High-Performance and Scalable Non-Blocking All-to-All with Collective Offload on InfiniBand Clusters: A study with Parallel 3DFFT
|
|
- Griselda Little
- 6 years ago
- Views:
Transcription
1 High-Performance and Scalable Non-Blocking All-to-All with Collective Offload on InfiniBand Clusters: A study with Parallel 3DFFT Krishna Kandalla (1), Hari Subramoni (1), Karen Tomko (2), Dmitry Pekurovsky (3), Sayantan Sur (1) and Dhabaleswar. K. Panda (1) (1) Computer Science & Engineering Department, The Ohio State University (2) The Ohio Supercomputer Center (3) San Diego Supercomputer Center
2 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 2
3 Introduction Parallel applications can scale beyond 100,000 cores InfiniBand is commonly used across commodity clusters Message Passing Interface (MPI) is the de-facto programming model Tsubame Supercomputer 73,278 cores 3
4 Collective Communication in MPI MPI-2.2 defines blocking collective operations limits performance and scalability of dense operations(alltoall) MPI-3 may support non-blocking collectives Hoefler et. al, proposed host-based approaches Latest ConnectX-2 adapters from Mellanox supports network offload features Study benefits with real scientific libraries P3DFFT 4
5 Overview of InfiniBand MQ M C Q Send Send CQ Wait Application Task List Send InfiniBand HCA Send Q Recv Q Collective Offload Applications can offload task-lists Send Send Wait to the NIC A CQE gets created on the MCQ after execution Problems: Alltoall is extremely communication intensive Size of task-list is limited Directly affects Alltoall scalability Physical Link Data Recv CQ Subramoni et. Al, Hot Interconnects 2010 HotI '10 5
6 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 6
7 Motivation MPI_Ialtoall Compute Computation Overlap? MPI_Wait Rank0 Rank1 Rank n Challenge: progress the collective schedules in an asynchronous manner with: performance portability minimal host processor intervention acceptable communication latency 7
8 Overlap (Higher is Better) Design Space of Collective Algorithms Blocking Coll Non-Blocking Coll (Host) Non-Blocking Coll (Offload) Latency (Lower is Better) Portability (Higher is Better)
9 Problem Statement Can we leverage network offload to design MPI_Ialltoall? Will network offload help overlap with collectives? Can network offload improve application throughput? Can we re-design scientific libraries(such as 3DFFT) to leverage our proposed MPI_Ialltoall? 9
10 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 10
11 Creating Task-lists with the trigger operation Trigger Application Task List Wait Send Send Send Send Task list with multiple phases A phase has send, wait & trigger tasks Progress thread calls ibv_get_cq_event() and blocks Trigger task generates an interrupt which signals the progress thread MQ M C Q Trigger Send CQ InfiniBand HCA Send Q Wait Send Send Recv Q Send Send Physical Link Recv CQ Trigger Send CQ Trigger Recv CQ 11
12 Designing Scalable Offload Alltoall Application Thread MPI_Init PI_Ialltoall MPI_Ialltoall returns Create Task-List Offload Progress Thread Post-list; ibv_get_cq _event() Compute Alltoall Complete Trigger; Post-list; Ibv_get_cq _event() MPI_Wait Trigger; End of list; 12
13 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 13
14 Parallel 3DFFT Library Applications in areas related to simulations of turbulence, Astrophysics etc. rely heavily on 3DFFT P3DFFT from San Diego Supercomputer Center (SDSC) is a portable, high performance implementation of 3DFFT ( P3DFFT uses a 2D pencil decomposition to maximize parallelism P3DFFT relies on expensive large message Alltoall operations to implement the transpose operations 14
15 Re-designing P3DFFT for Overlap V1 V2 V3 X X Y ( Y YZ Z X X Y Y Y Z Z X X Y Y YZ Z Intra-Node Inter-Node A 1DFFT along A Dimension V1 A B V2 A-B Transpose X X Y Y X X Y Y Y Z (I) Z X X Y Y Z (I) Two Parallel Transpose Operations Y Z... Y Z (I) Two Parallel Transpose Operations 15
16 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 16
17 Experimental Setup Intel Xeon E5640 (2.53 GHz), 12 GB memory per node MT26428 QDR ConnectX-2 with PCI-Ex interfaces, 171- port Mellanox QDR switch, OFED RHEL 5.4, e15 kernel version MVAPICH2 A High Performance MPI implementation over InfiniBand and other RDMA networks (v1.6) Used by more than 1580 organizations world-wide 17
