Multi-GPU Scaling of Direct Sparse Linear System Solver for Finite-Difference Frequency-Domain Photonic Simulation
|
|
- Laurel Hines
- 6 years ago
- Views:
Transcription
1 Multi-GPU Scaling of Direct Sparse Linear System Solver for Finite-Difference Frequency-Domain Photonic Simulation 1 Cheng-Han Du* I-Hsin Chung** Weichung Wang* * I n s t i t u t e o f A p p l i e d M a t h e m a t i c a l S c i e n c e s N a t i o n a l T a i w a n U n i v e r s i t y T a i p e i, T a i w a n * * I B M T. J. W a t s o n R e s e a r c h C e n t e r N Y, U S
2 Outline 2 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
3 Introduction (Ref: Sun et al., Nature 528, 2015) 3 Photonics Waveguides Resonant cavities Frequency filters Plasmonic devices Design concerns Structural characteristics Parameter refinement Experiment data (Ref: Ivinskaya & Lavrinenko, 2011)
4 Introduction - Why Multi-GPU Scaling Global supercomputing trend High energy efficiency Growing popularity in deep learning applications Integration of high-performance numerical simulation and deep learning Source: ORNL 4 Source: NVIDIA
5 Introduction 5 Machine-Learning-Derived Behavior Model and Intelligent Design Photonic Integrated Circuit Design Broadband Spectral Analysis Nonlinear Equations with Multiphysics Features Photonic Crystal Analyzer Shift-Inverse Eigensolver Preconditioner and Algorithm for Iterative Side-Equation Solver Parallel Direct FDFD Solver Kernel
6 Introduction 6 Machine-Learning-Derived Behavior Model Photonic Integrated Circuit Design Broadband Spectral Analysis Nonlinear Equations with Multiphysics Features Photonic Crystal Analyzer Shift-Inverse Eigensolver Preconditioner and Algorithm for Iterative Side-Equation Solver When iterative solver fails Parallel Direct FDFD Solver Kernel
7 Objectives Introduction Fast generation of numerical data for different parameters Data-driven intelligent design of optical components Explicit and fast acquisition of quantitative characteristics Reduction of postprocessing and data storage/transfer requirement 7 Finite-Difference Frequency-Domain Parallel Direct FDFD Solver Kernel
8 Outline 8 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
9 FDFD Problem Implementation Linear system E + k 2 0 ε r E = cԧj Direct solver for robust solution Yee s mesh Perfectly-matched layer High-frequency problem Challenge Heavy factorization loads 9 Parallel Direct FDFD Solver Kernel
10 Implementation Compressed hierarchical Schur method (CHiS) Domain decomposition, multi-level algorithm 3D nested dissection of Yee s mesh (N x N y N z ) Ideal periodic structure D 1 = D 2 = D 3 = = D 16 S 1,1 = S 1,2 = S 1,3 = = S 1,8 S 2,1 = S 2,2 = S 2,3 = S 2,4 S 3,1 = S 3,2 S 4,1 10
11 Implementation Compressed hierarchical Schur method Elimination tree deduplication Diagonals Interfaces to children 11 I U I L
12 Implementation Compressed hierarchical Schur method Elimination tree deduplication Diagonals Interfaces to children 12
13 Implementation Compressed hierarchical Schur method Leaf-level Interface Compression (LIC) Use one updating submatrix over multiple Schur complement submatrices with row/column permutations. The less sparse matrix computing, the less CPU-centric load 13
14 Implementation Compressed Hierarchical Schur method Expose larger chunks of matrix computation Major function calls and libraries Subdomains 14 Sparse diagonal: Sparse factorize Sparse interface: Sparse LS solve and matrix multiply Separators Dense diagonal: Dense LU (Option 1) PARDISO, Sparse BLAS (Option 2) MUMPS Packed dense interface: Dense LS solve and matrix multiply Hardware Acceleration (GPU: cublas, cusolver, etc.) BLAS (ZGEMM) and LAPACK (ZGETRF, ZGETRS)
15 GPU acceleration Implementation Considerations Multi-GPU scaling in single node (Scale-up) No longer solely based on nested dissection Asynchronous streams for small submatrices Overlapping some computation kernels Hardware scheduling Threaded GPU controls Thread affinity 15
16 Implementation GPU acceleration 16 Factorize all diagonal blocks S i,j related to level i. (CPU or GPU work.)
17 Implementation GPU acceleration 17 Asynchronously send some blocks to GPU and perform S 1 i,j I U
18 GPU acceleration Implementation 18 Continue to ZGEMM, no D2H data transmission S 1 i,j I U kept in GPU for I L S 1 i,j I U operation later. Workspace will be simply discarded if no longer needed.
19 Implementation GPU acceleration 19 Asynchronously perform ZGEMM I L (S 1 i,j I U )
20 Implementation GPU acceleration 20 Collect I L (S 1 i,j I U ) from all GPUs and perform higher-level Schur update by CPU
21 Implementation GPU acceleration 21 Continue more ZGEMM I L (S 1 i,j I U ) related to (S 1 i,j I U ) and Schur updates
22 GPU acceleration Workload balance for multi-gpu Distribute I U blocks by parent levels Tackle extreme cases with lots of duplicates Minor increase in H2D transfer Implementation 22
23 GPU acceleration Workload balance for multi-gpu Panel I U Each I U column should be large enough Multiple I L copies sent to GPUs Moderate increase in H2D transfer Implementation 23
24 Implementation 24 GPU acceleration Without workload balance Finishing time > 325 seconds
25 Implementation 25 GPU acceleration With workload balance Finishing time < 250 seconds
26 Outline 26 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
27 Hardware specifications Numerical Results I Server Brillante P8Exp CPU 2 Intel E v cores used Memory 256 GB 1 TB 27 2 IBM Power cores used GPU 2 K40 4 K80 Software Intel Parallel Studio 2016 update 1 Intel PARDISO IBM ESSL and Parallel ESSL IBM XL Fortran and XL C Compiler CUDA 7.5 MUMPS CUDA 7.5
28 SOI dielectric waveguide Numerical Results I Total grids: , 2,948,517 in matrix dimension Wavelength: 1.5 μm Grid size: 0.02 μm 100 GB RAM 28
29 Numerical Results I 29 Brillante: 2 K40 ZGETRS + ZGEMM seconds (90% overall time)
30 Naïve GPU acceleration yields good speedup due to high AI. Scatter time includes D2H transfer. Brillante: 2 K40 Numerical Results I 30
31 Brillante: 2 K40 Numerical Results I Async streams apply to low-level 31 separators, which is finished in seconds even in CPU-only mode.
