MVAPICH2 and MVAPICH2-MIC: Latest Status
|
|
- Prosper Elliott
- 5 years ago
- Views:
Transcription
1 MVAPICH2 and MVAPICH2-MIC: Latest Status Presentation at IXPUG Meeting, July 214 by Dhabaleswar K. (DK) Panda and Khaled Hamidouche The Ohio State University {panda,
2 Number of Clusters Percentage of Clusters Trends for Commodity Computing Clusters in the Top 5 List ( Percentage of Clusters Number of Clusters Timeline 2
3 Large-scale InfiniBand Installations 223 IB Clusters (44.3%) in the June 214 Top5 list ( Installations in the Top 5 (25 systems): 519,64 cores (Stampede) at TACC (7 th ) 12, 64 cores (Nebulae) at China/NSCS (28 th ) 62,64 cores (HPC2) in Italy (11 th ) 72,288 cores (Yellowstone) at NCAR (29 th ) 147, 456 cores (Super MUC) in Germany (12 th ) 7,56 cores (Helios) at Japan/IFERC (3 th ) 76,32 cores (Tsubame 2.5) at Japan/GSIC (13 th ) 138,368 cores (Tera-1) at France/CEA (35 th ) 194,616 cores (Cascade) at PNNL (15 th ) 222,72 cores (QUARTETTO) in Japan (37 th ) 11,4 cores (Pangea) at France/Total (16 th ) 53,54 cores (PRIMERGY) in Australia (38 th ) 96,192 cores (Pleiades) at NASA/Ames (21 st ) 77,52 cores (Conte) at Purdue University (39th) 73,584 cores (Spirit) at USA/Air Force (24 th ) 44,52 cores (Spruce A) at AWE in UK (4 th ) 77,184 cores (Curie thin nodes) at France/CEA (26 h ) 48,896 cores (MareNostrum) at Spain/BSC (41 st ) 65,32-cores, idataplex DX36M4 at Germany/Max- Planck (27 th ) and many more! 3
4 MVAPICH2/MVAPICH2-X Software High Performance open-source MPI Library for InfiniBand, 1Gig/iWARP, and RDMA over Converged Enhanced Ethernet (RoCE) MVAPICH (MPI-1), MVAPICH2 (MPI-2.2 and MPI-3.), Available since 22 MVAPICH2-X (MPI + PGAS), Available since 212 Support for GPGPUs and MIC Used by more than 2,15 organizations in 72 countries More than 218, downloads from OSU site directly Empowering many TOP5 clusters 7 th ranked 519,64-core cluster (Stampede) at TACC 13 th ranked 74,358-core cluster (Tsubame 2.5) at Tokyo Institute of Technology 23 rd ranked 96,192-core cluster (Pleiades) at NASA Available with software stacks of many IB, HSE, and server vendors including Linux Distros (RedHat and SuSE) Partner in the U.S. NSF-TACC Stampede System 4
5 MVAPICH2 2. GA and MVAPICH2-X 2. GA Released on 6/2/14 Major Features and Enhancements Based on MPICH-3.1 MPI-3 RMA Support Support for Non-blocking collectives MPI-T support CMA support is default for intra-node communication Optimization of collectives with CMA support Large message transfer support Reduced memory footprint Improved Job start-up time Optimization of collectives and tuning for multiple platforms Updated hwloc to version 1.9 MVAPICH2-X 2. GA supports hybrid MPI + PGAS (UPC and OpenSHMEM) programming models Based on MVAPICH2 2. GA including MPI-3 features Compliant with UPC and OpenSHMEM v1.f 5
6 MVAPICH2-MIC: Optimized MPI Library for Xeon Phi Clusters MPI libraries run out of box (or with minor changes) on the Xeon Phi Critical to optimize the runtimes for better performance Tune existing designs Designs using lower level features offered by MPSS Designs to address system-level limitations Initial version of MVAPICH2-MIC (based on MVAPICH2 2.a release) is available on Stampede since Oct 13 Supports all modes of usage host-only, offload, coprocessor-only and symmetric Improved shared memory communication channel SCIF-based designs for improved communication within MIC and between MICs and Hosts Proxy-based design to work around bandwidth limitations on Sandy Bridge platform Enhanced version based on MVAPICH2 2.GA release is being worked out. Will be coming out soon!! 6
7 MPI Applications on MIC Clusters MPI (+X) continues to be the predominant programming model in HPC Flexibility in launching MPI jobs on clusters with Xeon Phi Xeon Xeon Phi Multi-core Centric Host-only MPI Program Offload (/reverse Offload) MPI Program Offloaded Computation Symmetric MPI Program MPI Program Coprocessor-only Many-core Centric MPI Program 7
8 MPI Applications on MIC Clusters Offload mode: lesser overhead way to extract performance from Xeon Phi Xeon Xeon Phi Multi-core Centric Host-only MPI Program Offload MPI Program Offloaded Computation Symmetric MPI Program MPI Program Coprocessor-only Many-core Centric MPI Program 8