18 Micro-Benchmark Evaluations Overlap Benchmark Measure average MPI_Ialltoall latency Overlap Percentage: start_overlap_timer() MPI_Ialtoall(..) while(time < alltoall_latency) MPI_Wait(..) update timer end_overlap_timer() Throughput Benchmark start_throughput_timer() MPI_Ialtoall(..) CBLAS_DGEMM() MPI_Wait(..) end_throughput_timer() 18
19 Overlap Percentage (%) Communication/Computation Overlap 16K 32K 64K 128K 256K 512K 1M Message Length (Bytes) Alltoall-Offload Alltoall-Host-Based-Test-10 Alltoall-Host-Based-Test-1000 Alltoall-Host-Based-Test-5000 Alltoall Overlap Comparison with 256 Processes Alltoall-Offload delivers near perfect communication/computation overlap for all messages in a portable manner 19
20 Throughput (GFLOPS) DGEMM Throughput Comparison Serial CBLAS-DGEMM Problem size (N) Alltoall-Offload Host-Based Theoretical Peak CBLAS-DGEMM overlapped with Offload-Ialltoall delivers better throughput upto 110% when compared to Host-Based Ialltoall with 512 processes 20
21 Latency (msec) Latency Comparison Alltoall-Default-Host Alltoall-Offload Alltoall-Host-Based Alltoall-Host-Based-Thread K 32K 64K 128K 256K 512K 1M Message Length (Bytes) Alltoall Latency Comparison with 256 Processes Alltoall-Offload delivers good overlap, without sacrificing on communication latency! 21
22 Run-Time (s) Parallel 3D FFT Performance Blocking H-Test Offload 23% ` Problem Size P3DFFT Application kernel run-time Comparison with 128 processes P3DFFT with Offload-Ialltoall performs about 13.5% better than default P3DFFT and about 12% better than P3DFFT with Host-based-Test ` 13% Lower is Better 22
23 Outline Introduction Problem Statement Designing MPI_Ialltoall with Collective Offload Re-designing P3DFFT for overlap Experimental Evaluation Conclusions and Future work 23
24 Conclusions and Future Work Proposed MPI_Ialltoall shows near perfect (99%) overlap Throughput of applications improves significantly through offload-based non-blocking collectives P3DFFT s run-time improved by up to 23% Future work: Extend Offload-based techniques for other MPI collectives and study their benefits with real applications Support for Offload-based collectives will be available in future MVAPICH2 releases 24
25 Thank you! (1) {kandalla, subramon, surs, (2) (3) (1) Network-Based Computing Laboratory, Ohio State University (2) The Ohio Supercomputer Center (3) San Diego Supercomputer Center 25
Designing Power-Aware Collective Communication Algorithms for InfiniBand Clusters
Designing Power-Aware Collective Communication Algorithms for InfiniBand Clusters Krishna Kandalla, Emilio P. Mancini, Sayantan Sur, and Dhabaleswar. K. Panda Department of Computer Science & Engineering,
More informationMPI Alltoall Personalized Exchange on GPGPU Clusters: Design Alternatives and Benefits
MPI Alltoall Personalized Exchange on GPGPU Clusters: Design Alternatives and Benefits Ashish Kumar Singh, Sreeram Potluri, Hao Wang, Krishna Kandalla, Sayantan Sur, and Dhabaleswar K. Panda Network-Based
More informationImproving Application Performance and Predictability using Multiple Virtual Lanes in Modern Multi-Core InfiniBand Clusters
Improving Application Performance and Predictability using Multiple Virtual Lanes in Modern Multi-Core InfiniBand Clusters Hari Subramoni, Ping Lai, Sayantan Sur and Dhabhaleswar. K. Panda Department of
More informationPerformance Analysis and Evaluation of Mellanox ConnectX InfiniBand Architecture with Multi-Core Platforms
Performance Analysis and Evaluation of Mellanox ConnectX InfiniBand Architecture with Multi-Core Platforms Sayantan Sur, Matt Koop, Lei Chai Dhabaleswar K. Panda Network Based Computing Lab, The Ohio State
More informationMemory Scalability Evaluation of the Next-Generation Intel Bensley Platform with InfiniBand
Memory Scalability Evaluation of the Next-Generation Intel Bensley Platform with InfiniBand Matthew Koop, Wei Huang, Ahbinav Vishnu, Dhabaleswar K. Panda Network-Based Computing Laboratory Department of
More informationReducing Network Contention with Mixed Workloads on Modern Multicore Clusters
Reducing Network Contention with Mixed Workloads on Modern Multicore Clusters Matthew Koop 1 Miao Luo D. K. Panda matthew.koop@nasa.gov {luom, panda}@cse.ohio-state.edu 1 NASA Center for Computational
More informationCan Memory-Less Network Adapters Benefit Next-Generation InfiniBand Systems?