32 Brillante: 2 K40 Numerical Results I 32 Workload balance yields better speedup and multi-gpu scaling.
33 Numerical Results I P8Exp: 4 K80 with autoboost 33 Good performance scaling in quad-k80 server Higher performance with half-k80 computing Two threads competing single PCI-E bandwidth when using full-k80
34 Numerical Results I P8Exp: 4 K80 with autoboost 34 AccTRSMM: multi-gpu scaling Increased H2D transfer due to multiple I L copies to worksharing GPUs We still get acceptable scaling performance
35 Numerical Results I Periodic air hole wavelength filter No propagation at λ 0 = 1.5 μm Total grids: , 6,404,925 in matrix dimension 188 GB RAM 35
36 Brillante: 2 K40 Numerical Results I 36
37 Numerical Results I P8Exp: 4 K80 with autoboost 37
38 Numerical Results I P8Exp: GPU-scaling of AccTRSMM Much more dense matrix operations Good scaling in multi-gpu systems 38
39 Outline 39 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
40 P2P Matrix Sharing 40 Improved multi-gpu scaling with P2P transfer Past: Multiple I L copies sent to work-sharing GPUs Growing H2D transfer with increasing GPU sharing Major bottleneck for multi-p100 acceleration No cublas-xt: some matrix contents already distributed in GPUs S 1 Broadcast
41
42 P2P Matrix Sharing 42 Improved multi-gpu scaling with P2P transfer I L division cudamemcpypeerasync Threaded GPU control with busy-waiting S 1 division I U is shared with identical S 1 Expectation Replace massive H2D with P2P Reduced H2D transmission Other improvements Asynchronous D2H transfer right after ZGEMM S 1 D2H will be counted in AccTRSMM time in our P2P scheme
43 Outline 43 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
44 IntelExp Numerical Results II 2 Intel E v4 (20 physical cores) 8 Tesla P100 with 16 GB device memory PCI-E switch enclosure No NVLink DGX-1 2 Intel E v4 (40 physical cores) 8 NVLink-enabled Tesla P100 44
45 IntelExp Numerical Results II PCI-E enclosure on one CPU (experimental build) Aggregate CPU-GPU bandwidth: 10~12 GB/s (Uni-direction) GPU-GPU link bandwidth: 12.5 GB/s (Uni-direction) 45 CPU0 CPU1 GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7
46 IntelExp: 4GPU Numerical Results II Consistent PCI-E speed between GPUs at 12.5 GB/s Saturated CPU-GPU link 46
47 IntelExp: 8GPU Numerical Results II Some GPU links slow down by half Heavy congestion between CPU-GPU 47
48 Numerical Results II IntelExp: SOI waveguide simulation 48
49 Numerical Results II IntelExp: AccTRSMM Speedup (SOI waveguide) 49
50 Numerical Results II GPU AccTRSMM in SOI waveguide case 50 Great scaling performance in computing H2D and D2H transfer becomes the major scaling bottleneck P2P sharing eliminates H2D growth in multi-gpu Total H2D (GB) Total D2H (GB) AccTRSMM time (seconds) AccTRSMM scale No-P2P W/P2P No-P2P W/P2P No-P2P W/P2P No-P2P W/P2P 1-GPU X 1.00X 2-GPU X 1.63X 4-GPU X 2.18X 8-GPU X 2.50X
51 Numerical Results II IntelExp: Periodic air hole wavelength filter 51
52 Numerical Results II IntelExp: AccTRSMM Speedup (Air hole filter) 52
53 Numerical Results II GPU AccTRSMM in filter case 53 Great scaling performance in computing H2D and D2H transfer becomes the major scaling bottleneck P2P sharing eliminates H2D growth in multi-gpu Total H2D (GB) Total D2H (GB) AccTRSMM time (seconds) AccTRSMM scale No-P2P W/P2P No-P2P W/P2P No-P2P W/P2P No-P2P W/P2P 1-GPU X 1.00X 2-GPU X 1.71X 4-GPU X 2.37X 8-GPU X 2. 81X
54 DGX-1 Numerical Results II 54 Doubled CPU-GPU bandwidth in multi-gpu computing Aggregate bandwidth: 24 GB/s (Uni-direction) NVLink Up to 20GB/s (Uni-direction) Over 18GB/s in profiler Source: NVIDIA
55 Numerical Results II 55 DGX-1: SOI waveguide simulation Strange CPU behavior with OpenMP under investigation
56 Numerical Results II DGX-1: AccTRSMM (SOI waveguide) 56
57 Numerical Results II DGX-1 AccTRSMM in SOI waveguide case 57 Significant speedup from H2D and D2H (Double CPU-GPU links) NVLink further reduces sharing overheads NVLink between CPU-GPU? AccTRSMM time (seconds) AccTRSMM scale DGX1 IntelExp DGX1 IntelExp 1-GPU X 1.00X 2-GPU X 1.63X 4-GPU X 2.18X 8-GPU X 2.50X From (24 Haswell cores) to 35.3 seconds Over 12. 4X speedup
58 Outline 58 Introduction Implementation Numerical Results I P2P Matrix Sharing Numerical Results II Summary
59 Summary CHiS solver for 3D photonic simulation with multi-gpu FLOP, time, and memory saving: CPU-GPU traffic reduced Dense LA functions: ready for modern HPC architecture Sparse LA functions: SpMM, sparse LS solver Balanced multi-gpu acceleration with asynchronous data transfer and matrix computations P2P transfer: great computation scaling up to 8 GPUs Successful harnessing high-density GPU-accelerated systems Fast transfer between CPU-GPU MPI implementation in progress Fit computation task unit into GPU Maintain resource saving and scheduling and expose parallelization simultaneously 59