9 MIC MIC PCIe MPI Data Movement in Offload Mode MPI communication only from the host Node Node 1 CPU QPI CPU1 CPU1 CPU IB MPI Process 1. Intra-Socket 2. Inter-Socket 3. Inter-Node Supported by the Standard MVAPICH2 Library (Latest Release - MVAPICH2 2. GA) 9
10 Latency (us) Latency (us) One-way Latency: MPI over IB with MVAPICH Small Message Latency Large Message Latency Qlogic-DDR Qlogic-QDR ConnectX-DDR ConnectX2-PCIe2-QDR ConnectX3-PCIe3-FDR Sandy-ConnectIB-DualFDR Ivy-ConnectIB-DualFDR Message Size (bytes) Message Size (bytes) DDR, QDR GHz Quad-core (Westmere) Intel PCI Gen2 with IB switch FDR GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch 1
11 Bandwidth (MBytes/sec) Bandwidth (MBytes/sec) Bandwidth: MPI over IB with MVAPICH Unidirectional Bandwidth Bidirectional Bandwidth Qlogic-DDR Qlogic-QDR ConnectX-DDR ConnectX2-PCIe2-QDR ConnectX3-PCIe3-FDR Sandy-ConnectIB-DualFDR Ivy-ConnectIB-DualFDR Message Size (bytes) Message Size (bytes) DDR, QDR GHz Quad-core (Westmere) Intel PCI Gen2 with IB switch FDR GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR GHz Octa-core (SandyBridge) Intel PCI Gen3 with IB switch ConnectIB-Dual FDR GHz Deca-core (IvyBridge) Intel PCI Gen3 with IB switch 11
12 Bandwidth (MB/s) Bandwidth (MB/s) Latency (us) MVAPICH2 Two-Sided Intra-Node Performance (Shared memory and Kernel-based Zero-copy Support (LiMIC and CMA)) Latency Intra-Socket Inter-Socket.45 us.18 us K Latest MVAPICH2 2. Intel Ivy-bridge Bandwidth (Intra-socket) intra-socket-cma intra-socket-shmem intra-socket-limic Bandwidth (Inter-socket) 14,25 MB/s inter-socket-cma 13,749 MB/s 12 inter-socket-shmem 1 inter-socket-limic
13 Latency (us) Bandwidth (Mbytes/sec) Latency (us) Bandwidth (Mbytes/sec) MPI-3 RMA Get/Put with Flush Performance Inter-node Get/Put Latency Get Put 2.4 us 1.56 us K 2K 4K Inter-node Get/Put Bandwidth Get Put K 4K 16K 64K 256K Intra-socket Get/Put Latency Get Put 2 15 Intra-socket Get/Put Bandwidth Get Put us K 2K 4K 8K K 4K 16K 64K 256K Latest MVAPICH2 2., Intel Sandy-bridge with Connect-IB (single-port) 13
14 GFlops GFlops HPCG Benchmark with Offload Mode MVAPICH2-Offload MVAPICH2-Offload # Nodes # Nodes Full subscription of a node: 1 MPI process + 1 OpenMP threads on the CPU 3 MPI processes + 6 OpenMP threads on CPU + 24 OpenMP threads offload on MIC Input data size = 128^3 Binding using MV2_CPU_MAPPING 1 node MVAPICH2 achieves 24 GFlops 124 nodes MVAPICH2 achieves 18.2 TFlops 14
15 MPI Applications on Xeon Phi Clusters - IntraNode Xeon Phi as a many-core node Xeon Xeon Phi Multi-core Centric Host-only MPI Program Offload MPI Program Offloaded Computation Symmetric MPI Program MPI Program Coprocessor-only Many-core Centric MPI Program 15
16 MIC PCIe MPI Data Movement in Coprocessor-Only Mode CPU Node QPI CPU1 Xeon Phi C L2 C L2 C L2 C L2 GDDR GDDR GDDR GDDR C C C C L2 L2 L2 L2 MPI Process Intra-MIC Communication 16
17 MVAPICH2-MIC Channels for Intra-MIC Communication MVAPICH2 provides a hybrid of two channels CH3-SHM Derives the design for shared memory communication on host Tuned for the Xeon Phi architecture CH3-SCIF SCIF is a lower level API provided by MPSS Provides user control of the DMA Takes advantage of SCIF to improve performance CH3-SHM POSIX Calls Xeon Phi MVAPICH2 CH3-SCIF SCIF S. Potluri, A. Venkatesh, D. Bureddy, K. Kandalla, and D. K. Panda - Efficient Intra-node Communication on Intel-MIC Clusters - International Symposium on Cluster, Cloud and Grid Computing, May 213 (CCGrid 13). S. Potluri, K. Tomko, D. Bureddy and D. K. Panda - Intra-MIC MPI Communication using MVAPICH2: Early Experience - TACC-Intel Highly-Parallel Computing Symposium (TI-HPCS), April Best Student Paper 17
18 Bandwidth (MB/s) Better Bandwidth (MB/s) Better Latency (usec) Latency (usec) Intra-MIC - Point-to-Point Communication MV2-MIC-2.GA MV2-MIC-2.a Better K 4K 66% Better osu_latency (small) osu_bw osu_latency (medium) 3% K 4K osu_bibw 18
