the accelerator. 2 Forces all supported routines to execute on the CPU if the data is located within host processor address space. LIBSCI_ACC_BYPASS_FUNCTION Specifies the execution mode for FUNCTION. 0 Use automated mode. Adds slight overhead. This is the default. 1 Call the version that handles data addresses resident on the GPU. 2 Call the version that handles data addresses resident on the CPU. 3 For SGEMM, DGEMM, CGEMM, ZGEMM, this will call accelerated version. Warning: Passing a wrong CPU / GPU address will cause the program to crash. Performance Optimization on Blue Waters

  37. Test 04: Speed up matrix multiplication by using OpenMP-threaded AMD Core Math Library Challenge: Speed up application by using threaded library Running the test module swap PrgEnv-cray PrgEnv-pgi module add acml cc -lacml -Wl,-ydgemm_ -o app1.x app.c /opt/acml/5.3.1/pgi64_fma4/lib/libacml.a(dgemm.o): definition of dgemm_ cc -lacml -Wl,-ydgemm_ -mp=nonuma -o app2.x app.c /opt/acml/5.3.1/pgi64_fma4/lib/libacml.a(dgemm.o): definition of dgemm_ export OMP_NUM_THREADS=32 aprun -n1 -cc none -d32 ./app1.x aprun -n1 -cc none -d32 ./app2.x aprun -n1 -d32 ./app2.x Output: Time = 2.938, Time = 0.213, Time = 0.302 Take home lesson: Reuse instead of reinvent. “-Wl,-ydgemm_” tells where dgemm() was resolved from. “-cc none” allows OpenMP threads to migrate. Performance Optimization on Blue Waters

  38. Profiling Performance Optimization on Blue Waters

  39. Supports traditional post-mortem performance analysis Automatic identification of performance problems Indication of causes of problems Suggestions of modifications for performance improvement pat_build: provides automatic instrumentation CrayPAT run-time library collects measurements (transparent to the user) pat_report performs analysis and generates text reports pat_help: online help utility To start working with CrayPAT: module load perftools http://docs.cray.com/books/S-2376-612/S-2376-612.pdf The CrayPerformance Analysis Tools Performance Optimization on Blue Waters

  40. Supports two categories of experiments asynchronous experiments (sampling) capture values from the call stack or the program counter at specified intervals or when a specified counter overflows Event-based experiments (tracing) count some events such as the number of times a specific system call is executed While tracing provides most detailed information, it can be very heavy if the application runs on a large number of cores for a long period of time Sampling (-S, default: -Oapa = sampling +HWPC + MPI tracing) can be useful as a starting point, to provide a first overview of the work distribution Application Instrumentation with pat_build Performance Optimization on Blue Waters

  41. Optional timeline view of program available export PAT_RT_SUMMARY=0 (minimize volume of tracing data) Request hardware performance counter information: export PAT_RT_PERFCTR=1 (FLOP count) Example Runtime Environment Variables Performance Optimization on Blue Waters

  42. blas Basic Linear Algebra subprograms caf Co-Array Fortran (Cray CCE compiler only) hdf5 manages extremely large data collection heap dynamic heap io includes stdio and sysio groups lapack Linear Algebra Package math ANSI math mpi MPI omp OpenMP API pthreads POSIX threads shmem SHMEM sysio I/O system calls system system calls upc Unified Parallel C (Cray CCE compiler only) For a full list, please see pat_build(1) man page Predefined Trace Wrappers (-g tracegroup) Performance Optimization on Blue Waters

  43. > pat_build –O apa Gets top time consuming routines Least overhead > pat_build –u –g mpi ./my_program Collects information about user functions and MPI > pat_build –w ./my_program Collections information for MAIN Lightest-weight tracing > pat_build –g netcdf,mpi ./my_program Collects information about netcdf routines and MPI Example Experiments Performance Optimization on Blue Waters

  44. Access performance tools software % module load perftools Build application keeping .o files (CCE: -h keepfiles) % make clean ; make Instrument application for automatic profiling analysis You should get an instrumented program a.out+pat % pat_build a.out Run application to get top time consuming routines % aprun … a.out+pat (or qsub <pat script>) Steps to Collecting Performance Data Performance Optimization on Blue Waters