60 IBM Research NVIDIA Taiwan NVAITC Program Acknowledgement 60 Thank you!
GPU ACCELERATION OF WSMP (WATSON SPARSE MATRIX PACKAGE)
GPU ACCELERATION OF WSMP (WATSON SPARSE MATRIX PACKAGE) NATALIA GIMELSHEIN ANSHUL GUPTA STEVE RENNICH SEID KORIC NVIDIA IBM NVIDIA NCSA WATSON SPARSE MATRIX PACKAGE (WSMP) Cholesky, LDL T, LU factorization
More informationQuantum ESPRESSO on GPU accelerated systems
Quantum ESPRESSO on GPU accelerated systems Massimiliano Fatica, Everett Phillips, Josh Romero - NVIDIA Filippo Spiga - University of Cambridge/ARM (UK) MaX International Conference, Trieste, Italy, January
More informationParallelization of DQMC Simulations for Strongly Correlated Electron Systems
Parallelization of DQMC Simulations for Strongly Correlated Electron Systems Che-Rung Lee Dept. of Computer Science National Tsing-Hua University Taiwan joint work with I-Hsin Chung (IBM Research), Zhaojun
More informationCMSC 714 Lecture 6 MPI vs. OpenMP and OpenACC. Guest Lecturer: Sukhyun Song (original slides by Alan Sussman)
CMSC 714 Lecture 6 MPI vs. OpenMP and OpenACC Guest Lecturer: Sukhyun Song (original slides by Alan Sussman) Parallel Programming with Message Passing and Directives 2 MPI + OpenMP Some applications can
More informationGTC 2013: DEVELOPMENTS IN GPU-ACCELERATED SPARSE LINEAR ALGEBRA ALGORITHMS. Kyle Spagnoli. Research EM Photonics 3/20/2013
GTC 2013: DEVELOPMENTS IN GPU-ACCELERATED SPARSE LINEAR ALGEBRA ALGORITHMS Kyle Spagnoli Research Engineer @ EM Photonics 3/20/2013 INTRODUCTION» Sparse systems» Iterative solvers» High level benchmarks»
More informationOn Level Scheduling for Incomplete LU Factorization Preconditioners on Accelerators
On Level Scheduling for Incomplete LU Factorization Preconditioners on Accelerators Karl Rupp, Barry Smith rupp@mcs.anl.gov Mathematics and Computer Science Division Argonne National Laboratory FEMTEC
More informationIntroduction to parallel Computing
Introduction to parallel Computing VI-SEEM Training Paschalis Paschalis Korosoglou Korosoglou (pkoro@.gr) (pkoro@.gr) Outline Serial vs Parallel programming Hardware trends Why HPC matters HPC Concepts
More informationBuilding NVLink for Developers
Building NVLink for Developers Unleashing programmatic, architectural and performance capabilities for accelerated computing Why NVLink TM? Simpler, Better and Faster Simplified Programming No specialized
More informationGPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis
GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis Abstract: Lower upper (LU) factorization for sparse matrices is the most important computing step for circuit simulation
More informationMICROWAY S NVIDIA TESLA V100 GPU SOLUTIONS GUIDE
MICROWAY S NVIDIA TESLA V100 GPU SOLUTIONS GUIDE LEVERAGE OUR EXPERTISE sales@microway.com http://microway.com/tesla NUMBERSMASHER TESLA 4-GPU SERVER/WORKSTATION Flexible form factor 4 PCI-E GPUs + 3 additional
More informationSpeedup Altair RADIOSS Solvers Using NVIDIA GPU
Innovation Intelligence Speedup Altair RADIOSS Solvers Using NVIDIA GPU Eric LEQUINIOU, HPC Director Hongwei Zhou, Senior Software Developer May 16, 2012 Innovation Intelligence ALTAIR OVERVIEW Altair
More informationA GPU Sparse Direct Solver for AX=B
1 / 25 A GPU Sparse Direct Solver for AX=B Jonathan Hogg, Evgueni Ovtchinnikov, Jennifer Scott* STFC Rutherford Appleton Laboratory 26 March 2014 GPU Technology Conference San Jose, California * Thanks
More informationCafeGPI. Single-Sided Communication for Scalable Deep Learning
CafeGPI Single-Sided Communication for Scalable Deep Learning Janis Keuper itwm.fraunhofer.de/ml Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern, Germany Deep Neural Networks
More informationStudy and implementation of computational methods for Differential Equations in heterogeneous systems. Asimina Vouronikoy - Eleni Zisiou
Study and implementation of computational methods for Differential Equations in heterogeneous systems Asimina Vouronikoy - Eleni Zisiou Outline Introduction Review of related work Cyclic Reduction Algorithm
More informationUsing multifrontal hierarchically solver and HPC systems for 3D Helmholtz problem
Using multifrontal hierarchically solver and HPC systems for 3D Helmholtz problem Sergey Solovyev 1, Dmitry Vishnevsky 1, Hongwei Liu 2 Institute of Petroleum Geology and Geophysics SB RAS 1 EXPEC ARC,
More informationHow to perform HPL on CPU&GPU clusters. Dr.sc. Draško Tomić
How to perform HPL on CPU&GPU clusters Dr.sc. Draško Tomić email: drasko.tomic@hp.com Forecasting is not so easy, HPL benchmarking could be even more difficult Agenda TOP500 GPU trends Some basics about
More informationCUDA Experiences: Over-Optimization and Future HPC
CUDA Experiences: Over-Optimization and Future HPC Carl Pearson 1, Simon Garcia De Gonzalo 2 Ph.D. candidates, Electrical and Computer Engineering 1 / Computer Science 2, University of Illinois Urbana-Champaign
More information3D ADI Method for Fluid Simulation on Multiple GPUs. Nikolai Sakharnykh, NVIDIA Nikolay Markovskiy, NVIDIA
3D ADI Method for Fluid Simulation on Multiple GPUs Nikolai Sakharnykh, NVIDIA Nikolay Markovskiy, NVIDIA Introduction Fluid simulation using direct numerical methods Gives the most accurate result Requires