19 Latency (usec) Latency (usec) Intra-MIC - Collective Communication AlltoAll MV2-MIC-2.GA MV2-MIC-2.a Better % Better K 4K K 4K 8 processes osu_alltoall (small) 16 processes osu_alltoall (small) 8 Processes: 4% and 19% improvement for 4 and 4K bytes messages 16 Processes: 38% and 71% improvement for 64 and 1K bytes messages 19
20 GFlops Better Intra-MIC HPCG Benchmark MV2-MIC-2.GA MV2-MIC-2.a % Input size 4 MPI processes with 6 OpenMP threads per process Binding using MV2_MIC_MAPPING environment variable 2
21 MPI Applications on MIC Clusters - InterNode Xeon Xeon Phi Multi-core Centric Host-only MPI Program Offload MPI Program Offloaded Computation Symmetric MPI Program MPI Program Coprocessor-only Many-core Centric MPI Program 21
22 MIC MIC PCIe MPI Data Movement in Coprocessor-Only Mode Node Node 1 CPU QPI CPU1 CPU1 CPU IB MPI Process 1. MIC-RemoteMIC 22
23 MVAPICH2 Channels for MIC-RemoteMIC Communication Xeon Phi MVAPICH2 OFA-IB-CH3 IB Verbs mlx4_ Xeon Phi MVAPICH2 CH3 IB Verbs mlx4_ IB-HCA CH3-IB channel High-performance InfiniBand channel in MVAPICH2 Implemented using IB Verbs - DirectIB Currently does not support advanced features like hardware multicast, etc. Limited by P2P Bandwidth offered on Sandy Bridge platform 23
24 Host Proxy-based Designs in MVAPICH2-MIC SNB E MB/s 6296 MB/s 528 MB/s MB/s P2P Write/IB Write to Xeon Phi P2P Read / IB Read from Xeon Phi Xeon Phi-to-Host IB Read from Host Direct IB channels is limited by P2P read bandwidth MVAPICH2-MIC uses a hybrid DirectIB + host proxy-based approach to work around this S. Potluri, D. Bureddy, K. Hamidouche, A. Venkatesh, K. Kandalla, H. Subramoni and D. K. Panda, MVAPICH- PRISM: A Proxy-based Communication Framework using InfiniBand and SCIF for Intel MIC Clusters Int'l Conference on Supercomputing (SC '13), November
25 Bandwidth (MB/sec) Better Bandwidth (MB/sec) Better Latency (usec) Latency (usec) MIC-RemoteMIC Point-to-Point Communication (Active Proxy) MV2-MIC-2.GA w/ proxy MV2-MIC-2.GA w/o proxy Better Better K 8K 32K 128K 512K 2M 6 osu_latency (small) osu_latency (large) K 64K 1M K 64K 1M osu_bw osu_bibw 25
26 GFlops InterNode - Coprocessor-only Mode - HPCG MV2-MIC-2.GA # MICs Full subscription of a MIC: (Host is not involved) 3 MPI processes + 24 OpenMP threads on MIC Input size = 96^3 Binding using MV2_MIC_MAPPING 1 node MVAPICH2-MIC achieves 15 Gflops 8 nodes MVAPICH2-MIC achieves 15 GFlops 26
27 MPI Applications on MIC Clusters - InterNode Xeon Xeon Phi Multi-core Centric Host-only MPI Program Offload MPI Program Offloaded Computation Symmetric MPI Program MPI Program Coprocessor-only Many-core Centric MPI Program 27
28 MIC PCIe MPI Data Movement in Symmetric Mode - InterNode Node Node 1 CPU QPI CPU1 CPU1 CPU IB MPI Process 1. MIC- RemoteMIC 2. MIC- RemoteHost (Closer to HCA) 3. MIC- RemoteHost (Farther from HCA) Uses OFA-IB-CH3 channel backed by host-based proxy 28
29 K 4K 16K 64K 256K 1M 4M K 4K 16K 64K 256K 1M 4M Bandwidth (MB/s) Better Bandwidth (MB/s) Better Latency (usec) Latency (usec) MIC-RemoteHost Point-to-Point Communication (Active Proxy) MV2-MIC-2.GA w/ proxy MV2-MIC-2.GA w/o proxy K Better K 32K 128K 512K 2M Better osu_latency (small) osu_latency (large) 8742 osu_bw osu_bibw 29
30 Latency (usec) Latency (usec) Inter-Node Symmetric Mode Passive Proxy 64 MPI processes on 2 nodes (16H+16M per node) MV2-MIC-2.GA MV2-MIC-2.a Better X 6 1.8X K 4K 16K64K Better osu_alltoall osu_allgather Fully-subscribed mode (16 processes on the Host) Passive proxy design outperforms the active proxy (default in MV2-MIC-2.a) 3
31 Inter-Node Symmetric Mode Redesigning Collectives Latency (us) x 1 Latency (us) x MV2-MIC-2.a MV2-MIC-2.GA Message Size (bytes) Allgather: 32 Nodes (16H+16M) Message Size (bytes) Alltoall: 16 Nodes (8H+8M) 3X Latency (us) x 1 Latency (us) x Message Size (bytes) Allgather: 32 Nodes (8H+8M) Message Size (bytes) Alltoall: 16 Nodes (8H+8M) A. Venkatesh, S. Potluri, R. Rajachandrasekar, M. Luo, K. Hamidouche and D. K. Panda - High Performance Alltoall and Allgather designs for InfiniBand MIC Clusters; IPDPS 14, May 214 2X 2.5X 31 Better Better