  45. You should get a performance file (“<sdatafile>.xf”) or multiple files in a directory <sdatadir> Generate report % pat_report <sdatafile>.xf > sampling_report Steps to Collecting Performance Data (2) Performance Optimization on Blue Waters

  46. Example: HW counter data PAPI_TLB_DM Data translation lookaside buffer misses PAPI_L1_DCA Level 1 data cache accesses PAPI_FP_OPS Average Rate of Floating point operations per single MPI task MFLOPS (aggregate) Total FLOP rate of the entire job FLOP rate shows the efficiency of hardware utilization ======================================================================== USER ------------------------------------------------------------------------ Time% 98.3% Time 4.434402 secs Imb.Time -- secs Imb.Time% -- Calls 0.001M/sec 4500.0 calls PAPI_L1_DCM 14.820M/sec 65712197 misses PAPI_TLB_DM 0.902M/sec 3998928 misses PAPI_L1_DCA 333.331M/sec 1477996162 refs PAPI_FP_OPS 445.571M/sec 1975672594 ops User time (approx) 4.434 secs 11971868993 cycles 100.0%Time Average Time per Call 0.000985 sec CrayPat Overhead : Time 0.1% HW FP Ops / User time 445.571M/sec 1975672594 ops 4.1%peak(DP) HW FP Ops / WCT 445.533M/sec Computational intensity 0.17 ops/cycle 1.34 ops/ref MFLOPS (aggregate) 1782.28M/sec TLB utilization 369.60 refs/miss 0.722 avg uses D1 cache hit,miss ratios 95.6% hits 4.4% misses D1 cache utilization (misses) 22.49 refs/miss 2.811 avg hits ======================================================================== Performance Optimization on Blue Waters

  47. Example: Time spent in User and MPI functions Table 1: Profile by Function Samp % | Samp | Imb. | Imb. |Group | | Samp | Samp % | Function | | | | PE='HIDE’ 100.0% | 775 | -- | -- |Total |------------------------------------------- | 94.2% | 730 | -- | -- |USER ||------------------------------------------ || 43.4% | 336 | 8.75 | 2.6% |mlwxyz_ || 16.1% | 125 | 6.28 | 4.9% |half_ || 8.0% | 62 | 6.25 | 9.5% |full_ || 6.8% | 53 | 1.88 | 3.5% |artv_ || 4.9% | 38 | 1.34 | 3.6% |bnd_ || 3.6% | 28 | 2.00 | 6.9% |currenf_ || 2.2% | 17 | 1.50 | 8.6% |bndsf_ || 1.7% | 13 | 1.97 | 13.5% |model_ || 1.4% | 11 | 1.53 | 12.2% |cfl_ || 1.3% | 10 | 0.75 | 7.0% |currenh_ || 1.0% | 8 | 5.28 | 41.9% |bndbo_ || 1.0% | 8 | 8.28 | 53.4% |bndto_ ||========================================== | 5.4% | 42 | -- | -- |MPI ||------------------------------------------ || 1.9% | 15 | 4.62 | 23.9% |mpi_sendrecv_ || 1.8% | 14 | 16.53 | 55.0% |mpi_bcast_ || 1.7% | 13 | 5.66 | 30.7% |mpi_barrier_ |=========================================== Computation intensity vs. Communication intensity Performance Optimization on Blue Waters

  48. Test 05: Obtain FLOP count and User/MPI time • Challenge: What is application FLOP count? Use CrayPat to perform profiling experiment and compare the result with manual count. • Running the test: • module add perftools • cc -c app.c // compile the application • cc -o app.x app.o // link the application • pat_build app.x // instrument the application • qsub run // submit the job • aprun -n16 -d2 ./app.x+pat • pat_report app.x+pat*.xf > report.out • Results: • MPI: 88.9%; USER: 11.1%; communication intensive job • FLOP count = 2 GF (hint – convert FLOP rate to FLOP count) Performance Optimization on Blue Waters

  49. I/O optimization HDF5 I/O Library PnetCDF Adios IOBUF MPI-IO I/O Middleware Scientist… Application… Damaris Utilities Parallel File System Lustre Darshan Blue Waters Performance Optimization on Blue Waters

  50. Lustre File System: Striping Physical • File striping: single files are distributed across a series of OSTs • File size can grow to the aggregate size of available OSTs (rather than a single disk) • Accessing multiple OSTs concurrently increases I/O bandwidth Logical Performance Optimization on Blue Waters

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