Can Memory-Less Network Adapters Benefit Next-Generation InfiniBand Systems? Sayantan Sur, Abhinav Vishnu, Hyun-Wook Jin, Wei Huang and D. K. Panda {surs, vishnu, jinhy, huanwei, panda}@cse.ohio-state.edu
More informationSR-IOV Support for Virtualization on InfiniBand Clusters: Early Experience
SR-IOV Support for Virtualization on InfiniBand Clusters: Early Experience Jithin Jose, Mingzhe Li, Xiaoyi Lu, Krishna Kandalla, Mark Arnold and Dhabaleswar K. (DK) Panda Network-Based Computing Laboratory
More informationMulti-Threaded UPC Runtime for GPU to GPU communication over InfiniBand
Multi-Threaded UPC Runtime for GPU to GPU communication over InfiniBand Miao Luo, Hao Wang, & D. K. Panda Network- Based Compu2ng Laboratory Department of Computer Science and Engineering The Ohio State
More informationStudy. Dhabaleswar. K. Panda. The Ohio State University HPIDC '09
RDMA over Ethernet - A Preliminary Study Hari Subramoni, Miao Luo, Ping Lai and Dhabaleswar. K. Panda Computer Science & Engineering Department The Ohio State University Introduction Problem Statement
More informationHow to Boost the Performance of Your MPI and PGAS Applications with MVAPICH2 Libraries
How to Boost the Performance of Your MPI and PGAS s with MVAPICH2 Libraries A Tutorial at the MVAPICH User Group (MUG) Meeting 18 by The MVAPICH Team The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationDesigning High Performance Communication Middleware with Emerging Multi-core Architectures
Designing High Performance Communication Middleware with Emerging Multi-core Architectures Dhabaleswar K. (DK) Panda Department of Computer Science and Engg. The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationLiMIC: Support for High-Performance MPI Intra-Node Communication on Linux Cluster
LiMIC: Support for High-Performance MPI Intra-Node Communication on Linux Cluster H. W. Jin, S. Sur, L. Chai, and D. K. Panda Network-Based Computing Laboratory Department of Computer Science and Engineering
More informationDesigning High Performance DSM Systems using InfiniBand Features
Designing High Performance DSM Systems using InfiniBand Features Ranjit Noronha and Dhabaleswar K. Panda The Ohio State University NBC Outline Introduction Motivation Design and Implementation Results
More informationDesigning High-Performance MPI Collectives in MVAPICH2 for HPC and Deep Learning
5th ANNUAL WORKSHOP 209 Designing High-Performance MPI Collectives in MVAPICH2 for HPC and Deep Learning Hari Subramoni Dhabaleswar K. (DK) Panda The Ohio State University The Ohio State University E-mail:
More informationIntra-MIC MPI Communication using MVAPICH2: Early Experience
Intra-MIC MPI Communication using MVAPICH: Early Experience Sreeram Potluri, Karen Tomko, Devendar Bureddy, and Dhabaleswar K. Panda Department of Computer Science and Engineering Ohio State University
More informationIn the multi-core age, How do larger, faster and cheaper and more responsive memory sub-systems affect data management? Dhabaleswar K.
In the multi-core age, How do larger, faster and cheaper and more responsive sub-systems affect data management? Panel at ADMS 211 Dhabaleswar K. (DK) Panda Network-Based Computing Laboratory Department
More informationDesign Alternatives for Implementing Fence Synchronization in MPI-2 One-Sided Communication for InfiniBand Clusters
Design Alternatives for Implementing Fence Synchronization in MPI-2 One-Sided Communication for InfiniBand Clusters G.Santhanaraman, T. Gangadharappa, S.Narravula, A.Mamidala and D.K.Panda Presented by:
More informationGPU- Aware Design, Implementation, and Evaluation of Non- blocking Collective Benchmarks
GPU- Aware Design, Implementation, and Evaluation of Non- blocking Collective Benchmarks Presented By : Esthela Gallardo Ammar Ahmad Awan, Khaled Hamidouche, Akshay Venkatesh, Jonathan Perkins, Hari Subramoni,
More informationEfficient and Truly Passive MPI-3 RMA Synchronization Using InfiniBand Atomics
1 Efficient and Truly Passive MPI-3 RMA Synchronization Using InfiniBand Atomics Mingzhe Li Sreeram Potluri Khaled Hamidouche Jithin Jose Dhabaleswar K. Panda Network-Based Computing Laboratory Department
More informationAccelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures Mohammadreza Bayatpour, Hari Subramoni, D. K.
Accelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures Mohammadreza Bayatpour, Hari Subramoni, D. K. Panda Department of Computer Science and Engineering The Ohio
More informationApplication-Transparent Checkpoint/Restart for MPI Programs over InfiniBand
Application-Transparent Checkpoint/Restart for MPI Programs over InfiniBand Qi Gao, Weikuan Yu, Wei Huang, Dhabaleswar K. Panda Network-Based Computing Laboratory Department of Computer Science & Engineering
More informationOptimized Distributed Data Sharing Substrate in Multi-Core Commodity Clusters: A Comprehensive Study with Applications
Optimized Distributed Data Sharing Substrate in Multi-Core Commodity Clusters: A Comprehensive Study with Applications K. Vaidyanathan, P. Lai, S. Narravula and D. K. Panda Network Based Computing Laboratory
More informationAccelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures
Accelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures M. Bayatpour, S. Chakraborty, H. Subramoni, X. Lu, and D. K. Panda Department of Computer Science and Engineering
More informationExploiting Full Potential of GPU Clusters with InfiniBand using MVAPICH2-GDR
Exploiting Full Potential of GPU Clusters with InfiniBand using MVAPICH2-GDR Presentation at Mellanox Theater () Dhabaleswar K. (DK) Panda - The Ohio State University panda@cse.ohio-state.edu Outline Communication
More informationImplementing Efficient and Scalable Flow Control Schemes in MPI over InfiniBand
Implementing Efficient and Scalable Flow Control Schemes in MPI over InfiniBand Jiuxing Liu and Dhabaleswar K. Panda Computer Science and Engineering The Ohio State University Presentation Outline Introduction
More informationRDMA Read Based Rendezvous Protocol for MPI over InfiniBand: Design Alternatives and Benefits
RDMA Read Based Rendezvous Protocol for MPI over InfiniBand: Design Alternatives and Benefits Sayantan Sur Hyun-Wook Jin Lei Chai D. K. Panda Network Based Computing Lab, The Ohio State University Presentation
More informationDesigning Optimized MPI Broadcast and Allreduce for Many Integrated Core (MIC) InfiniBand Clusters
Designing Optimized MPI Broadcast and Allreduce for Many Integrated Core (MIC) InfiniBand Clusters K. Kandalla, A. Venkatesh, K. Hamidouche, S. Potluri, D. Bureddy and D. K. Panda Presented by Dr. Xiaoyi
More informationAdvanced RDMA-based Admission Control for Modern Data-Centers
Advanced RDMA-based Admission Control for Modern Data-Centers Ping Lai Sundeep Narravula Karthikeyan Vaidyanathan Dhabaleswar. K. Panda Computer Science & Engineering Department Ohio State University Outline
More informationDesigning Multi-Leader-Based Allgather Algorithms for Multi-Core Clusters *
Designing Multi-Leader-Based Allgather Algorithms for Multi-Core Clusters * Krishna Kandalla, Hari Subramoni, Gopal Santhanaraman, Matthew Koop and Dhabaleswar K. Panda Department of Computer Science and
More informationMVAPICH-Aptus: Scalable High-Performance Multi-Transport MPI over InfiniBand
MVAPICH-Aptus: Scalable High-Performance Multi-Transport MPI over InfiniBand Matthew Koop 1,2 Terry Jones 2 D. K. Panda 1 {koop, panda}@cse.ohio-state.edu trj@llnl.gov 1 Network-Based Computing Lab, The
More informationOptimized Non-contiguous MPI Datatype Communication for GPU Clusters: Design, Implementation and Evaluation with MVAPICH2
Optimized Non-contiguous MPI Datatype Communication for GPU Clusters: Design, Implementation and Evaluation with MVAPICH2 H. Wang, S. Potluri, M. Luo, A. K. Singh, X. Ouyang, S. Sur, D. K. Panda Network-Based
More informationLatest Advances in MVAPICH2 MPI Library for NVIDIA GPU Clusters with InfiniBand
Latest Advances in MVAPICH2 MPI Library for NVIDIA GPU Clusters with InfiniBand Presentation at GTC 2014 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationUnifying UPC and MPI Runtimes: Experience with MVAPICH