More informationHigh performance Computing and O&G Challenges
High performance Computing and O&G Challenges 2 Seismic exploration challenges High Performance Computing and O&G challenges Worldwide Context Seismic,sub-surface imaging Computing Power needs Accelerating
More informationFrequency Scaling and Energy Efficiency regarding the Gauss-Jordan Elimination Scheme on OpenPower 8
Frequency Scaling and Energy Efficiency regarding the Gauss-Jordan Elimination Scheme on OpenPower 8 Martin Köhler Jens Saak 2 The Gauss-Jordan Elimination scheme is an alternative to the LU decomposition
More informationsimulation framework for piecewise regular grids
WALBERLA, an ultra-scalable multiphysics simulation framework for piecewise regular grids ParCo 2015, Edinburgh September 3rd, 2015 Christian Godenschwager, Florian Schornbaum, Martin Bauer, Harald Köstler
More informationHARNESSING IRREGULAR PARALLELISM: A CASE STUDY ON UNSTRUCTURED MESHES. Cliff Woolley, NVIDIA
HARNESSING IRREGULAR PARALLELISM: A CASE STUDY ON UNSTRUCTURED MESHES Cliff Woolley, NVIDIA PREFACE This talk presents a case study of extracting parallelism in the UMT2013 benchmark for 3D unstructured-mesh
More informationAccelerating GPU computation through mixed-precision methods. Michael Clark Harvard-Smithsonian Center for Astrophysics Harvard University
Accelerating GPU computation through mixed-precision methods Michael Clark Harvard-Smithsonian Center for Astrophysics Harvard University Outline Motivation Truncated Precision using CUDA Solving Linear
More informationEfficient Multi-GPU CUDA Linear Solvers for OpenFOAM
Efficient Multi-GPU CUDA Linear Solvers for OpenFOAM Alexander Monakov, amonakov@ispras.ru Institute for System Programming of Russian Academy of Sciences March 20, 2013 1 / 17 Problem Statement In OpenFOAM,
More informationAsynchronous OpenCL/MPI numerical simulations of conservation laws
Asynchronous OpenCL/MPI numerical simulations of conservation laws Philippe HELLUY 1,3, Thomas STRUB 2. 1 IRMA, Université de Strasbourg, 2 AxesSim, 3 Inria Tonus, France IWOCL 2015, Stanford Conservation
More informationApplications of Berkeley s Dwarfs on Nvidia GPUs
Applications of Berkeley s Dwarfs on Nvidia GPUs Seminar: Topics in High-Performance and Scientific Computing Team N2: Yang Zhang, Haiqing Wang 05.02.2015 Overview CUDA The Dwarfs Dynamic Programming Sparse
More informationOptimising the Mantevo benchmark suite for multi- and many-core architectures
Optimising the Mantevo benchmark suite for multi- and many-core architectures Simon McIntosh-Smith Department of Computer Science University of Bristol 1 Bristol's rich heritage in HPC The University of
More informationHigh-Order Finite-Element Earthquake Modeling on very Large Clusters of CPUs or GPUs
High-Order Finite-Element Earthquake Modeling on very Large Clusters of CPUs or GPUs Gordon Erlebacher Department of Scientific Computing Sept. 28, 2012 with Dimitri Komatitsch (Pau,France) David Michea
More informationAdaptive-Mesh-Refinement Hydrodynamic GPU Computation in Astrophysics
Adaptive-Mesh-Refinement Hydrodynamic GPU Computation in Astrophysics H. Y. Schive ( 薛熙于 ) Graduate Institute of Physics, National Taiwan University Leung Center for Cosmology and Particle Astrophysics
More informationGPU COMPUTING WITH MSC NASTRAN 2013
SESSION TITLE WILL BE COMPLETED BY MSC SOFTWARE GPU COMPUTING WITH MSC NASTRAN 2013 Srinivas Kodiyalam, NVIDIA, Santa Clara, USA THEME Accelerated computing with GPUs SUMMARY Current trends in HPC (High
More informationS0432 NEW IDEAS FOR MASSIVELY PARALLEL PRECONDITIONERS
S0432 NEW IDEAS FOR MASSIVELY PARALLEL PRECONDITIONERS John R Appleyard Jeremy D Appleyard Polyhedron Software with acknowledgements to Mark A Wakefield Garf Bowen Schlumberger Outline of Talk Reservoir
More informationAlgorithms, System and Data Centre Optimisation for Energy Efficient HPC
2015-09-14 Algorithms, System and Data Centre Optimisation for Energy Efficient HPC Vincent Heuveline URZ Computing Centre of Heidelberg University EMCL Engineering Mathematics and Computing Lab 1 Energy
More informationPerformance Benefits of NVIDIA GPUs for LS-DYNA
Performance Benefits of NVIDIA GPUs for LS-DYNA Mr. Stan Posey and Dr. Srinivas Kodiyalam NVIDIA Corporation, Santa Clara, CA, USA Summary: This work examines the performance characteristics of LS-DYNA
More informationGenerating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory
Generating Efficient Data Movement Code for Heterogeneous Architectures with Distributed-Memory Roshan Dathathri Thejas Ramashekar Chandan Reddy Uday Bondhugula Department of Computer Science and Automation
More informationACCELERATING CFD AND RESERVOIR SIMULATIONS WITH ALGEBRAIC MULTI GRID Chris Gottbrath, Nov 2016
ACCELERATING CFD AND RESERVOIR SIMULATIONS WITH ALGEBRAIC MULTI GRID Chris Gottbrath, Nov 2016 Challenges What is Algebraic Multi-Grid (AMG)? AGENDA Why use AMG? When to use AMG? NVIDIA AmgX Results 2
More informationPhD Student. Associate Professor, Co-Director, Center for Computational Earth and Environmental Science. Abdulrahman Manea.