32 GFlops InterNode Symmetric Mode - HPCG MV2-MIC-2.GA # Nodes Full subscription of a node: 2 MPI processes + 16 OpenMP threads on CPU 3 MPI processes + 24 OpenMP threads on MIC Input size = 96^3 Binding using both MV2_CPU_MAPPING and MV2_MIC_MAPPING To explore different threading levels for host and Xeon Phi 1 node MVAPICH2-MIC achieves 23.5 GFlops 16 nodes MVAPICH2-MIC achieves 34 GFlops 32
33 On-Going and Future Work Redesigning of other collective operations Optimizing MPI3-RMA operations for MIC Automatic proxy selection using architecture topology detection Evaluating application performance and scaling Extending designs to KNL-based self-hosted nodes 33
34 Conclusion MVAPICH2-MIC provides a high-performance and scalable MPI library for InfiniBand clusters using Xeon Phi Initial version takes advantage of SCIF to improve Intra-MIC and Intranode MIC-Host communication Host-Proxy based designs help work around P2P bandwidth bottlenecks Provides initial support for heterogeneity-aware collectives Enhanced version of MVAPICH2-MIC (based on 2. GA) will be available soon 34
35 Upcoming 2 nd Annual MVAPICH User Group (MUG) Meeting August 25-27, 214; Columbus, Ohio, USA Keynote Talks, Invited Talks, Contributed Presentations Tutorial on MVAPICH2 and MVAPICH2-X optimization and tuning Speakers (Confirmed so far): Keynote: Dan Stanzione (TACC) Keynote: Darren Kerbyson (PNNL) Sadaf Alam (CSCS, Switzerland) Onur Celebioglu (Dell) Jens Glaser (Univ. of Michigan) Jeff Hammond (Intel) Adam Moody (LLNL) David Race (Cray) Davide Rossetti (NVIDIA) Gilad Shainer (Mellanox) Sayantan Sur (Intel) Mahidhar Tatineni (SDSC) Jerome Vienne (TACC) More details at: 35
36 Web Pointers MVAPICH Web Page 36
Latest 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 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 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 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 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 informationOverview of MVAPICH2 and MVAPICH2- X: Latest Status and Future Roadmap
Overview of MVAPICH2 and MVAPICH2- X: Latest Status and Future Roadmap MVAPICH2 User Group (MUG) MeeKng by Dhabaleswar K. (DK) Panda The Ohio State University E- mail: panda@cse.ohio- state.edu h
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 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 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 informationSupporting PGAS Models (UPC and OpenSHMEM) on MIC Clusters
Supporting PGAS Models (UPC and OpenSHMEM) on MIC Clusters Presentation at IXPUG Meeting, July 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 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 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 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 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 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 informationDesigning MPI and PGAS Libraries for Exascale Systems: The MVAPICH2 Approach
Designing MPI and PGAS Libraries for Exascale Systems: The MVAPICH2 Approach Talk at OpenFabrics Workshop (April 216) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
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 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 informationProgramming Models for Exascale Systems
Programming Models for Exascale Systems Talk at HPC Advisory Council Stanford Conference and Exascale Workshop (214) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationHigh-Performance MPI Library with SR-IOV and SLURM for Virtualized InfiniBand Clusters
High-Performance MPI Library with SR-IOV and SLURM for Virtualized InfiniBand Clusters Talk at OpenFabrics Workshop (April 2016) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
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 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 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 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 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 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 informationAdvanced Topics in InfiniBand and High-speed Ethernet for Designing HEC Systems
Advanced Topics in InfiniBand and High-speed Ethernet for Designing HEC Systems A Tutorial at SC 13 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