Unifying UPC and MPI Runtimes: Experience with MVAPICH Jithin Jose Miao Luo Sayantan Sur D. K. Panda Network-Based Computing Laboratory Department of Computer Science and Engineering The Ohio State University,
More informationA Portable InfiniBand Module for MPICH2/Nemesis: Design and Evaluation
A Portable InfiniBand Module for MPICH2/Nemesis: Design and Evaluation Miao Luo, Ping Lai, Sreeram Potluri, Emilio P. Mancini, Hari Subramoni, Krishna Kandalla, Dhabaleswar K. Panda Department of Computer
More informationHigh Performance MPI on IBM 12x InfiniBand Architecture
High Performance MPI on IBM 12x InfiniBand Architecture Abhinav Vishnu, Brad Benton 1 and Dhabaleswar K. Panda {vishnu, panda} @ cse.ohio-state.edu {brad.benton}@us.ibm.com 1 1 Presentation Road-Map Introduction
More informationA Plugin-based Approach to Exploit RDMA Benefits for Apache and Enterprise HDFS
A Plugin-based Approach to Exploit RDMA Benefits for Apache and Enterprise HDFS Adithya Bhat, Nusrat Islam, Xiaoyi Lu, Md. Wasi- ur- Rahman, Dip: Shankar, and Dhabaleswar K. (DK) Panda Network- Based Compu2ng
More informationEnabling Efficient Use of UPC and OpenSHMEM PGAS models on GPU Clusters
Enabling Efficient Use of UPC and OpenSHMEM PGAS models on GPU Clusters Presentation at GTC 2014 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationFuture Routing Schemes in Petascale clusters
Future Routing Schemes in Petascale clusters Gilad Shainer, Mellanox, USA Ola Torudbakken, Sun Microsystems, Norway Richard Graham, Oak Ridge National Laboratory, USA Birds of a Feather Presentation Abstract
More informationMemcached Design on High Performance RDMA Capable Interconnects
Memcached Design on High Performance RDMA Capable Interconnects Jithin Jose, Hari Subramoni, Miao Luo, Minjia Zhang, Jian Huang, Md. Wasi- ur- Rahman, Nusrat S. Islam, Xiangyong Ouyang, Hao Wang, Sayantan
More informationPerformance Evaluation of InfiniBand with PCI Express
Performance Evaluation of InfiniBand with PCI Express Jiuxing Liu Amith Mamidala Abhinav Vishnu Dhabaleswar K Panda Department of Computer and Science and Engineering The Ohio State University Columbus,
More informationMVAPICH2 and MVAPICH2-MIC: Latest Status
MVAPICH2 and MVAPICH2-MIC: Latest Status Presentation at IXPUG Meeting, July 214 by Dhabaleswar K. (DK) Panda and Khaled Hamidouche The Ohio State University E-mail: {panda, hamidouc}@cse.ohio-state.edu
More informationS. Narravula, P. Balaji, K. Vaidyanathan, H.-W. Jin and D. K. Panda. The Ohio State University
Architecture for Caching Responses with Multiple Dynamic Dependencies in Multi-Tier Data- Centers over InfiniBand S. Narravula, P. Balaji, K. Vaidyanathan, H.-W. Jin and D. K. Panda The Ohio State University
More informationAccelerating HPL on Heterogeneous GPU Clusters
Accelerating HPL on Heterogeneous GPU Clusters Presentation at GTC 2014 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda Outline
More informationPerformance Evaluation of InfiniBand with PCI Express
Performance Evaluation of InfiniBand with PCI Express Jiuxing Liu Server Technology Group IBM T. J. Watson Research Center Yorktown Heights, NY 1598 jl@us.ibm.com Amith Mamidala, Abhinav Vishnu, and Dhabaleswar
More informationMVAPICH2 Project Update and Big Data Acceleration
MVAPICH2 Project Update and Big Data Acceleration Presentation at HPC Advisory Council European Conference 212 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationUnified Runtime for PGAS and MPI over OFED
Unified Runtime for PGAS and MPI over OFED D. K. Panda and Sayantan Sur Network-Based Computing Laboratory Department of Computer Science and Engineering The Ohio State University, USA Outline Introduction
More informationCan Parallel Replication Benefit Hadoop Distributed File System for High Performance Interconnects?