Abdulrahman Manea PhD Student Hamdi Tchelepi Associate Professor, Co-Director, Center for Computational Earth and Environmental Science Energy Resources Engineering Department School of Earth Sciences
More informationX10 specific Optimization of CPU GPU Data transfer with Pinned Memory Management
X10 specific Optimization of CPU GPU Data transfer with Pinned Memory Management Hideyuki Shamoto, Tatsuhiro Chiba, Mikio Takeuchi Tokyo Institute of Technology IBM Research Tokyo Programming for large
More informationPARDISO Version Reference Sheet Fortran
PARDISO Version 5.0.0 1 Reference Sheet Fortran CALL PARDISO(PT, MAXFCT, MNUM, MTYPE, PHASE, N, A, IA, JA, 1 PERM, NRHS, IPARM, MSGLVL, B, X, ERROR, DPARM) 1 Please note that this version differs significantly
More informationOptimizing Out-of-Core Nearest Neighbor Problems on Multi-GPU Systems Using NVLink
Optimizing Out-of-Core Nearest Neighbor Problems on Multi-GPU Systems Using NVLink Rajesh Bordawekar IBM T. J. Watson Research Center bordaw@us.ibm.com Pidad D Souza IBM Systems pidsouza@in.ibm.com 1 Outline
More informationBrief notes on setting up semi-high performance computing environments. July 25, 2014
Brief notes on setting up semi-high performance computing environments July 25, 2014 1 We have two different computing environments for fitting demanding models to large space and/or time data sets. 1
More informationMAGMA a New Generation of Linear Algebra Libraries for GPU and Multicore Architectures
MAGMA a New Generation of Linear Algebra Libraries for GPU and Multicore Architectures Stan Tomov Innovative Computing Laboratory University of Tennessee, Knoxville OLCF Seminar Series, ORNL June 16, 2010
More informationEvaluation of Asynchronous Offloading Capabilities of Accelerator Programming Models for Multiple Devices
Evaluation of Asynchronous Offloading Capabilities of Accelerator Programming Models for Multiple Devices Jonas Hahnfeld 1, Christian Terboven 1, James Price 2, Hans Joachim Pflug 1, Matthias S. Müller
More informationIssues In Implementing The Primal-Dual Method for SDP. Brian Borchers Department of Mathematics New Mexico Tech Socorro, NM
Issues In Implementing The Primal-Dual Method for SDP Brian Borchers Department of Mathematics New Mexico Tech Socorro, NM 87801 borchers@nmt.edu Outline 1. Cache and shared memory parallel computing concepts.
More informationSparse Multifrontal Performance Gains via NVIDIA GPU January 16, 2009
Sparse Multifrontal Performance Gains via NVIDIA GPU January 16, 2009 Dan l Pierce, PhD, MBA, CEO & President AAI Joint with: Yukai Hung, Chia-Chi Liu, Yao-Hung Tsai, Weichung Wang, and David Yu Access
More informationHYPERDRIVE IMPLEMENTATION AND ANALYSIS OF A PARALLEL, CONJUGATE GRADIENT LINEAR SOLVER PROF. BRYANT PROF. KAYVON 15618: PARALLEL COMPUTER ARCHITECTURE
HYPERDRIVE IMPLEMENTATION AND ANALYSIS OF A PARALLEL, CONJUGATE GRADIENT LINEAR SOLVER AVISHA DHISLE PRERIT RODNEY ADHISLE PRODNEY 15618: PARALLEL COMPUTER ARCHITECTURE PROF. BRYANT PROF. KAYVON LET S
More informationCUDA Accelerated Linpack on Clusters. E. Phillips, NVIDIA Corporation
CUDA Accelerated Linpack on Clusters E. Phillips, NVIDIA Corporation Outline Linpack benchmark CUDA Acceleration Strategy Fermi DGEMM Optimization / Performance Linpack Results Conclusions LINPACK Benchmark
More informationMAGMA. Matrix Algebra on GPU and Multicore Architectures
MAGMA Matrix Algebra on GPU and Multicore Architectures Innovative Computing Laboratory Electrical Engineering and Computer Science University of Tennessee Piotr Luszczek (presenter) web.eecs.utk.edu/~luszczek/conf/
More informationFatMan vs. LittleBoy: Scaling up Linear Algebraic Operations in Scale-out Data Platforms
FatMan vs. LittleBoy: Scaling up Linear Algebraic Operations in Scale-out Data Platforms Luna Xu (Virginia Tech) Seung-Hwan Lim (ORNL) Ali R. Butt (Virginia Tech) Sreenivas R. Sukumar (ORNL) Ramakrishnan
More informationGAMER : a GPU-accelerated Adaptive-MEsh-Refinement Code for Astrophysics GPU 與自適性網格於天文模擬之應用與效能
GAMER : a GPU-accelerated Adaptive-MEsh-Refinement Code for Astrophysics GPU 與自適性網格於天文模擬之應用與效能 Hsi-Yu Schive ( 薛熙于 ), Tzihong Chiueh ( 闕志鴻 ), Yu-Chih Tsai ( 蔡御之 ), Ui-Han Zhang ( 張瑋瀚 ) Graduate Institute
More informationACCELERATING THE PRODUCTION OF SYNTHETIC SEISMOGRAMS BY A MULTICORE PROCESSOR CLUSTER WITH MULTIPLE GPUS
ACCELERATING THE PRODUCTION OF SYNTHETIC SEISMOGRAMS BY A MULTICORE PROCESSOR CLUSTER WITH MULTIPLE GPUS Ferdinando Alessi Annalisa Massini Roberto Basili INGV Introduction The simulation of wave propagation
More informationESPRESO ExaScale PaRallel FETI Solver. Hybrid FETI Solver Report
ESPRESO ExaScale PaRallel FETI Solver Hybrid FETI Solver Report Lubomir Riha, Tomas Brzobohaty IT4Innovations Outline HFETI theory from FETI to HFETI communication hiding and avoiding techniques our new