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 Software Libraries and Middleware for Exascale Systems: Opportunities and Challenges
Designing Software Libraries and Middleware for Exascale Systems: Opportunities and Challenges Talk at Brookhaven National Laboratory (Oct 214) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail:
More informationAccelerating Big Data Processing with RDMA- Enhanced Apache Hadoop
Accelerating Big Data Processing with RDMA- Enhanced Apache Hadoop Keynote Talk at BPOE-4, in conjunction with ASPLOS 14 (March 2014) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationAcceleration for Big Data, Hadoop and Memcached
Acceleration for Big Data, Hadoop and Memcached A Presentation at HPC Advisory Council Workshop, Lugano 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 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 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 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 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 informationHigh-Performance and Scalable Designs of Programming Models for Exascale Systems Talk at HPC Advisory Council Stanford Conference (2015)
High-Performance and Scalable Designs of Programming Models for Exascale Systems Talk at HPC Advisory Council Stanford Conference (215) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationAccelerating Data Management and Processing on Modern Clusters with RDMA-Enabled Interconnects
Accelerating Data Management and Processing on Modern Clusters with RDMA-Enabled Interconnects Keynote Talk at ADMS 214 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
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 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 informationMVAPICH2-GDR: Pushing the Frontier of HPC and Deep Learning
MVAPICH2-GDR: Pushing the Frontier of HPC and Deep Learning Talk at Mellanox Theater (SC 16) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationOptimizing & Tuning Techniques for Running MVAPICH2 over IB
Optimizing & Tuning Techniques for Running MVAPICH2 over IB Talk at 2nd Annual IBUG (InfiniBand User's Group) Workshop (214) by Hari Subramoni The Ohio State University E-mail: subramon@cse.ohio-state.edu
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 informationDesigning 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 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 informationThe MVAPICH2 Project: Pushing the Frontier of InfiniBand and RDMA Networking Technologies Talk at OSC/OH-TECH Booth (SC 15)
The MVAPICH2 Project: Pushing the Frontier of InfiniBand and RDMA Networking Technologies Talk at OSC/OH-TECH Booth (SC 15) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationHigh-Performance and Scalable Non-Blocking All-to-All with Collective Offload on InfiniBand Clusters: A study with Parallel 3DFFT
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
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 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 informationInfiniBand Strengthens Leadership as the Interconnect Of Choice By Providing Best Return on Investment. TOP500 Supercomputers, June 2014
InfiniBand Strengthens Leadership as the Interconnect Of Choice By Providing Best Return on Investment TOP500 Supercomputers, June 2014 TOP500 Performance Trends 38% CAGR 78% CAGR Explosive high-performance
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 informationEnabling Efficient Use of MPI and PGAS Programming Models on Heterogeneous Clusters with High Performance Interconnects
Enabling Efficient Use of MPI and PGAS Programming Models on Heterogeneous Clusters with High Performance Interconnects Dissertation Presented in Partial Fulfillment of the Requirements for the Degree
More informationThe MVAPICH2 Project: Latest Developments and Plans Towards Exascale Computing
The MVAPICH2 Project: Latest Developments and Plans Towards Exascale Computing Presentation at Mellanox Theatre (SC 16) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
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 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 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 informationOperational Robustness of Accelerator Aware MPI