Can Parallel Replication Benefit Hadoop Distributed File System for High Performance Interconnects? N. S. Islam, X. Lu, M. W. Rahman, and D. K. Panda Network- Based Compu2ng Laboratory Department of Computer
More informationEfficient and Scalable Multi-Source Streaming Broadcast on GPU Clusters for Deep Learning
Efficient and Scalable Multi-Source Streaming Broadcast on Clusters for Deep Learning Ching-Hsiang Chu 1, Xiaoyi Lu 1, Ammar A. Awan 1, Hari Subramoni 1, Jahanzeb Hashmi 1, Bracy Elton 2 and Dhabaleswar
More informationPerformance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA
Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA Pak Lui, Gilad Shainer, Brian Klaff Mellanox Technologies Abstract From concept to
More informationMVAPICH2: A High Performance MPI Library for NVIDIA GPU Clusters with InfiniBand
MVAPICH2: A High Performance MPI Library for NVIDIA GPU Clusters with InfiniBand Presentation at GTC 213 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationPerformance Analysis and Evaluation of PCIe 2.0 and Quad-Data Rate InfiniBand
th IEEE Symposium on High Performance Interconnects Performance Analysis and Evaluation of PCIe. and Quad-Data Rate InfiniBand Matthew J. Koop Wei Huang Karthik Gopalakrishnan Dhabaleswar K. Panda Network-Based
More informationExploiting InfiniBand and GPUDirect Technology for High Performance Collectives on GPU Clusters
Exploiting InfiniBand and Direct Technology for High Performance Collectives on Clusters Ching-Hsiang Chu chu.368@osu.edu Department of Computer Science and Engineering The Ohio State University OSU Booth
More informationScaling with PGAS Languages
Scaling with PGAS Languages Panel Presentation at OFA Developers Workshop (2013) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationHigh Performance Migration Framework for MPI Applications on HPC Cloud
High Performance Migration Framework for MPI Applications on HPC Cloud Jie Zhang, Xiaoyi Lu and Dhabaleswar K. Panda {zhanjie, luxi, panda}@cse.ohio-state.edu Computer Science & Engineering Department,
More informationDesigning High Performance Heterogeneous Broadcast for Streaming Applications on GPU Clusters
Designing High Performance Heterogeneous Broadcast for Streaming Applications on Clusters 1 Ching-Hsiang Chu, 1 Khaled Hamidouche, 1 Hari Subramoni, 1 Akshay Venkatesh, 2 Bracy Elton and 1 Dhabaleswar
More informationCESM (Community Earth System Model) Performance Benchmark and Profiling. August 2011
CESM (Community Earth System Model) Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell,
More informationINAM 2 : InfiniBand Network Analysis and Monitoring with MPI
INAM 2 : InfiniBand Network Analysis and Monitoring with MPI H. Subramoni, A. A. Mathews, M. Arnold, J. Perkins, X. Lu, K. Hamidouche, and D. K. Panda Department of Computer Science and Engineering The
More informationHigh Performance MPI Support in MVAPICH2 for InfiniBand Clusters
High Performance MPI Support in MVAPICH2 for InfiniBand Clusters A Talk at NVIDIA Booth (SC 11) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationMellanox Technologies Maximize Cluster Performance and Productivity. Gilad Shainer, October, 2007
Mellanox Technologies Maximize Cluster Performance and Productivity Gilad Shainer, shainer@mellanox.com October, 27 Mellanox Technologies Hardware OEMs Servers And Blades Applications End-Users Enterprise
More informationMVAPICH2-MIC on Beacon:
MVAPICH2-MIC on Beacon: Exploring Performance on a Cluster with 4 Coprocessors per Node R. Glenn Brook Chief Technology Officer, JICS Director, AACE Mitch Horton & Chad Burdyshaw Computational Scientists,
More informationMM5 Modeling System Performance Research and Profiling. March 2009
MM5 Modeling System Performance Research and Profiling March 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center
More information2008 International ANSYS Conference
2008 International ANSYS Conference Maximizing Productivity With InfiniBand-Based Clusters Gilad Shainer Director of Technical Marketing Mellanox Technologies 2008 ANSYS, Inc. All rights reserved. 1 ANSYS,
More informationA Case for High Performance Computing with Virtual Machines
A Case for High Performance Computing with Virtual Machines Wei Huang*, Jiuxing Liu +, Bulent Abali +, and Dhabaleswar K. Panda* *The Ohio State University +IBM T. J. Waston Research Center Presentation
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 informationSR-IOV Support for Virtualization on InfiniBand Clusters: Early Experience
Support for Virtualization on InfiniBand Clusters: Early Experience Jithin Jose, Mingzhe Li, Xiaoyi Lu, Krishna Chaitanya Kandalla, Mark Daniel Arnold, and Dhabaleswar K. (DK) Panda Department of Computer