More informationGPU ACCELERATED DATABASE MANAGEMENT SYSTEMS
CIS 601 - Graduate Seminar Presentation 1 GPU ACCELERATED DATABASE MANAGEMENT SYSTEMS PRESENTED BY HARINATH AMASA CSU ID: 2697292 What we will talk about.. Current problems GPU What are GPU Databases GPU
More informationGPU Acceleration of the Longwave Rapid Radiative Transfer Model in WRF using CUDA Fortran. G. Ruetsch, M. Fatica, E. Phillips, N.
GPU Acceleration of the Longwave Rapid Radiative Transfer Model in WRF using CUDA Fortran G. Ruetsch, M. Fatica, E. Phillips, N. Juffa Outline WRF and RRTM Previous Work CUDA Fortran Features RRTM in CUDA
More informationSolving Dense Linear Systems on Graphics Processors
Solving Dense Linear Systems on Graphics Processors Sergio Barrachina Maribel Castillo Francisco Igual Rafael Mayo Enrique S. Quintana-Ortí High Performance Computing & Architectures Group Universidad
More informationSTRATEGIES TO ACCELERATE VASP WITH GPUS USING OPENACC. Stefan Maintz, Dr. Markus Wetzstein
STRATEGIES TO ACCELERATE VASP WITH GPUS USING OPENACC Stefan Maintz, Dr. Markus Wetzstein smaintz@nvidia.com; mwetzstein@nvidia.com Companies Academia VASP USERS AND USAGE 12-25% of CPU cycles @ supercomputing
More informationTuring Architecture and CUDA 10 New Features. Minseok Lee, Developer Technology Engineer, NVIDIA
Turing Architecture and CUDA 10 New Features Minseok Lee, Developer Technology Engineer, NVIDIA Turing Architecture New SM Architecture Multi-Precision Tensor Core RT Core Turing MPS Inference Accelerated,
More informationJ. Blair Perot. Ali Khajeh-Saeed. Software Engineer CD-adapco. Mechanical Engineering UMASS, Amherst
Ali Khajeh-Saeed Software Engineer CD-adapco J. Blair Perot Mechanical Engineering UMASS, Amherst Supercomputers Optimization Stream Benchmark Stag++ (3D Incompressible Flow Code) Matrix Multiply Function
More informationPerformance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA TESLA GPU Cluster
Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA TESLA GPU Cluster Veerendra Allada, Troy Benjegerdes Electrical and Computer Engineering, Ames Laboratory Iowa State University &
More informationSUPPLEMENTARY INFORMATION
Supplementary Information Compact spectrometer based on a disordered photonic chip Brandon Redding, Seng Fatt Liew, Raktim Sarma, Hui Cao* Department of Applied Physics, Yale University, New Haven, CT
More informationEvaluation of sparse LU factorization and triangular solution on multicore architectures. X. Sherry Li
Evaluation of sparse LU factorization and triangular solution on multicore architectures X. Sherry Li Lawrence Berkeley National Laboratory ParLab, April 29, 28 Acknowledgement: John Shalf, LBNL Rich Vuduc,
More informationCPU-GPU Heterogeneous Computing
CPU-GPU Heterogeneous Computing Advanced Seminar "Computer Engineering Winter-Term 2015/16 Steffen Lammel 1 Content Introduction Motivation Characteristics of CPUs and GPUs Heterogeneous Computing Systems
More informationOptimisation Myths and Facts as Seen in Statistical Physics
Optimisation Myths and Facts as Seen in Statistical Physics Massimo Bernaschi Institute for Applied Computing National Research Council & Computer Science Department University La Sapienza Rome - ITALY
More informationParallelization of Shortest Path Graph Kernels on Multi-Core CPUs and GPU
Parallelization of Shortest Path Graph Kernels on Multi-Core CPUs and GPU Lifan Xu Wei Wang Marco A. Alvarez John Cavazos Dongping Zhang Department of Computer and Information Science University of Delaware
More informationNVIDIA COLLECTIVE COMMUNICATION LIBRARY (NCCL)
NVIDIA COLLECTIVE COMMUNICATION LIBRARY (NCCL) RN-08645-000_v01 March 2018 Release Notes TABLE OF CONTENTS Chapter Chapter Chapter Chapter Chapter Chapter Chapter 1. 2. 3. 4. 5. 6. 7. NCCL NCCL NCCL NCCL
More informationHigh performance 2D Discrete Fourier Transform on Heterogeneous Platforms. Shrenik Lad, IIIT Hyderabad Advisor : Dr. Kishore Kothapalli
High performance 2D Discrete Fourier Transform on Heterogeneous Platforms Shrenik Lad, IIIT Hyderabad Advisor : Dr. Kishore Kothapalli Motivation Fourier Transform widely used in Physics, Astronomy, Engineering
More informationAccelerating the Iterative Linear Solver for Reservoir Simulation
Accelerating the Iterative Linear Solver for Reservoir Simulation Wei Wu 1, Xiang Li 2, Lei He 1, Dongxiao Zhang 2 1 Electrical Engineering Department, UCLA 2 Department of Energy and Resources Engineering,
More informationAnalyzing the Performance of IWAVE on a Cluster using HPCToolkit
Analyzing the Performance of IWAVE on a Cluster using HPCToolkit John Mellor-Crummey and Laksono Adhianto Department of Computer Science Rice University {johnmc,laksono}@rice.edu TRIP Meeting March 30,