Operational Robustness of Accelerator Aware MPI Sadaf Alam Swiss National Supercomputing Centre (CSSC) Switzerland 2nd Annual MVAPICH User Group (MUG) Meeting, 2014 Computing Systems @ CSCS http://www.cscs.ch/computers
More informationInterconnect Your Future
Interconnect Your Future Smart Interconnect for Next Generation HPC Platforms Gilad Shainer, August 2016, 4th Annual MVAPICH User Group (MUG) Meeting Mellanox Connects the World s Fastest Supercomputer
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 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 informationExploiting GPUDirect RDMA in Designing High Performance OpenSHMEM for NVIDIA GPU Clusters
2015 IEEE International Conference on Cluster Computing Exploiting GPUDirect RDMA in Designing High Performance OpenSHMEM for NVIDIA GPU Clusters Khaled Hamidouche, Akshay Venkatesh, Ammar Ahmad Awan,
More informationPerformance of PGAS Models on KNL: A Comprehensive Study with MVAPICH2-X
Performance of PGAS Models on KNL: A Comprehensive Study with MVAPICH2-X Intel Nerve Center (SC 7) Presentation Dhabaleswar K (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu Parallel
More informationSoftware Libraries and Middleware for Exascale Systems
Software Libraries and Middleware for Exascale Systems Talk at HPC Advisory Council China Workshop (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 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 informationDesigning and Building Efficient HPC Cloud with Modern Networking Technologies on Heterogeneous HPC Clusters
Designing and Building Efficient HPC Cloud with Modern Networking Technologies on Heterogeneous HPC Clusters Jie Zhang Dr. Dhabaleswar K. Panda (Advisor) Department of Computer Science & Engineering The
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 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 informationSolutions for Scalable HPC
Solutions for Scalable HPC Scot Schultz, Director HPC/Technical Computing HPC Advisory Council Stanford Conference Feb 2014 Leading Supplier of End-to-End Interconnect Solutions Comprehensive End-to-End
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 informationSupport Hybrid MPI+PGAS (UPC/OpenSHMEM/CAF) Programming Models through a Unified Runtime: An MVAPICH2-X Approach
Support Hybrid MPI+PGAS (UPC/OpenSHMEM/CAF) Programming Models through a Unified Runtime: An MVAPICH2-X Approach Talk at OSC theater (SC 15) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail:
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 informationEnabling Efficient Use of UPC and OpenSHMEM PGAS Models on GPU Clusters. Presented at GTC 15
Enabling Efficient Use of UPC and OpenSHMEM PGAS Models on GPU Clusters Presented at Presented by Dhabaleswar K. (DK) Panda The Ohio State University E- mail: panda@cse.ohio- state.edu hcp://www.cse.ohio-
More informationCommunication Frameworks for HPC and Big Data
Communication Frameworks for HPC and Big Data Talk at HPC Advisory Council Spain Conference (215) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
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 informationJob Startup at Exascale:
Job Startup at Exascale: Challenges and Solutions Hari Subramoni The Ohio State University http://nowlab.cse.ohio-state.edu/ Current Trends in HPC Supercomputing systems scaling rapidly Multi-/Many-core
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 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 informationScalability and Performance of MVAPICH2 on OakForest-PACS
Scalability and Performance of MVAPICH2 on OakForest-PACS Talk at JCAHPC Booth (SC 17) 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 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 informationAdvantages to Using MVAPICH2 on TACC HPC Clusters
Advantages to Using MVAPICH2 on TACC HPC Clusters Jérôme VIENNE viennej@tacc.utexas.edu Texas Advanced Computing Center (TACC) University of Texas at Austin Wednesday 27 th August, 2014 1 / 20 Stampede
More informationHow to Tune and Extract Higher Performance with MVAPICH2 Libraries?