More informationCoupling GPUDirect RDMA and InfiniBand Hardware Multicast Technologies for Streaming Applications
Coupling GPUDirect RDMA and InfiniBand Hardware Multicast Technologies for Streaming Applications GPU Technology Conference GTC 2016 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationSupport for GPUs with GPUDirect RDMA in MVAPICH2 SC 13 NVIDIA Booth
Support for GPUs with GPUDirect RDMA in MVAPICH2 SC 13 NVIDIA Booth by D.K. Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda Outline Overview of MVAPICH2-GPU
More informationOptimizing LS-DYNA Productivity in Cluster Environments
10 th International LS-DYNA Users Conference Computing Technology Optimizing LS-DYNA Productivity in Cluster Environments Gilad Shainer and Swati Kher Mellanox Technologies Abstract Increasing demand for
More informationMPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA
MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA Gilad Shainer 1, Tong Liu 1, Pak Lui 1, Todd Wilde 1 1 Mellanox Technologies Abstract From concept to engineering, and from design to
More informationHigh Performance Distributed Lock Management Services using Network-based Remote Atomic Operations
High Performance Distributed Lock Management Services using Network-based Remote Atomic Operations S. Narravula, A. Mamidala, A. Vishnu, K. Vaidyanathan, and D. K. Panda Presented by Lei Chai Network Based
More informationLiMIC: Support for High-Performance MPI Intra-Node Communication on Linux Cluster
: Support for High-Performance MPI Intra-Node Communication on Linux Cluster Hyun-Wook Jin Sayantan Sur Lei Chai Dhabaleswar K. Panda Department of Computer Science and Engineering The Ohio State University
More informationEvaluating the Impact of RDMA on Storage I/O over InfiniBand
Evaluating the Impact of RDMA on Storage I/O over InfiniBand J Liu, DK Panda and M Banikazemi Computer and Information Science IBM T J Watson Research Center The Ohio State University Presentation Outline
More informationMULTI-CONNECTION AND MULTI-CORE AWARE ALL-GATHER ON INFINIBAND CLUSTERS
MULTI-CONNECTION AND MULTI-CORE AWARE ALL-GATHER ON INFINIBAND CLUSTERS Ying Qian Mohammad J. Rashti Ahmad Afsahi Department of Electrical and Computer Engineering, Queen s University Kingston, ON, CANADA
More informationInterconnect Your Future
Interconnect Your Future Gilad Shainer 2nd Annual MVAPICH User Group (MUG) Meeting, August 2014 Complete High-Performance Scalable Interconnect Infrastructure Comprehensive End-to-End Software Accelerators
More informationCP2K Performance Benchmark and Profiling. April 2011
CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council HPC works working group activities Participating vendors: HP, Intel, Mellanox
More informationMILC Performance Benchmark and Profiling. April 2013
MILC Performance Benchmark and Profiling April 2013 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting
More informationHigh Performance MPI-2 One-Sided Communication over InfiniBand
High Performance MPI-2 One-Sided Communication over InfiniBand Weihang Jiang Jiuxing Liu Hyun-Wook Jin Dhabaleswar K. Panda William Gropp Rajeev Thakur Computer and Information Science The Ohio State University
More informationDesigning Shared Address Space MPI libraries in the Many-core Era
Designing Shared Address Space MPI libraries in the Many-core Era Jahanzeb Hashmi hashmi.29@osu.edu (NBCL) The Ohio State University Outline Introduction and Motivation Background Shared-memory Communication
More informationOptimizing MPI Communication on Multi-GPU Systems using CUDA Inter-Process Communication
Optimizing MPI Communication on Multi-GPU Systems using CUDA Inter-Process Communication Sreeram Potluri* Hao Wang* Devendar Bureddy* Ashish Kumar Singh* Carlos Rosales + Dhabaleswar K. Panda* *Network-Based
More informationHigh-Performance Training for Deep Learning and Computer Vision HPC
High-Performance Training for Deep Learning and Computer Vision HPC Panel at CVPR-ECV 18 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationThe Role of InfiniBand Technologies in High Performance Computing. 1 Managed by UT-Battelle for the Department of Energy
The Role of InfiniBand Technologies in High Performance Computing 1 Managed by UT-Battelle Contributors Gil Bloch Noam Bloch Hillel Chapman Manjunath Gorentla- Venkata Richard Graham Michael Kagan Vasily
More informationHigh Performance MPI-2 One-Sided Communication over InfiniBand
High Performance MPI-2 One-Sided Communication over InfiniBand Weihang Jiang Jiuxing Liu Hyun-Wook Jin Dhabaleswar K. Panda William Gropp Rajeev Thakur Computer and Information Science The Ohio State University