More informationA Scalable Parallel LSQR Algorithm for Solving Large-Scale Linear System for Seismic Tomography
1 A Scalable Parallel LSQR Algorithm for Solving Large-Scale Linear System for Seismic Tomography He Huang, Liqiang Wang, Po Chen(University of Wyoming) John Dennis (NCAR) 2 LSQR in Seismic Tomography
More informationTechnology for a better society. hetcomp.com
Technology for a better society hetcomp.com 1 J. Seland, C. Dyken, T. R. Hagen, A. R. Brodtkorb, J. Hjelmervik,E Bjønnes GPU Computing USIT Course Week 16th November 2011 hetcomp.com 2 9:30 10:15 Introduction
More informationAccelerating Leukocyte Tracking Using CUDA: A Case Study in Leveraging Manycore Coprocessors
Accelerating Leukocyte Tracking Using CUDA: A Case Study in Leveraging Manycore Coprocessors Michael Boyer, David Tarjan, Scott T. Acton, and Kevin Skadron University of Virginia IPDPS 2009 Outline Leukocyte
More informationParticle-in-Cell Simulations on Modern Computing Platforms. Viktor K. Decyk and Tajendra V. Singh UCLA
Particle-in-Cell Simulations on Modern Computing Platforms Viktor K. Decyk and Tajendra V. Singh UCLA Outline of Presentation Abstraction of future computer hardware PIC on GPUs OpenCL and Cuda Fortran
More informationJohn Levesque Nov 16, 2001
1 We see that the GPU is the best device available for us today to be able to get to the performance we want and meet our users requirements for a very high performance node with very high memory bandwidth.
More informationIBM Power AC922 Server
IBM Power AC922 Server The Best Server for Enterprise AI Highlights More accuracy - GPUs access system RAM for larger models Faster insights - significant deep learning speedups Rapid deployment - integrated
More informationTrends in HPC (hardware complexity and software challenges)
Trends in HPC (hardware complexity and software challenges) Mike Giles Oxford e-research Centre Mathematical Institute MIT seminar March 13th, 2013 Mike Giles (Oxford) HPC Trends March 13th, 2013 1 / 18
More informationNVIDIA COLLECTIVE COMMUNICATION LIBRARY (NCCL)
NVIDIA COLLECTIVE COMMUNICATION LIBRARY (NCCL) RN-08645-000_v01 September 2018 Release Notes TABLE OF CONTENTS Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter Chapter 1. NCCL Overview...1
More informationPerformance impact of dynamic parallelism on different clustering algorithms
Performance impact of dynamic parallelism on different clustering algorithms Jeffrey DiMarco and Michela Taufer Computer and Information Sciences, University of Delaware E-mail: jdimarco@udel.edu, taufer@udel.edu
More informationIntel Performance Libraries
Intel Performance Libraries Powerful Mathematical Library Intel Math Kernel Library (Intel MKL) Energy Science & Research Engineering Design Financial Analytics Signal Processing Digital Content Creation
More informationPorting the NAS-NPB Conjugate Gradient Benchmark to CUDA. NVIDIA Corporation
Porting the NAS-NPB Conjugate Gradient Benchmark to CUDA NVIDIA Corporation Outline! Overview of CG benchmark! Overview of CUDA Libraries! CUSPARSE! CUBLAS! Porting Sequence! Algorithm Analysis! Data/Code
More informationAnalysis and Optimization of Power Consumption in the Iterative Solution of Sparse Linear Systems on Multi-core and Many-core Platforms
Analysis and Optimization of Power Consumption in the Iterative Solution of Sparse Linear Systems on Multi-core and Many-core Platforms H. Anzt, V. Heuveline Karlsruhe Institute of Technology, Germany
More informationHigh-Performance Data Loading and Augmentation for Deep Neural Network Training
High-Performance Data Loading and Augmentation for Deep Neural Network Training Trevor Gale tgale@ece.neu.edu Steven Eliuk steven.eliuk@gmail.com Cameron Upright c.upright@samsung.com Roadmap 1. The General-Purpose
More informationFast and reliable linear system solutions on new parallel architectures
Fast and reliable linear system solutions on new parallel architectures Marc Baboulin Université Paris-Sud Chaire Inria Saclay Île-de-France Séminaire Aristote - Ecole Polytechnique 15 mai 2013 Marc Baboulin
More informationScaling to Petaflop. Ola Torudbakken Distinguished Engineer. Sun Microsystems, Inc
Scaling to Petaflop Ola Torudbakken Distinguished Engineer Sun Microsystems, Inc HPC Market growth is strong CAGR increased from 9.2% (2006) to 15.5% (2007) Market in 2007 doubled from 2003 (Source: IDC
More informationGPGPUs in HPC. VILLE TIMONEN Åbo Akademi University CSC
GPGPUs in HPC VILLE TIMONEN Åbo Akademi University 2.11.2010 @ CSC Content Background How do GPUs pull off higher throughput Typical architecture Current situation & the future GPGPU languages A tale of
More informationME964 High Performance Computing for Engineering Applications
ME964 High Performance Computing for Engineering Applications Outlining Midterm Projects Topic 3: GPU-based FEA Topic 4: GPU Direct Solver for Sparse Linear Algebra March 01, 2011 Dan Negrut, 2011 ME964
