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GWDG Matrix Transpose Results with Hybrid OpenMP / MPI O. Haan Gesellschaft für wissenschaftliche Datenverarbeitung Göttingen, Germany ( GWDG ) SCICOMP 2000, SDSC, La Jolla

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Overview Hybrid Programming Model Distributed Matrix Transpose Performance Measurements Summary of Results

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Architecture of Scalable Parallel Computers Two level hierarchy cluster of SMP nodes distributed memory high speed interconnect SMP nodes with multiple processors shared memory bus or switch connected

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Programming Models message passing over all processors MPI implementation for shared memory multiple access to switch adapters SP: 4-way Winterhawk2 + 8-way Nighthawk - shared memory over all processors virtual global address space SP: - hybrid message passing - shared memory message passing between nodes shared memory within nodes SP: +

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Hybrid Programming Model SPMD program with MPI tasks OpenMP threads within each task communication between MPI tasks

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Example of Hybrid Program program hybrid_example include “ mpif.h “ com = MPI_COMM_WORLD call MPI_INIT(ierr) call MPI_COMM_SIZE(com,nk,ierr) call MPI_COMM_RANK(com,my_task,ierr) kp = OMP_GET_NUM_PROCS() !$OMP PARALLEL PRIVATE(my_thread) my_thread = OMP_GET_THREAD_NUM() call work(my_thread,kp,my_task,nk,thread_res) !$OMP END PARALLEL do i = 0, kp-1 node_res = node_res + thread_res(i) end do call MPI_REDUCE(node_res,glob_res,1, : MPI_REAL,MPI_SUM,0,com,ierr) call MPI_FINALIZE(ierr) stop end

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Hybrid Programming vs. Pure Message Passing + works on all SP configuration coarser internode communication granularity faster intranode communication - larger programming effort additional synchronization steps reduced reuse of cached data the net score depends on the problem

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Distributed Matrix Transpose

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GWDG O. Haan, Matrix Transpose Results, SCICOMP step Transpose n1 x n2 matrix A( i1, i2 ) --> n2 x n1 matrix B( i2, i1 ) decompose n1, n2 in local and global parts: n1 = n1l * np n2 = n2l * np write matrices A, B as 4-dim arrays: A( i1l, i1g, i2l ; i2g ), B( i2l, i2g, i1l ; i1g ) step 1 : local reorder A( i1l, i1g, i2l ; i2g ) -> a1( i1l, i2l, i1g ; i2g ) step 2 : global reorder a1( i1l, i2l, i1g ; i2g ) -> a2( i1l, i2l, i2g ; i1g ) step 3 : local transpose a2( i1l, i2l, i2g ; i1g ) -> B( i2l, i2g, i1l ; i1g )

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Local Steps: Copy with Reorder data in memory: speed limited by performance of bus and memory subsystems Winterhawk2 : all processors share the same bus bandwidth : 1.6 GB/s data in cache: speed limited by processor performance Winterhawk2 : one load plus one store per cycle bandwidth : 8 MB / (1/375) s =3 GB / s

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Copy: Data in Memory

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Copy : Prefetch

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Copy : Data in Cache

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Global Reorder a1( *, *, i1g ; i2g ) -> a2( *, *, i2g ; i1g ) global reorder on np processors in np steps p0 p1 p2 step0 step1 step2

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Performance Modelling Hardware model: nk nodes with kp procs each np = nk * kp is total procs count Switch model:nk concurrent links between nodes latency tlat, bandwidth c execution model for Hybrid: reorder on nk nodes: nk steps with n1*n2 / nk**2 data per node execution model for MPI: reorder on np processors: np steps with n1*n2 / np**2 data per node switch links shared between kp procs

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Performance Modelling Hybrid timing model: MPI timing model:

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Timing of Global Reorder (internode part)

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Timing of Global Reorder (internode part)

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Timing of Global Reorder

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Timing of Transpose

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Scaling of Transpose

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Timing of Transpose Steps

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Summary of Results: Hardware Memory access in Winterhawk2 is not adaquate: copy rate of 400 MB/s = 50 Mwords/s peak CPU rate of 6000 Mflops/s a factor of 100 between computational speed and memory speed Sharing of switch link by 4 processors degrades communication speed: bandwidth smaller by more than a factor of 4 ( factor of 4 expected ) latency larger by nearly a factor of 4 ( factor of 1 expected )

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Summary of Results: Hybrid vs. MPI hybrid OpenMP / MPI programming is profitable for distributed matrix tranpose : 1000 x 1000 matrix on 16 nodes : 2.3 times faster x matrix on 16 nodes : 1.1 times faster Competing influences : MPI programming enhances use of cached data Hybrid programming has lower communication latency and coarser communication granularity

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Summary of Results: Use of Transpose in FFT 2-dim complex array of size Execution time on nk nodes : where r : computational speed per node c : transpose speed per node effective execution speed per node :

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GWDG O. Haan, Matrix Transpose Results, SCICOMP Summary of Results: Use of Transpose in FFT- Example SP r = 4 * 200 Mflop/s = 800 Mflop/s c depends on n, nk and programming model nk = 16 n = 10**6 10**9 hybrid c = Mword/s MPI c = Mword/s effective execution speed per node hybrid = Mflop/s MPI = Mflop/s

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