More informationLS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance
11 th International LS-DYNA Users Conference Computing Technology LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance Gilad Shainer 1, Tong Liu 2, Jeff Layton
More informationSTAR-CCM+ Performance Benchmark and Profiling. July 2014
STAR-CCM+ Performance Benchmark and Profiling July 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: CD-adapco, Intel, Dell, Mellanox Compute
More informationCUDA Kernel based Collective Reduction Operations on Large-scale GPU Clusters
CUDA Kernel based Collective Reduction Operations on Large-scale GPU Clusters Ching-Hsiang Chu, Khaled Hamidouche, Akshay Venkatesh, Ammar Ahmad Awan and Dhabaleswar K. (DK) Panda Speaker: Sourav Chakraborty
More informationCooperative VM Migration for a virtualized HPC Cluster with VMM-bypass I/O devices
Cooperative VM Migration for a virtualized HPC Cluster with VMM-bypass I/O devices Ryousei Takano, Hidemoto Nakada, Takahiro Hirofuchi, Yoshio Tanaka, and Tomohiro Kudoh Information Technology Research
More informationCRFS: A Lightweight User-Level Filesystem for Generic Checkpoint/Restart
CRFS: A Lightweight User-Level Filesystem for Generic Checkpoint/Restart Xiangyong Ouyang, Raghunath Rajachandrasekar, Xavier Besseron, Hao Wang, Jian Huang, Dhabaleswar K. Panda Department of Computer
More informationOverview of the MVAPICH Project: Latest Status and Future Roadmap
Overview of the MVAPICH Project: Latest Status and Future Roadmap MVAPICH2 User Group (MUG) Meeting by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationHigh Performance Distributed Lock Management Services using Network-based Remote Atomic Operations
High Performance Distributed Lock Management Services using Network-based Remote Atomic Operations S. Narravula A. Mamidala A. Vishnu K. Vaidyanathan D. K. Panda Department of Computer Science and Engineering
More informationBirds of a Feather Presentation
Mellanox InfiniBand QDR 4Gb/s The Fabric of Choice for High Performance Computing Gilad Shainer, shainer@mellanox.com June 28 Birds of a Feather Presentation InfiniBand Technology Leadership Industry Standard
More informationEC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures
EC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures Haiyang Shi, Xiaoyi Lu, and Dhabaleswar K. (DK) Panda {shi.876, lu.932, panda.2}@osu.edu The Ohio State University
More informationHigh-Performance Broadcast for Streaming and Deep Learning
High-Performance Broadcast for Streaming and Deep Learning Ching-Hsiang Chu chu.368@osu.edu Department of Computer Science and Engineering The Ohio State University OSU Booth - SC17 2 Outline Introduction
More informationDesign and Evaluation of Benchmarks for Financial Applications using Advanced Message Queuing Protocol (AMQP) over InfiniBand
Design and Evaluation of Benchmarks for Financial Applications using Advanced Message Queuing Protocol (AMQP) over InfiniBand Hari Subramoni, Gregory Marsh, Sundeep Narravula, Ping Lai, and Dhabaleswar
More informationNAMD Performance Benchmark and Profiling. January 2015
NAMD Performance Benchmark and Profiling January 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource
More informationMemory Management Strategies for Data Serving with RDMA
Memory Management Strategies for Data Serving with RDMA Dennis Dalessandro and Pete Wyckoff (presenting) Ohio Supercomputer Center {dennis,pw}@osc.edu HotI'07 23 August 2007 Motivation Increasing demands
More informationPer-call Energy Saving Strategies in All-to-all Communications
Computer Science Technical Reports Computer Science 2011 Per-call Energy Saving Strategies in All-to-all Communications Vaibhav Sundriyal Iowa State University, vaibhavs@iastate.edu Masha Sosonkina Iowa
More informationTIBCO, HP and Mellanox High Performance Extreme Low Latency Messaging
TIBCO, HP and Mellanox High Performance Extreme Low Latency Messaging Executive Summary: With the recent release of TIBCO FTL TM, TIBCO is once again changing the game when it comes to providing high performance
More informationNAMD Performance Benchmark and Profiling. November 2010
NAMD Performance Benchmark and Profiling November 2010 Note The following research was performed under the HPC Advisory Council activities Participating vendors: HP, Mellanox Compute resource - HPC Advisory
More informationEfficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning
Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning Ammar Ahmad Awan, Khaled Hamidouche, Akshay Venkatesh, and Dhabaleswar K. Panda Network-Based Computing Laboratory Department
More information