More informationTesla GPU Computing A Revolution in High Performance Computing
Tesla GPU Computing A Revolution in High Performance Computing Mark Harris, NVIDIA Agenda Tesla GPU Computing CUDA Fermi What is GPU Computing? Introduction to Tesla CUDA Architecture Programming & Memory
More informationPerformance of Implicit Solver Strategies on GPUs
9. LS-DYNA Forum, Bamberg 2010 IT / Performance Performance of Implicit Solver Strategies on GPUs Prof. Dr. Uli Göhner DYNAmore GmbH Stuttgart, Germany Abstract: The increasing power of GPUs can be used
More informationLarge scale Imaging on Current Many- Core Platforms
Large scale Imaging on Current Many- Core Platforms SIAM Conf. on Imaging Science 2012 May 20, 2012 Dr. Harald Köstler Chair for System Simulation Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen,
More informationN-Body Simulation using CUDA. CSE 633 Fall 2010 Project by Suraj Alungal Balchand Advisor: Dr. Russ Miller State University of New York at Buffalo
N-Body Simulation using CUDA CSE 633 Fall 2010 Project by Suraj Alungal Balchand Advisor: Dr. Russ Miller State University of New York at Buffalo Project plan Develop a program to simulate gravitational
More informationTechnical Report Performance Analysis of CULA on different NVIDIA GPU Architectures. Prateek Gupta
Technical Report 2014-02 Performance Analysis of CULA on different NVIDIA GPU Architectures Prateek Gupta May 20, 2014 1 Spring 2014: Performance Analysis of CULA on different NVIDIA GPU Architectures
More informationPortable and Productive Performance on Hybrid Systems with libsci_acc Luiz DeRose Sr. Principal Engineer Programming Environments Director Cray Inc.
Portable and Productive Performance on Hybrid Systems with libsci_acc Luiz DeRose Sr. Principal Engineer Programming Environments Director Cray Inc. 1 What is Cray Libsci_acc? Provide basic scientific
More informationParallel H.264/AVC Motion Compensation for GPUs using OpenCL
Parallel H.264/AVC Motion Compensation for GPUs using OpenCL Biao Wang, Mauricio Alvarez-Mesa, Chi Ching Chi, Ben Juurlink Embedded Systems Architecture Technische Universität Berlin Berlin, Germany January
More informationIntroduction to Parallel and Distributed Computing. Linh B. Ngo CPSC 3620
Introduction to Parallel and Distributed Computing Linh B. Ngo CPSC 3620 Overview: What is Parallel Computing To be run using multiple processors A problem is broken into discrete parts that can be solved
More informationThe Future of High Performance Interconnects
The Future of High Performance Interconnects Ashrut Ambastha HPC Advisory Council Perth, Australia :: August 2017 When Algorithms Go Rogue 2017 Mellanox Technologies 2 When Algorithms Go Rogue 2017 Mellanox
More informationSYNERGIE VON HPC UND DEEP LEARNING MIT NVIDIA GPUS
SYNERGIE VON HPC UND DEEP LEARNING MIT NVIDIA S Axel Koehler, Principal Solution Architect HPCN%Workshop%Goettingen,%14.%Mai%2018 NVIDIA - AI COMPUTING COMPANY Computer Graphics Computing Artificial Intelligence
More informationAvailable online at ScienceDirect. Parallel Computational Fluid Dynamics Conference (ParCFD2013)
Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 61 ( 2013 ) 81 86 Parallel Computational Fluid Dynamics Conference (ParCFD2013) An OpenCL-based parallel CFD code for simulations
More informationComplexity and Advanced Algorithms. Introduction to Parallel Algorithms
Complexity and Advanced Algorithms Introduction to Parallel Algorithms Why Parallel Computing? Save time, resources, memory,... Who is using it? Academia Industry Government Individuals? Two practical
More informationRECENT TRENDS IN GPU ARCHITECTURES. Perspectives of GPU computing in Science, 26 th Sept 2016
RECENT TRENDS IN GPU ARCHITECTURES Perspectives of GPU computing in Science, 26 th Sept 2016 NVIDIA THE AI COMPUTING COMPANY GPU Computing Computer Graphics Artificial Intelligence 2 NVIDIA POWERS WORLD
More informationLecture 15: More Iterative Ideas
Lecture 15: More Iterative Ideas David Bindel 15 Mar 2010 Logistics HW 2 due! Some notes on HW 2. Where we are / where we re going More iterative ideas. Intro to HW 3. More HW 2 notes See solution code!
More informationIBM Deep Learning Solutions
IBM Deep Learning Solutions Reference Architecture for Deep Learning on POWER8, P100, and NVLink October, 2016 How do you teach a computer to Perceive? 2 Deep Learning: teaching Siri to recognize a bicycle
More informationPorting Scalable Parallel CFD Application HiFUN on NVIDIA GPU
Porting Scalable Parallel CFD Application NVIDIA D. V., N. Munikrishna, Nikhil Vijay Shende 1 N. Balakrishnan 2 Thejaswi Rao 3 1. S & I Engineering Solutions Pvt. Ltd., Bangalore, India 2. Aerospace Engineering,
More information