How to Tune and Extract Higher Performance with MVAPICH2 Libraries? An XSEDE ECSS webinar 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 Computing
High Performance Computing Dror Goldenberg, HPCAC Switzerland Conference March 2015 End-to-End Interconnect Solutions for All Platforms Highest Performance and Scalability for X86, Power, GPU, ARM and
More informationOPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA
OPEN MPI WITH RDMA SUPPORT AND CUDA Rolf vandevaart, NVIDIA OVERVIEW What is CUDA-aware History of CUDA-aware support in Open MPI GPU Direct RDMA support Tuning parameters Application example Future work
More informationHigh-Performance and Scalable Designs of Programming Models for Exascale Systems
High-Performance and Scalable Designs of Programming Models for Exascale Systems Keynote Talk at HPCAC, Switzerland (April 217) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationGPU-centric communication for improved efficiency
GPU-centric communication for improved efficiency Benjamin Klenk *, Lena Oden, Holger Fröning * * Heidelberg University, Germany Fraunhofer Institute for Industrial Mathematics, Germany GPCDP Workshop
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 informationIntroduction to Xeon Phi. Bill Barth January 11, 2013
Introduction to Xeon Phi Bill Barth January 11, 2013 What is it? Co-processor PCI Express card Stripped down Linux operating system Dense, simplified processor Many power-hungry operations removed Wider
More informationTECHNOLOGIES FOR IMPROVED SCALING ON GPU CLUSTERS. Jiri Kraus, Davide Rossetti, Sreeram Potluri, June 23 rd 2016
TECHNOLOGIES FOR IMPROVED SCALING ON GPU CLUSTERS Jiri Kraus, Davide Rossetti, Sreeram Potluri, June 23 rd 2016 MULTI GPU PROGRAMMING Node 0 Node 1 Node N-1 MEM MEM MEM MEM MEM MEM MEM MEM MEM MEM MEM
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 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 informationMPI: Overview, Performance Optimizations and Tuning
MPI: Overview, Performance Optimizations and Tuning A Tutorial at HPC Advisory Council Conference, Lugano 2012 by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
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 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 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 informationSimulation using MIC co-processor on Helios
Simulation using MIC co-processor on Helios Serhiy Mochalskyy, Roman Hatzky PRACE PATC Course: Intel MIC Programming Workshop High Level Support Team Max-Planck-Institut für Plasmaphysik Boltzmannstr.
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 informationDesigning MPI and PGAS Libraries for Exascale Systems: The MVAPICH2 Approach
Designing MPI and PGAS Libraries for Exascale Systems: The MVAPICH2 Approach Talk at OpenFabrics Workshop (March 217) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu
More informationOp#miza#on and Tuning Collec#ves in MVAPICH2
Op#miza#on and Tuning Collec#ves in MVAPICH2 MVAPICH2 User Group (MUG) Mee#ng by Hari Subramoni The Ohio State University E- mail: subramon@cse.ohio- state.edu h
More informationInterconnection Network for Tightly Coupled Accelerators Architecture
Interconnection Network for Tightly Coupled Accelerators Architecture Toshihiro Hanawa, Yuetsu Kodama, Taisuke Boku, Mitsuhisa Sato Center for Computational Sciences University of Tsukuba, Japan 1 What
More informationThe Future of Supercomputer Software Libraries
The Future of Supercomputer Software Libraries Talk at HPC Advisory Council Israel Supercomputing Conference by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda
More informationOncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries
Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries Jeffrey Young, Alex Merritt, Se Hoon Shon Advisor: Sudhakar Yalamanchili 4/16/13 Sponsors: Intel, NVIDIA, NSF 2 The Problem Big
More information